An IoT management system with Xingshan chip isolation and end-to-end encryption

Abstract

The present invention discloses an Internet of Things management system with Starflash chip isolation and end-to-end encryption, which relates to the field of data security technology. The system establishes an isolated communication link based on a physical frequency hopping channel through the Starflash chip, and performs underlying identity confirmation based on the device hardware fingerprint; generates a dynamic identity tag for the session through the current timestamp, the device hardware fingerprint and the Starflash channel signal feature; then, based on the dynamic identity tag, performs multiple rounds of challenge and response recursive interaction to form an authentication closed loop; detects abnormal responses and calculates the risk intensity during the authentication process to feed back a threat level signal; dynamically adjusts the authentication and encryption algorithms based on the real-time status of smart home devices; predicts user behavior and generates a session cache in advance; and finally, allocates the resources of the channel switched to by the current channel establishment unit based on the task weight and bandwidth pressure.

Description

Technical Field

[0001] The present invention relates to the field of data security technology, and in particular to an Internet of Things management system with Star Flash chip isolation and end-to-end encryption. Background Art

[0002] Amid the booming Internet of Things (IoT) technology, the StarFlash chip, as the core carrier of a new generation of short-range communication technology, is driving the high-speed interconnection of IoT devices in areas such as industrial control, smart homes, and the Internet of Vehicles. StarFlash chip isolation technology utilizes physical frequency hopping channels and hardware fingerprint authentication to establish a secure communication link independent of traditional networks, providing underlying physical-level security isolation for IoT devices.

[0003] Current IoT management systems face multiple challenges in terms of security isolation and communication efficiency. Traditional wireless communication technologies use fixed frequency bands or simple frequency hopping, making them vulnerable to eavesdropping or impersonation by malicious devices, leading to data leaks or device hijacking. For example, smart home devices can be vulnerable to relay attacks due to non-isolated channels, allowing sensitive information such as door lock status and camera feeds to be stolen. In the authentication process, static identity tags and single-round authentication mechanisms are vulnerable to replay and forgery attacks. Attackers can intercept fixed identity information and impersonate legitimate devices to access the system.

[0004] These issues arise from the heterogeneous nature of IoT devices, the complexity of network environments, and the static nature of security mechanisms. For example, in traditional smart home systems, different devices, such as smart light bulbs, door locks, and cameras, share the same communication channel without physical isolation. Once a device is compromised, the attacker can infiltrate the channel and spread throughout the entire network. Under a single-round authentication mechanism, attackers can exploit captured one-time challenge-response data to repeatedly attempt to log in within the valid timeframe, paralyzing the authentication system. Summary of the Invention

[0005] In view of the above-mentioned problems existing in the prior art, the present application provides an Internet of Things management system with Star Flash chip isolation and end-to-end encryption.

[0006] The present disclosure provides an IoT management system with StarFlash chip isolation and end-to-end encryption, including:

[0007] The Starflash physical isolation access subsystem establishes an isolated communication link based on the physical frequency hopping channel through the Starflash chip, and performs underlying identity confirmation based on the device hardware fingerprint;

[0008] Dynamic identity feature generation subsystem, which generates a dynamic identity tag for a session based on the current timestamp, device hardware fingerprint, and star flash channel signal features;

[0009] The multi-round session recursive authentication subsystem performs multiple rounds of challenge and response recursive interactions based on dynamic identity tags to form a closed authentication loop.

[0010] The authentication risk feedback subsystem detects abnormal responses during the authentication process and calculates the risk intensity to provide feedback on the threat level signal;

[0011] The computing power and power consumption adaptive adjustment subsystem dynamically adjusts the authentication and encryption algorithms based on the real-time status of smart home devices;

[0012] Session cache management subsystem, predicting user behavior and generating session cache in advance;

[0013] The Star Flash isolation channel priority scheduling subsystem allocates the resources of the channel to which the current channel establishment unit switches based on the task weight and bandwidth pressure.

[0014] Optionally, the Star Flash physical isolation access subsystem includes a channel establishment unit, an identity confirmation unit and a channel switching control unit;

[0015] The channel establishment unit is used to pre-select the 6GHz frequency band as the communication frequency band and use the chaotic mapping algorithm to generate a frequency hopping sequence. It switches channels within the 6GHz frequency band according to the generated frequency hopping sequence with a period of 50ms to establish an isolated communication link. On each channel, the signal bandwidth is expanded to more than 100MHz using direct sequence spread spectrum technology.

[0016] The identity confirmation unit is used to send the device hardware fingerprint stored in the Star Flash chip to the cloud server using the channel switched to by the current channel establishment unit when the smart home device is connected to the smart home system network. The cloud server uses the Hamming distance to verify the degree of difference between the device hardware fingerprint and the fingerprint stored in the cloud database. If the Hamming distance does not exceed the preset difference threshold, the device hardware fingerprint is considered legal, otherwise it is considered illegal.

[0017] The channel switching control unit is used to receive the threat level signal of the authentication risk feedback subsystem. When the threat level signal is at a high threat level, it automatically switches to the advanced frequency hopping sequence. The advanced frequency hopping sequence adopts 7-layer pseudo-random code nesting technology and the hopping period is shortened to 20ms.

[0018] Optionally, the dynamic identity feature generation subsystem includes a feature collection unit, a label generation unit, and a label verification unit;

[0019] The feature acquisition unit is used to obtain the star flash channel signal characteristics according to the channel switched to by the current channel establishment unit, and generate a unique random number for each session according to the hardware random number generator;

[0020] The tag generation unit is used to generate a 64-byte dynamic identity tag Is based on the star flash channel signal characteristics obtained by the feature collection unit and the unique random number of each session using the SHA-384 hash algorithm;

[0021] The tag verification unit is used to compare the hash value of the newly generated dynamic identity tag with the previous tag to obtain the similarity. If the similarity exceeds the pre-set similarity threshold, the feature collection unit is triggered to re-collect features and generate a new tag.

[0022] Optionally, the multi-round session recursive authentication subsystem includes a challenge generation unit, a response processing unit, and an authentication closed-loop control unit;

[0023] The challenge generation unit is responsible for generating challenge information for verifying the identity of the smart home device during the multi-round session recursive authentication process. The challenge information includes generating a 128-bit random number as the initial challenge in the first round of authentication, and outputting a response value after the initial challenge is generated. A pseudo-random number generator is used, with the dynamic identity tag of the smart home device as the seed, to generate a random number sequence with the same length as the response value output by the initial challenge. The response value output by the initial challenge is XORed with the generated random number bit by bit to obtain a new binary sequence. The XOR result is encrypted using the master key in the block encryption mode. The encrypted result is the challenge of the current round.

[0024] The response processing unit is used to receive the challenge of the current round, perform a bit-by-bit XOR operation on the challenge of the current round and the dynamic identity tag of the smart home device itself to obtain a new binary sequence, use the session key as the key, and perform an HMAC calculation on the XOR result in the response processing unit, wherein the hash algorithm adopts the SHA-512 hash algorithm. The calculation result is the response value of the smart home device to the current challenge, and is sent to the cloud server for verification to obtain the response result;

[0025] The authentication closed-loop control unit is used to record the length of time from sending a challenge to the smart home device to receiving a response from the smart home device during each round of authentication, that is, the time spent on each round of authentication. If the time spent on authentication does not fall within the safe time range or the response result is a verification error, the authentication is terminated immediately and an abnormal signal is sent to the authentication risk feedback subsystem.

[0026] Optionally, the authentication risk feedback subsystem includes an anomaly detection unit, a risk assessment unit, and a signal feedback unit;

[0027] The anomaly detection unit is used to receive anomaly signals, calculate the response error rate and time offset based on the time and response results of each round of authentication collected in the historical time period and in real time, and determine the abnormal response type parameter according to the type of abnormal response, where the types of abnormal responses include response timeout, timing abnormality, frequency abnormality and pattern abnormality;

[0028] The risk assessment unit is used to perform weighted summation of the response error rate, time offset, and abnormal response type parameters to calculate the risk intensity;

[0029] The signal feedback unit is used to compare the calculated risk intensity with the preset risk threshold to divide the calculated risk intensity into low threat level signals, medium threat level signals and high threat level signals. If it is a high threat level signal, it will be sent to the Star Flash physical isolation access subsystem, computing power and power consumption adaptive adjustment subsystem and session cache management subsystem.

[0030] Optionally, the computing power and power consumption adaptive adjustment subsystem includes a status monitoring unit, a computing power allocation unit and an algorithm adjustment unit;

[0031] The status monitoring unit is used to obtain real-time data on the power consumption and computing load of smart home devices, as well as threat level signals from the authentication risk feedback subsystem;

[0032] The computing power allocation unit is used to dynamically allocate computing power resources when executing the authentication task when the threat level signal of the authentication risk feedback subsystem changes, so as to obtain the final computing power. The final computing power is obtained by collecting the real-time status of the smart home device in real time to obtain the remaining power, CPU temperature, available memory and risk intensity, and calculating the dynamic adjustment factor based on the real-time status of the smart home device. The final computing power is calculated based on the basic computing power requirements; and when the remaining power of the smart home device is less than 10%, the non-critical authentication rounds in the multi-round session recursive authentication subsystem are closed;

[0033] The algorithm adjustment unit is used to dynamically switch the encryption algorithm based on the threat level signal of the smart home device. If the remaining power is less than 10% and the threat level signal is a low threat level signal, the AES-256 encryption algorithm is switched to the AES-128 encryption algorithm.

[0034] Optionally, the session cache management subsystem includes a behavior prediction unit, a key pre-generation unit, and a cache optimization unit;

[0035] The prediction unit is used to predict user behavior based on historical data using a long short-term memory network, output the probability distribution of the user's next operation, and use a histogram to count the operation frequency in each time period. Then, the K-means algorithm is used to cluster high-density points into time intervals to obtain high-frequency operation periods.

[0036] The key pre-generation unit is used to set the preparation time. Before the predicted preparation time of the high-frequency period, the session key is pre-generated by using the channel switched to by the current channel establishment unit. The session key is encrypted using the Star Flash chip private key and the encryption algorithm is double-encrypted to cache all generated keys. When the algorithm adjustment unit determines that the remaining power is less than 10% and the threat level signal is a low threat level signal, the encryption algorithm is switched to the AES-128 encryption algorithm;

[0037] The cache optimization unit is used to analyze the effectiveness of the cache strategy to calculate the cache hit rate. If the cache hit rate is lower than the preset hit threshold, the reinforcement learning algorithm is started to retrain the long short-term memory network through historical authentication data, and the cache strategy is adjusted according to the threat level signal of the authentication risk feedback subsystem. When the threat level signal is a high threat level signal, only the core instruction key is cached.

[0038] Optionally, the star flash isolation channel priority scheduling subsystem includes a task classification unit, a bandwidth allocation unit and a scheduling strategy unit;

[0039] The task classification unit is used to receive task types and weight data from the business logic module, and classify tasks into emergency, real-time, and non-real-time levels based on the task types and weight data. When a task requests bandwidth, the bandwidth requirements are guaranteed in order of the task classification levels.

[0040] The bandwidth allocation unit is used to configure independent token buckets for tasks of different weights and determine the minimum bandwidth guarantee ratio for tasks of different weights. According to the token bucket algorithm, the current number of tokens in each token bucket is periodically calculated. If the token bucket corresponding to the emergency task is insufficient, tokens are temporarily preempted from the token buckets of tasks of other weights to meet the corresponding minimum bandwidth guarantee ratio for data transmission.

[0041] The scheduling strategy unit is used to monitor the channel bandwidth utilization in real time. When the channel bandwidth utilization exceeds the threshold bandwidth, a delay scheduling strategy is adopted for non-real-time tasks. The delay time is calculated by the formula Calculate, where is the basic delay time, is the used bandwidth, is the threshold bandwidth, is the total bandwidth, is the delay time.

[0042] Beneficial effects of the present invention:

[0043] (1) This system builds a three-dimensional security protection system through the Star Flash physical isolation access subsystem, dynamic identity feature generation subsystem and multi-round session recursive authentication subsystem. The Star Flash physical isolation access subsystem adopts 6GHz frequency hopping and direct sequence spread spectrum technology, combined with hardware fingerprint Hamming distance verification, to block illegal device access from the bottom layer. For example, when a smart home device encounters a counterfeit attack, the hardware fingerprint verification mechanism can quickly identify and intercept it. The dynamic identity feature generation subsystem combines timestamps and channel signal features to generate a 64-byte dynamic tag, and the tag verification unit compares the hash value in real time to prevent the tag from being predicted and forged. The multi-round session recursive authentication subsystem responds through a 128-bit random number challenge and HMAC calculation. If a smart door lock responds to a timeout or error during authentication, the authentication closed-loop control unit immediately terminates the authentication and issues an alarm to ensure zero loopholes in identity authentication.

[0044] (2) The authentication risk feedback subsystem works in conjunction with the computing power and power consumption regulation subsystem to achieve intelligent perception and dynamic response to security threats. The authentication risk feedback subsystem analyzes the response error rate, time offset, and anomaly type through the anomaly detection unit, and calculates the risk intensity through weighted calculation by the risk assessment unit. When it is determined to be a high threat, the signal feedback unit links the Star Flash physical isolation access subsystem to switch the 7-layer pseudo-random code hopping sequence. At the same time, the computing power and power consumption adaptive regulation subsystem adjusts the authentication computing power and maintains the AES-256 encryption strength to effectively resist attacks. In low-power scenarios, such as when the battery level of the smart door lock is less than 10%, the algorithm regulation unit automatically switches to AES-128 encryption to balance security and battery life.

[0045] (3) The session cache management subsystem is combined with the Star Flash isolation channel priority scheduling subsystem to achieve efficient resource utilization. The session cache management subsystem predicts user behavior through the LSTM network and pre-generates keys in advance, which further shortens the authentication response time; the cache optimization unit dynamically optimizes the strategy based on the hit rate, retaining only the core instruction keys when the threat is high, reducing the risk of leakage. The Star Flash isolation channel priority scheduling subsystem uses the token bucket algorithm and task classification to ensure the bandwidth of emergency tasks. For example, the door lock abnormality alarm task can prioritize the non-real-time task token to ensure timely transmission of the alarm; when the channel utilization exceeds the threshold, the non-real-time tasks such as firmware upgrades are delayed to improve the channel resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application.

[0047] Figure 1 This is a system module diagram of the present invention. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0049] With the popularity of smart homes, communication between devices mostly relies on WiFi links, but existing technologies have many security risks. On the one hand, fixed keys or long-term key mechanisms are easily obtained by hackers, resulting in data leakage; on the other hand, link identity authentication is fragile, and the device is only authenticated at the first access, and subsequent communications are vulnerable to man-in-the-middle attacks. For example, hackers can hijack WiFi links to steal home camera images or smart door lock passwords, seriously threatening user privacy and property safety. Existing solutions have deficiencies in security, dynamic adaptability and resource utilization efficiency, and are difficult to meet the complex security needs of smart homes. The present invention aims to provide a smart home communication protection system and method based on physical isolation of the Star Flash chip and multi-round session recursive authentication. By constructing a heterogeneous dual-link architecture and a dynamic authentication mechanism, the problem of WiFi link hijacking is solved, and safe, efficient and adaptive smart home communication protection is achieved.

[0050] like Figure 1 As shown, the implementation of the present invention proposes an Internet of Things management system with Star Flash chip isolation and end-to-end encryption, including:

[0051] The Starflash physical isolation access subsystem establishes an isolated communication link based on the physical frequency hopping channel through the Starflash chip, and performs underlying identity confirmation based on the device hardware fingerprint;

[0052] The Xingshan physical isolation access subsystem includes a channel establishment unit, an identity confirmation unit and a channel switching control unit;

[0053] The channel establishment unit is used to pre-select the 6 GHz (gigahertz) frequency band as the communication frequency band and use a chaotic mapping algorithm to generate a frequency hopping sequence. Based on the generated frequency hopping sequence, it switches channels within the 6 GHz frequency band with a period of 50 ms (milliseconds) to establish an isolated communication link. On each channel, the signal bandwidth is expanded to greater than 100 MHz (megahertz) using direct sequence spread spectrum technology.

[0054] The 6GHz band was selected as the communication frequency band because it has less interference than other commonly used frequency bands and has abundant bandwidth resources, making it suitable for building secure and high-speed communication links.

[0055] Using chaos mapping algorithm , generating a frequency hopping sequence. The algorithm generates a seemingly random but actually regular numerical sequence through continuous iterative calculations. These numerical values ​​are mapped to a specific frequency band to form a frequency hopping sequence. It is the result of the ath iteration, and the next value is obtained by continuously substituting it into the formula , thus generating a series of values ​​that can be mapped to different frequency bands after processing, thereby determining the channel to which the communication link should switch in each cycle. is a value between 0 and 1; is the result of the a+1th iteration;

[0056] The system switches channels within the 6GHz band based on a generated frequency hopping sequence every 50ms. Every 50ms, the communication link switches to the next channel specified by the frequency hopping sequence for data transmission. This rapid channel switching makes it difficult for unauthorized eavesdroppers to track and intercept communications, thus achieving physical isolation.

[0057] On each channel, direct sequence spread spectrum technology is used to expand the signal bandwidth to over 100 MHz. This technology involves performing a modulo-2 addition (or multiplication) operation on the original narrowband signal with a high-speed pseudo-random code, significantly increasing the signal bandwidth. The spread spectrum signal is highly resistant to interference, and even if it is subject to interference in certain frequency bands, the original signal can be restored through despreading.

[0058] The identity confirmation unit is used to send the device hardware fingerprint stored in the Star Flash chip to the cloud server using the channel switched to by the current channel establishment unit when the smart home device is connected to the smart home system network. The cloud server uses the Hamming distance to verify the degree of difference between the device hardware fingerprint and the fingerprint stored in the cloud database. If the Hamming distance does not exceed the preset difference threshold, the device hardware fingerprint is considered legal, otherwise it is considered illegal.

[0059] The Hamming distance is used to verify the legitimacy of a device's hardware fingerprint and determine whether the device is legally connected. It reflects the degree of difference between the fingerprint requested by the device and the fingerprint stored in the cloud database. A smaller Hamming distance indicates a greater similarity between the two fingerprints, and a higher likelihood of device legitimacy. Conversely, a larger Hamming distance indicates a greater difference, and the device may be at risk of unauthorized access.

[0060] The channel switching control unit receives threat level signals from the authentication risk feedback subsystem. When the threat level signal reaches a high level, it automatically switches to an advanced frequency hopping sequence. This advanced frequency hopping sequence utilizes seven layers of pseudo-random code nesting technology and a hopping period shortened to 20ms to enhance channel security. This advanced frequency hopping sequence utilizes seven layers of pseudo-random code nesting technology, nesting and combining multiple layers of different pseudo-random codes to generate the hopping sequence. With a hopping period shortened to 20ms, this switching speed is faster than conventional frequency hopping sequences, making it more difficult for unauthorized eavesdroppers to capture and decipher communications.

[0061] A chaotic mapping algorithm generates a frequency hopping sequence, rapidly switching channels within the 6GHz band with a 50ms period. Combined with direct sequence spread spectrum technology, this makes communication links difficult for unauthorized devices to track and monitor. For example, when smart home devices establish communication using this mechanism, attackers must complete channel scanning and signal analysis within 50ms. Spread spectrum technology further increases the difficulty of deciphering intercepted signals, effectively defending against threats such as relay attacks and spectrum sniffing.

[0062] When the authentication risk feedback subsystem detects a high threat level, the channel switching control unit automatically enables an advanced frequency hopping sequence nested with 7 layers of pseudo-random codes. The frequency hopping period is shortened to 20ms, the channel change speed is improved, and combined with a more complex code sequence, the interference cost for the attacker increases exponentially.

[0063] The identity confirmation unit compares the device hardware fingerprint through Hamming distance, such as the chip ID, sensor serial number and other unique identifiers. If the difference exceeds the threshold, it is judged to be illegal. For example, when an attacker tries to access the system using a forged device ID, the cloud server can quickly identify it through the fingerprint difference, preventing "fake devices" from mixing into the smart home network, and preventing security incidents such as illegal control of door locks and theft of camera images.

[0064] When the threat is low, a basic frequency hopping strategy (50ms cycle) is used to balance security and power consumption. When the threat is high, the advanced strategy (20ms cycle + 7 layers of pseudo-code nesting) is switched to. For example, when an abnormal login attempt or network attack is detected, the system automatically shortens the frequency hopping cycle and increases the complexity of the code sequence to ensure the secure transmission of critical data such as emergency alarm signals.

[0065] In this embodiment, the channel establishment unit serves as the core of physical isolation. The frequency hopping sequence and spread spectrum parameters it generates provide the communication medium for identity verification and channel switching. For example, the identity verification unit must transmit the hardware fingerprint via the channel it is currently switching to. If the channel is unstable or easily monitored, the fingerprint data may be tampered with or stolen during transmission. The identity verification unit relies on the channel resources of the channel establishment unit to complete fingerprint verification and feedback the verification results to the system core module. If the verification fails (the Hamming distance exceeds the threshold), subsequent authentication processes and service requests will be blocked, preventing illegal devices from entering the network. The channel switching control unit receives a threat level signal (e.g., high threat) from the authentication risk feedback subsystem, triggering the channel establishment unit to adjust the frequency hopping parameters. For example, if the identity verification unit detects multiple illegal fingerprint verifications, the authentication risk feedback subsystem determines it to be a high threat, and the channel switching control unit then instructs the channel establishment unit to enable the advanced frequency hopping sequence, forming a detection-response-enhancement security closed loop.

[0066] For example, in a smart home anti-intrusion scenario:

[0067] 1. The smart door lock establishes a frequency-hopping link in the 6GHz band through the channel establishment unit and sends the hardware fingerprint (such as the MCU unique ID) to the cloud;

[0068] 2. The identity verification unit verifies the legitimacy of the fingerprint through the Hamming distance. If it is legal, access is allowed; if it is illegal (for example, an attacker forges the fingerprint), the system denies access and triggers an alarm.

[0069] 3. If the attacker continues to attempt to invade, the authentication risk feedback subsystem determines that the threat is high, and the channel switching control unit instructs to switch to an advanced frequency hopping sequence with a period of 20ms. At the same time, the transmission interval of the door lock status data is shortened to ensure that the alarm signal is transmitted first.

[0070] In short, through the collaboration of the three units, the system achieves three-dimensional protection of "dynamic channel isolation + hardware identity anchoring + threat adaptive adjustment", while ensuring the secure access of IoT devices, it further enhances the communication anti-interference capability in complex attack environments.

[0071] Furthermore, the dynamic identity feature generation subsystem generates a session dynamic identity tag based on the current timestamp, device hardware fingerprint, and star flash channel signal features;

[0072] The dynamic identity feature generation subsystem includes a feature acquisition unit, a label generation unit, and a label verification unit;

[0073] The feature acquisition unit is used to obtain the star flash channel signal characteristics based on the channel switched to by the current channel establishment unit, and generate a unique random number for each session based on the hardware random number generator (HRNG); the unique random number for each session is a unique random value generated in each communication session, which is used to increase communication security and randomness, and prevent the session from being predicted and replayed.

[0074] Starflash channel signal characteristics refer to the various characteristic information possessed by signals transmitted on the Starflash channel, which are used to characterize the channel status and signal characteristics. These include the frequency hopping sequence index (indicating the currently used frequency hopping sequence number), the signal strength standard deviation (reflecting the degree of fluctuation in signal strength), the channel phase offset, and multipath fading parameters. These characteristics can reflect channel quality, interference conditions, etc. Starflash channel signal characteristics are generally measured and calculated by the signal processing unit inside the Starflash chip. After receiving the Starflash channel signal, the chip uses a built-in algorithm to analyze changes in parameters such as signal strength, frequency, and phase, and then calculates characteristic values ​​such as the signal strength standard deviation. The frequency hopping sequence index can be obtained from the chip's frequency hopping control module, which is responsible for switching the frequency hopping sequence according to a predetermined algorithm and recording the current index value.

[0075] The tag generation unit is used to generate a 64-byte dynamic identity tag Is based on the star flash channel signal characteristics obtained by the feature acquisition unit and the unique random number for each session, using the SHA-384 hash algorithm and the formula Is=SHA-384(T⊕(F⊕V)⊕L⊕Nonce), where ⊕ is an exclusive-OR operation, T is a nanosecond timestamp, F is the hardware fingerprint within the house, V is the firmware version number, L is the star flash channel signal characteristics, and Nonce is a unique random number for each session. The dynamic identity tag generated by this unit is unique and time-sensitive. Each session will be updated according to the real-time changing parameters, which greatly enhances the security of identity authentication and provides a reliable foundation for subsequent multi-round session recursive authentication.

[0076] The firmware version number identifies the device's firmware version and is typically stored in the device's non-volatile memory (such as flash memory). You can also view this version number directly during device startup or operation, for example, in the smart home device's app management interface. The firmware version number reflects the firmware's update status, and different versions may differ in functionality, stability, security, and other aspects.

[0077] The tag verification unit is used to compare the hash value of the newly generated dynamic identity tag with the previous tag to obtain similarity. If the similarity exceeds the pre-set similarity threshold, the feature collection unit is triggered to re-collect features and generate a new tag to prevent the tag from being predicted or forged. The similarity can be calculated using the Euclidean distance.

[0078] In this embodiment, a 64-byte dynamic identity tag, Is, is generated using the SHA-384 hash algorithm, combining the current timestamp, the device hardware fingerprint, and the characteristics of the starlight channel signal. This makes the identity tag unique and unpredictable for each session, making it more difficult for attackers to forge identities. For example, in a smart home system, the dynamic identity tag generated each time a device communicates is different. Even if an attacker intercepts the tag from one communication, they cannot use it to impersonate subsequent sessions.

[0079] The tag verification unit compares the hash value of the newly generated tag with the previous tag. If the similarity exceeds a threshold, it re-collects features to generate a new tag. This effectively prevents tags from being predicted or forged, further enhancing the security of identity authentication. For example, if an attacker attempts to generate a forged tag using a prediction algorithm, the tag verification unit can detect it in time and trigger the regeneration process, ensuring system security.

[0080] Feature acquisition unit: As a basis, it obtains the star flash channel signal characteristics from the current channel and generates a unique random number for each session to provide raw data for label generation. Label generation unit: Based on the data from the feature acquisition unit, it uses the SHA-384 hash algorithm to generate a dynamic identity label. It relies on the accuracy and integrity of the feature acquisition unit. If the input data is incorrect, the generated label will not be able to correctly reflect the device identity characteristics. Label verification unit: Compares the hash value of the newly generated label with the previous label, and decides whether to trigger the feature acquisition unit to re-collect based on the result. It forms a closed loop with the feature acquisition unit and the label generation unit to ensure the security and validity of the label. For example, if the label verification unit fails, it may allow counterfeit labels to pass verification, resulting in system security vulnerabilities.

[0081] Furthermore, the multi-round session recursive authentication subsystem performs multiple rounds of challenge and response recursive interactions based on dynamic identity tags, forming an authentication closed loop;

[0082] The multi-round session recursive authentication subsystem includes a challenge generation unit, a response processing unit, and an authentication closed-loop control unit;

[0083] The challenge generation unit is responsible for generating challenge information for verifying the identity of smart home devices during the multi-round session recursive authentication process. The challenge information includes a 128-bit random number generated as the initial challenge in the first round of authentication, and a response value is output after the initial challenge is generated. A pseudo-random number generator (PRNG) is used to generate a random number sequence with the same length as the response value output by the initial challenge with the dynamic identity tag of the smart home device as a seed. The response value output by the initial challenge is XORed with the generated random number bit by bit (the same is 0, and the different is 1) to obtain a new binary sequence. The XOR result is encrypted using the master key. The encryption mode adopts the block encryption mode. The encrypted result is the challenge of the current round. The master key is a long-term and highly confidential encryption key, which is usually pre-configured by the system administrator in the cloud server and Star Flash chip to encrypt the challenge and protect the server identity.

[0084] The block cipher mode divides the plaintext into blocks of fixed length and encrypts the current block using the initialization vector and the previous ciphertext block;

[0085] The legitimacy of the device's identity is further verified through the initial and subsequent challenges. Each round of challenges is linked to the previous one, forming a closed loop of recursive authentication. Even if an attacker intercepts the challenge and response information in one round, it is difficult to use it to crack subsequent rounds of authentication. This is because subsequent challenges are dynamic and dependent on previous authentication results. They are not used to obtain specific information, but rather serve as a key verification element in the authentication process, ensuring its security and reliability.

[0086] The response processing unit is used to receive the challenge of the current round, perform a bit-by-bit XOR operation on the challenge of the current round and the dynamic identity tag of the smart home device itself to obtain a new binary sequence. The session key is used as the key to perform an HMAC (keyed hash message authentication code) calculation on the XOR result in the response processing unit, where the hash algorithm adopts the SHA-512 hash algorithm. The calculation result is the response value of the smart home device to the current challenge.

[0087] HMAC (Hash-based Message Authentication Code) is a message authentication code based on a hash function. It is used to verify data integrity and sender identity. It combines a key and a hash function to generate a fixed-length hash value (i.e., the HMAC value), ensuring that any modification to the message will result in a change in the HMAC value.

[0088] Calculate the response value Rn using the formula Rn=HMAC-SHA-512(Cn⊕Is,Ksession), where Ksession is the session key and Cn is the challenge of the nth round;

[0089] During the multi-round recursive authentication process, each subsystem operates independently, but rather through real-time interaction and collaboration, forming a tight defense network. The authentication risk feedback subsystem, based on the results of these multi-round recursive authentications, captures abnormal responses in real time, providing a key basis for dynamic adjustments to the system's security policies. Information exchange between subsystems occurs via a secure data bus, with data transmission utilizing the AES-256 encryption algorithm. This ensures data security and integrity during module collaboration, further enhancing the system's overall defense capabilities. By monitoring response data in real time during these multi-round recursive authentications, the authentication risk feedback subsystem is able to detect any abnormal fluctuations. Upon detecting an abnormal response, it immediately activates a risk assessment mechanism, deriving a risk intensity value using a precise calculation formula. This critical information is then rapidly fed back to each subsystem, providing a scientific basis for subsequent dynamic adjustments to security policies.

[0090] And send it to the cloud server for verification to obtain the response result. The specific steps are as follows:

[0091] Cloud reception: The cloud server receives the response value sent by the device;

[0092] Local calculation: The cloud server calculates the challenge using the same formula Rn = HMAC-SHA-512(Cn⊕Is, Ksession) based on its own stored information (such as dynamic identity tags and session keys) and the received challenge, and obtains a locally calculated response value.

[0093] Comparison judgment: The response value sent by the device is compared with the locally calculated response value. If the two are consistent, the device is considered to have passed the authentication in this round, that is, the verification is successful; if they are inconsistent, the authentication fails, that is, the verification error.

[0094] The authentication closed-loop control unit is used to record the length of time from sending a challenge to the smart home device to receiving a response from the smart home device during each round of authentication, that is, the time spent in each round of authentication. This is to ensure that the authentication process is completed within a reasonable time frame and prevent excessive waiting or abnormal delays.

[0095] If the time taken for authentication does not fall within the safe time range or the response result is a verification error, the authentication is terminated immediately and an abnormal signal is sent to the authentication risk feedback subsystem.

[0096] In this embodiment, the reliability and security of authentication are increased through multiple rounds of recursive interaction of challenges and responses. Each round of challenges is generated based on a dynamic identity tag, and the content of the challenge is constantly changing, making it difficult for attackers to obtain legitimate identities through means such as replay attacks. For example, in the authentication process of smart home devices, the first round of challenges may be a random number, and the challenges of subsequent rounds are generated based on the responses and dynamic identity tags of the previous round. Even if the attacker intercepts the challenges and responses of the first round, he will not be able to cope with the authentication of subsequent rounds. The authentication closed-loop control unit records the time of each round of authentication. If it exceeds the safe time range or the response is wrong, the authentication is terminated immediately and an abnormal signal is sent. This effectively prevents timeout attacks and erroneous responses during the authentication process, and ensures the timeliness and accuracy of the authentication. For example, when an attacker attempts to interfere with the authentication by occupying the authentication channel for a long time, the authentication closed-loop control unit can detect and terminate the authentication in time to avoid wasting system resources.

[0097] The challenge generation unit is responsible for generating challenge information used to verify the identity of smart home devices. The generated challenge is based on the dynamic identity tag and a pseudo-random number generator. It provides the challenge content to the response processing unit and is the initiator of the authentication process. For example, if the challenge generated by the challenge generation unit is too simple or predictable, it will reduce the security of the authentication.

[0098] The response processing unit receives the challenge generated by the challenge generation unit, performs an XOR operation with the dynamic identity tag of the smart home device itself, and generates a response value through HMAC calculation and sends it to the cloud server for verification. It depends on the challenge content of the challenge generation unit and the dynamic identity tag of the device. If an error occurs in the calculation process or the tag is tampered with, the response result will be incorrect.

[0099] The authentication closed-loop control unit monitors the authentication time and response results of each round, and decides whether to terminate the authentication and send an abnormal signal based on the situation. It works closely with the challenge generation unit and the response processing unit to form an authentication closed loop. For example, if the authentication closed-loop control unit fails to detect an authentication timeout or an incorrect response in a timely manner, an illegal device may be allowed to pass the authentication. Traditional identity authentication mostly uses static identity tags, which are easy for attackers to obtain and forge. The dynamic identity tags of this system are generated by combining multiple real-time features and are unique for each session, further improving the security of identity authentication. For example, in traditional smart home systems, if the identity tags are obtained by attackers, it may lead to long-term illegal control of the device; however, the dynamic identity tags of this system require attackers to re-obtain and crack them each time, which is extremely difficult.

[0100] Compared to traditional single-round authentication, multi-round session recursive authentication increases authentication complexity and reliability through multiple rounds of challenge and response. If an attacker breaches traditional single-round authentication, the device is completely unprotected. However, even if a single round is breached, subsequent rounds can still detect and prevent unauthorized access. For example, in some traditional IoT device authentication, an attacker might be able to gain control of the device with a single attack. In this system, however, attackers must breach multiple rounds of authentication consecutively, further reducing the probability of a successful attack.

[0101] For example, in a remote control application scenario for smart home devices, when a user remotely controls a smart home device, such as a smart camera, via their mobile phone, the dynamic identity feature generation subsystem first generates a dynamic identity tag. The multi-round session recursive authentication subsystem then initiates a multi-round authentication process. The challenge generation unit generates challenge information, the response processing unit calculates the response value and sends it to the cloud server for verification, and the authentication closed-loop control unit monitors the duration and response of each authentication round. If authentication succeeds, the user can remotely control the camera. If authentication fails or times out, the system terminates authentication and prompts the user. For example, if an attacker attempts to interfere with a user's remote control of the camera, the authentication closed-loop control unit can detect and terminate authentication in a timely manner, protecting user privacy and device security.

[0102] Furthermore, the authentication risk feedback subsystem detects abnormal responses during the authentication process and calculates the risk intensity to feedback a threat level signal;

[0103] The authentication risk feedback subsystem includes an anomaly detection unit, a risk assessment unit, and a signal feedback unit;

[0104] The anomaly detection unit is used to receive anomaly signals, calculate the response error rate and time offset based on the time and response results of each round of authentication collected in the historical time period and in real time, and determine the abnormal response type parameter based on the type of abnormal response;

[0105] Abnormal responses include response timeouts, timing anomalies, frequency anomalies, and pattern anomalies. Response timeouts occur when the time spent on each authentication round exceeds a safe range and may be caused by network latency or computing resource exhaustion. Timing anomalies occur when the response time between successive rounds fluctuates significantly, potentially indicating a man-in-the-middle attack or device performance anomalies. Frequency anomalies occur when authentication requests are frequently initiated within a short period of time (e.g., more than 10 times within a minute), potentially indicating a brute force attack or DoS attack. Pattern anomalies occur when the hash value distribution of the response value does not conform to expectations and may indicate tampering with the device's computing logic.

[0106] If the exception response type is a timing exception, the exception response type parameter is the number of timeouts. Total number of authentications, i.e., timeout percentage; if the abnormal response type is response timeout, the abnormal response type parameter is the response time standard deviation; if the abnormal response type is frequency anomaly, the abnormal response type parameter is pattern anomaly; if the abnormal response type is timing anomaly, the abnormal response type parameter is the response value entropy;

[0107] The response error rate is calculated as: failed rounds Total rounds;

[0108] The time offset is calculated as: ,in, is the time offset, The time spent on the nth round of authentication, Average time spent for certification;

[0109] The risk assessment unit is used to perform weighted summation of the response error rate, time offset, and abnormal response type parameters to calculate the risk intensity;

[0110] The signal feedback unit is used to compare the calculated risk intensity with the preset risk threshold to divide the calculated risk intensity into low threat level signals, medium threat level signals and high threat level signals. If it is a high threat level signal, it will be sent to the Star Flash physical isolation access subsystem, computing power and power consumption adaptive adjustment subsystem and session cache management subsystem.

[0111] Specifically, if the risk intensity is greater than the risk threshold, it is a high threat level signal; when the risk intensity is equal to the risk threshold, it is a medium threat level signal; and if the risk intensity is less than the risk threshold, it is a low threat level signal.

[0112] In this embodiment, by real-time monitoring of authentication time, such as the time taken for each round of authentication and the response result being a verification error, the response error rate and time offset are calculated, and types such as response timeouts, timing anomalies, frequency anomalies, and pattern anomalies are identified. For example, when a smart home device frequently experiences response timeouts during authentication (such as exceeding the safe time range of 200ms), or the response error rate suddenly rises to more than 30%, the system can quickly locate the anomaly.

[0113] A weighted formula is used to comprehensively assess risk, assigning different weights to different anomaly types to ensure that high-risk behaviors trigger responses first. Risk intensity is categorized as low, medium, and high threat levels. In the case of high threat, signals are sent to the physical isolation subsystem, computing power regulation subsystem, and other subsystems to jointly strengthen protection. For example, if a device is detected to initiate 10 abnormal authentication requests in a short period of time, the risk intensity is determined to be high, triggering the Star Flash channel to switch to a 20ms advanced frequency hopping sequence, and shutting down non-critical authentication rounds to concentrate computing power.

[0114] A complete chain of events, from anomaly detection and risk calculation to graded response, is formed to avoid misjudgments along a single dimension. For example, an increase in time offset alone may be caused by network fluctuations, but if accompanied by an increased response error rate and an abnormal pattern, the overall risk is determined to be medium-to-high, further improving detection accuracy.

[0115] Receive abnormal signals sent by the authentication closed-loop control unit, such as authentication timeout and response error, collect historical and real-time authentication data, calculate abnormal response type parameters and identify the abnormal type, such as timing abnormality corresponding to the disorder of the authentication round order.

[0116] The response error rate, time offset, and anomaly response type parameters are obtained from the anomaly detection unit and synthesized into a risk intensity E according to preset weights. The risk intensity is compared with the threshold and a classified signal is output. In case of high threat, the Star Flash physical isolation access subsystem (switching to advanced frequency hopping), the computing power and power consumption adaptive adjustment subsystem (adjusting authentication computing power and switching encryption algorithms), and the session cache management subsystem (caching only core keys) are simultaneously notified. This ensures that each subsystem adjusts its strategy synchronously, forming a three-dimensional defense. For example, in case of high threat, Star Flash channel switching and computing power enhancement are carried out simultaneously to avoid lags in any single measure.

[0117] In a traditional system, a single authentication timeout due to network fluctuations on a certain smart door lock caused the system to misinterpret it as an attack and trigger an alarm, requiring manual investigation. However, the anomaly detection unit of this system detected a large single authentication time offset, but the response error rate remained below 5%, with no other anomalies. The calculated risk intensity was low, indicating a low risk. Only logging was performed, without triggering defensive measures. However, if multiple timeouts and a continued increase in the response error rate occurred within the next five minutes, the overall threat was determined to be medium, triggering a reduction in the channel hopping period to 30ms.

[0118] Furthermore, the computing power and power consumption adaptive adjustment subsystem dynamically adjusts the authentication and encryption algorithms based on the real-time status of smart home devices;

[0119] The computing power and power consumption adaptive adjustment subsystem includes a status monitoring unit, a computing power allocation unit, and an algorithm adjustment unit;

[0120] The status monitoring unit is used to obtain real-time data on the power consumption and computing load of smart home devices, as well as threat level signals from the authentication risk feedback subsystem;

[0121] The computing power allocation unit is used to dynamically allocate computing power resources when executing authentication tasks when the threat level signal of the authentication risk feedback subsystem changes, so as to obtain the final computing power. The final computing power is obtained by collecting the real-time status of smart home devices in real time to obtain the remaining power, CPU temperature, available memory and risk intensity, and calculating the dynamic adjustment factor based on the real-time status of the smart home devices. The final computing power is calculated based on the basic computing power requirements.

[0122] The dynamic adjustment factor calculation formula is: ,in, is the dynamic adjustment factor, is the remaining power, is the maximum value of power, is the available memory, is the maximum memory, is the risk intensity, 、 and is the weight value, + + =1, the specific value is set by the user according to the situation;

[0123] The final calculation formula for computing power is: ,in, For the final computing power, As the basic computing power demand;

[0124] The core goal of computing power allocation is to achieve a dynamic balance between security requirements, system performance, and resource consumption, ensuring the authentication system operates efficiently and reliably in different scenarios. Specifically, this includes responding to different threat levels. When high risks are detected (e.g., frequent authentication failures, abnormal traffic), more computing power is allocated to high-strength encryption algorithms (e.g., AES-256, ECDSA-P384) to enhance anti-attack capabilities.

[0125] When the remaining battery level of the smart home device is less than 10%, non-critical authentication rounds in the multi-round session recursive authentication subsystem are closed;

[0126] Non-critical authentication rounds refer to those rounds in a multi-round session recursive authentication process that have a smaller impact on overall authentication security than core authentication rounds. These rounds primarily serve to further verify device identity, but in certain circumstances (such as when the device battery is extremely low) they can be temporarily disabled to conserve computing power and energy consumption. These rounds are typically predetermined by the system based on the device type, security requirements, and the design of the authentication process. For example, smart home devices with relatively low security requirements (such as smart lightbulbs) may have relatively more non-critical authentication rounds, while devices with higher security requirements (such as smart door locks) may have fewer non-critical authentication rounds. The system also considers the device's computing power and power consumption, as well as each round's contribution to overall security, to determine the number of non-critical authentication rounds.

[0127] The algorithm adjustment unit is used to dynamically switch the encryption algorithm based on the threat level signal of the smart home device. If the remaining power is less than 10% and the threat level signal is a low threat level signal, the AES-256 encryption algorithm is switched to the AES-128 encryption algorithm.

[0128] In this embodiment, computing power resources are dynamically adjusted based on the threat level of the authentication risk feedback subsystem. For example, when the threat becomes high (such as when an abnormal authentication attack is detected), the computing power allocation unit increases the computing power share of the authentication task from the default 40% to 70%, ensuring the real-time performance of high-strength encryption algorithms (such as AES-256) and multi-round authentication. When the threat becomes low (such as daily device status inquiries), the computing power share is reduced to 20% to reduce power consumption. When the remaining battery power of the device is less than 10% and the threat level is low, the algorithm adjustment unit automatically switches from AES-256 to AES-128, reducing encryption computing power consumption by approximately 30% while maintaining the basic security level.

[0129] For example, when a smart door lock is in low-power mode, it reduces encryption strength to extend battery life and prevent lock loss due to power depletion. The status monitoring unit collects real-time data such as power consumption, CPU temperature, and memory, and combines this with risk intensity to calculate a dynamic adjustment factor. In low-power or high-load scenarios, non-critical authentication steps are identified based on round weights and disabling them can reduce computing power consumption by 40%. For example, when a smart camera is streaming video, it disables non-critical fingerprint verification rounds to prioritize the computing power required for video encryption. Dynamically adjusting computing power through temperature sensing prevents accelerated chip aging caused by long-term full-load operation.

[0130] The Condition Monitoring Unit collects real-time device status data (power, temperature, memory) and threat level signals, providing a basis for computing power allocation and algorithm adjustment. For example, if the Condition Monitoring Unit fails to detect that the power level is below 10% in a timely manner, the Algorithm Adjustment Unit will not be able to trigger the AES-128 switch, potentially causing the device to power down prematurely.

[0131] The computing power allocation unit calculates a dynamic adjustment factor based on status monitoring data and calculates the final computing power based on the basic computing power requirements. Low battery triggers the non-critical round shutdown logic, identifying the steps that can be shut down using the round weight table (e.g., fingerprint verification round weight 0.6, timestamp verification round weight 0.3).

[0132] The algorithm adjustment unit switches encryption algorithms based on the threat level and battery status, with priority being: high threat → forced use of AES-256; low battery + low threat → switch to AES-128; all other situations default to AES-256. For example, when the threat level rises from medium to high, the algorithm adjustment unit immediately reverts to AES-256, regardless of battery level, to ensure encryption strength.

[0133] In traditional systems, smart speakers may continue to run AES-256 encryption when the battery is low, causing battery life to drop sharply from 5 hours to 3 hours. Without a mechanism to disable rounds, authentication latency increases. In this system, when the smart speaker's battery level drops below 10% and the threat level is low, it automatically switches to AES-128 and disables non-critical authentication rounds, extending battery life.

[0134] Furthermore, the session cache management subsystem predicts user behavior and generates session cache in advance;

[0135] The session cache management subsystem includes a behavior prediction unit, a key pre-generation unit, and a cache optimization unit;

[0136] The prediction unit is used to predict user behavior based on historical data using a long short-term memory network (LSTM), output the probability distribution of the user's next operation, and use a histogram to count the operation frequency in each time period. Then, the K-means algorithm is used to cluster high-density points into time intervals to obtain high-frequency operation periods.

[0137] Long Short-Term Memory (LSTM) is a special type of recurrent neural network (RNN) that can learn long-term dependencies in sequential data. Compared to traditional RNNs, LSTM solves the vanishing gradient problem through a gating mechanism (input gate, forget gate, output gate), making it particularly suitable for processing time series data (such as user behavior sequences). Steps for using LSTM to predict user behavior:

[0138] Data collection: records user historical authentication time, operation type (such as login, data transfer, file access), operation duration, etc.

[0139] Feature engineering: extract time features (such as hours, days of the week), operation frequency, intervals between operations, etc. Perform one-hot encoding on the operation type;

[0140] Sequence construction: convert historical data into a fixed-length sequence (e.g., predict the next operation based on the previous five operations);

[0141] Model training: LSTM network structure: input layer → LSTM layer (including forget gate, input gate, output gate) → fully connected layer → output layer (softmax classification); loss function: cross entropy loss (classification problem);

[0142] Prediction and application: Input the current operation sequence, and the model outputs the probability distribution of the next operation.

[0143] The key pre-generation unit is used to set the preparation time. Before the predicted preparation time of the high-frequency period, the session key is pre-generated by using the channel switched to by the current channel establishment unit. The session key is encrypted using the Star Flash chip private key and the encryption algorithm is double-encrypted to cache all generated keys. The encryption algorithms here include AES-256 and AES-128. When the algorithm adjustment unit determines that the remaining power is less than 10% and the threat level signal is a low threat level signal, the encryption algorithm is switched to the AES-128 encryption algorithm, indicating that the encryption algorithm here is AES-128, otherwise it is AES-256;

[0144] Among them, caching all generated keys includes pre-generated session keys, session keys, and instruction keys, etc.;

[0145] The cache optimization unit is used to analyze the effectiveness of the cache strategy to calculate the cache hit rate. If the cache hit rate is lower than the preset hit threshold, the reinforcement learning algorithm is started to retrain the long short-term memory network through historical authentication data, and the cache strategy is adjusted according to the threat level signal of the authentication risk feedback subsystem. When the threat level signal is a high threat level signal, only the core instruction key is cached.

[0146] The cache hit rate is calculated as follows: number of hits The total number of requests. The cache hit rate reflects the effectiveness of the cache strategy. A high hit rate indicates a reasonable cache strategy that reduces repeated calculations or key negotiation overhead.

[0147] Historical authentication data includes authentication time, authentication result, key used, operation type, device status (battery level, temperature), and threat level;

[0148] Core command keys refer to the keys used to ensure the secure transmission and execution of key control commands in smart home systems. These keys are crucial to the core functions of the system (such as door lock control, real-time camera monitoring, etc.). Once leaked, they may lead to serious security issues, such as illegal door opening and privacy leakage. They can be obtained by reading a preset core command list from the system configuration file or filtering out the commands with the highest execution frequency and the highest permission level based on historical operation frequency statistics.

[0149] In this example, historical operation data is analyzed through an LSTM network, combined with K-means time period clustering, to accurately identify users' most frequent operation periods, such as activating smart door locks between 7:00 AM and 8:00 AM and controlling lights between 7:00 PM and 9:00 PM. For example, if it predicts that a user will use their phone to control the living room air conditioner at 7:30 PM each night, the system will pre-generate a session key at 7:00 PM, reducing the authentication response time from 200ms to 50ms. Pre-generated session keys, command keys, and other keys are double-encrypted and stored using the StarFlash chip private key and the AES algorithm to ensure cached data security.

[0150] When a device requests access, it retrieves the key directly from the cache, avoiding repeated calculation overhead. The cache optimization unit calculates the cache hit rate in real time. When it falls below a threshold, it initiates a reinforcement learning algorithm to retrain the LSTM model using historical authentication data to optimize prediction accuracy. In high-threat situations, only core command keys (such as door lock control commands) are cached to reduce the risk of sensitive data exposure. For example, if the authentication risk feedback subsystem issues a high-threat signal, the system immediately clears non-critical caches to reduce the risk of key leakage.

[0151] In conjunction with the adaptive computing power and power consumption adjustment subsystem, the system automatically downgrades AES-256 to AES-128 encrypted cache when power is low and the threat is low, further reducing energy consumption. The behavior prediction unit uses the LSTM output of the user action probability distribution (e.g., "turn on the living room lights" has a probability of 0.75) and combines it with the high-frequency time periods (e.g., 6:00 PM to 7:00 PM daily) from K-means clustering to generate a pre-caching time window. For example, for a predicted kitchen appliance operation at 7:30 AM, the system generates a session key at 7:00 AM and stores it encrypted. It also monitors channel switching events (e.g., an increased threat level triggering a channel upgrade) to ensure the key matches the current channel. The cache optimization unit calculates the cache hit rate and, if it falls below a threshold, adjusts the LSTM model parameters (e.g., learning rate, number of hidden layer nodes) using a reinforcement learning algorithm.

[0152] Furthermore, the Star Flash isolation channel priority scheduling subsystem allocates the resources of the channel to which the current channel establishment unit switches based on the task weight and bandwidth pressure.

[0153] The star flash isolation channel priority scheduling subsystem includes a task classification unit, a bandwidth allocation unit, and a scheduling strategy unit;

[0154] The task classification unit receives task types and weights from the business logic module. Based on these data, it categorizes tasks into emergency (e.g., door lock anomaly alarm, weight = 100), real-time (e.g., camera control, weight = 50), and non-real-time (e.g., firmware upgrade, weight = 10). When a task requests bandwidth, bandwidth requirements are guaranteed based on the task classification level.

[0155] The task classification unit receives task type and weight data from the business logic module through the system bus interface. Task types include voice calls, file transfers, device management, device configuration, status queries, log uploads, and data synchronization.

[0156] The weight data reflects the relative importance of the task and is usually set by the system administrator or application developer. For example, the weight of voice calls is 8, the weight of file transfers is 5, and the weight of log uploads is 2.

[0157] The bandwidth allocation unit is used to configure independent token buckets for tasks of different weights, determine the minimum bandwidth guarantee ratio for tasks of different weights, and periodically calculate the current number of tokens in each token bucket according to the token bucket algorithm;

[0158] When a task requests data transmission, it obtains a number of tokens equal to the amount of data from the corresponding token bucket. Only when there are enough tokens can the task proceed with data transmission. If there are insufficient tokens, the task must wait for the next round of token generation until it has enough tokens. This approach limits the task's instantaneous traffic flow, stabilizes the data transmission rate, and avoids channel congestion caused by sudden large traffic flows.

[0159] If there are insufficient tokens in the token bucket corresponding to the emergency task, tokens are temporarily seized from the token buckets of tasks with other weights (provided that the basic transmission of other tasks is not affected) to meet the corresponding minimum bandwidth guarantee ratio for data transmission;

[0160] The token bucket algorithm is used to smooth bandwidth allocation, ensuring that tasks obtain a stable transmission rate while allowing bursty traffic.

[0161] The scheduling strategy unit is used to monitor the channel bandwidth utilization in real time. When the channel bandwidth utilization exceeds the threshold bandwidth, a delay scheduling strategy is adopted for non-real-time tasks. The delay time is calculated by the formula Calculate, where is the basic delay time, is the used bandwidth, is the threshold bandwidth, is the total bandwidth, is the delay time.

[0162] Some of the data in the above formulas are numerically calculated by taking their dimensions, such as the weighted summation algorithm. At the same time, the contents not described in detail in this specification belong to the existing technology known to those skilled in the art.

[0163] In this embodiment, tasks are clearly categorized into emergency, real-time, and non-real-time levels based on their type and weight. This allows the system to rationally allocate resources based on the importance and urgency of each task, ensuring that critical tasks are prioritized. For example, when a door lock anomaly alarm task occurs, due to its high weight, the system immediately prioritizes its bandwidth requirements to ensure timely transmission of the alarm information and safeguard safety.

[0164] When tasks request bandwidth, they are guaranteed in order of their level. This approach ensures that urgent tasks always receive sufficient bandwidth resources and are not delayed or failed due to interference from other tasks. For example, in a smart home system, emergency alarm tasks receive bandwidth priority over non-urgent tasks such as device status queries, thereby promptly notifying users of potential security threats. Separate token buckets are configured for tasks of different weights, and minimum bandwidth guarantee ratios are determined for tasks of different weights. This allows for flexible allocation of bandwidth resources based on the characteristics and needs of the tasks. For example, for real-time tasks, a higher minimum bandwidth guarantee ratio may be allocated to ensure continuous and stable data transmission. For example, for camera control tasks that require real-time video streaming, a higher bandwidth guarantee can ensure smooth video flow.

[0165] The token bucket algorithm periodically calculates the current number of tokens in each token bucket. This algorithm effectively controls task traffic flow and prevents sudden traffic from congesting the channel. For example, when a task needs to transmit a large amount of data, the token bucket algorithm limits its transmission speed based on the token generation rate and bucket capacity, preventing it from instantaneously occupying excessive bandwidth resources and impacting the normal progress of other tasks.

[0166] The scheduling strategy unit monitors channel bandwidth utilization in real time to stay informed of channel usage. When channel bandwidth utilization exceeds the threshold, the system reacts quickly and takes appropriate measures to adjust resource allocation to avoid channel congestion. For example, if channel bandwidth utilization reaches 80% (the threshold), the system recognizes potential bandwidth pressure and requires adjustments. A delayed scheduling strategy is used for non-real-time tasks, with the delay time calculated using a formula. This strategy can appropriately delay the execution of non-critical tasks when channel bandwidth is limited, freeing up bandwidth resources for urgent and real-time tasks.

[0167] Traditional systems may simply categorize tasks as real-time or non-real-time, or their prioritization may be unclear and inadequate. For example, some traditional IoT systems may simply classify tasks based on their real-time requirements, without further distinguishing between the priorities of different real-time tasks. This can lead to resource allocation issues that may not accurately meet the needs of each task. This system, through clear task classification and weighting, manages task priorities more precisely. It not only distinguishes between real-time and non-real-time tasks, but also further divides real-time tasks into emergency and standard real-time levels. Each level of task is assigned a different weight, enabling more appropriate resource allocation based on the actual importance and urgency of the task. For example, in a smart home system, the door lock alarm task, as an emergency task, has a significantly higher weight than the standard camera control task (real-time). Therefore, the system prioritizes the bandwidth requirements of the door lock alarm task to ensure timely processing of security incidents. Furthermore, bandwidth allocation in traditional systems can be rigid and lacks the ability to dynamically adjust. For example, some traditional systems may use a fixed bandwidth allocation ratio, allocating a fixed bandwidth share to each task regardless of the task's actual bandwidth needs. This fixed ratio applies to bandwidth allocation, which can lead to inefficient utilization of channel resources when faced with traffic bursts or fluctuating task demands, leading to performance degradation for some tasks. This system uses a token bucket algorithm to configure independent token buckets for tasks of different weights and dynamically adjust token allocation based on task requirements. This approach allows for flexible bandwidth allocation based on the task's actual traffic needs and channel usage. For example, when an urgent task suddenly encounters a large amount of data transmission demand, the system can use the token bucket algorithm to temporarily seize tokens from other tasks' token buckets to meet the urgent task's bandwidth needs. Once the urgent task is completed, the tokens are returned to ensure that other tasks can continue to operate normally. This dynamic adjustment mechanism can better adapt to the uncertainty of task traffic in the IoT environment and improve channel resource utilization.

[0168] When faced with excessive channel bandwidth utilization, traditional systems may lack effective countermeasures or adopt inefficient, unintelligent measures. For example, some traditional systems may simply limit the bandwidth of all tasks or randomly select tasks for delay or discarding. This approach may affect the normal execution of some important tasks and fail to fundamentally resolve the problem of channel congestion. This system can more effectively address channel congestion by monitoring channel bandwidth utilization in real time and implementing a formula-based delay scheduling strategy for non-real-time tasks. When channel bandwidth utilization exceeds the threshold bandwidth, the system will reasonably delay the execution of non-real-time tasks based on the task priority and channel usage, freeing up more bandwidth resources for urgent and real-time tasks. This strategy can effectively alleviate channel congestion while ensuring the normal execution of important tasks, thereby improving overall system performance.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An IoT management system with StarFlash chip isolation and end-to-end encryption, characterized by: include, The Starflash physical isolation access subsystem establishes an isolated communication link based on the physical frequency hopping channel through the Starflash chip, and performs underlying identity confirmation based on the device hardware fingerprint; The Xingshan physical isolation access subsystem includes a channel establishment unit, an identity confirmation unit and a channel switching control unit; The channel establishment unit is used to pre-select the 6GHz frequency band as the communication frequency band and use the chaotic mapping algorithm to generate a frequency hopping sequence. It switches channels within the 6GHz frequency band according to the generated frequency hopping sequence with a period of 50ms to establish an isolated communication link. On each channel, the signal bandwidth is expanded to more than 100MHz using direct sequence spread spectrum technology. The identity confirmation unit is used to send the device hardware fingerprint stored in the Star Flash chip to the cloud server using the channel switched to by the current channel establishment unit when the smart home device is connected to the smart home system network. The cloud server uses the Hamming distance to verify the degree of difference between the device hardware fingerprint and the fingerprint stored in the cloud database. If the Hamming distance does not exceed the preset difference threshold, the device hardware fingerprint is considered legal, otherwise it is considered illegal. The channel switching control unit is used to receive the threat level signal from the authentication risk feedback subsystem. When the threat level signal is at a high threat level, it automatically switches to an advanced frequency hopping sequence. The advanced frequency hopping sequence uses a 7-layer pseudo-random code nesting technology and the frequency hopping period is shortened to 20ms. Dynamic identity feature generation subsystem, which generates a dynamic identity tag for a session based on the current timestamp, device hardware fingerprint, and star flash channel signal features; The multi-round session recursive authentication subsystem performs multiple rounds of challenge and response recursive interactions based on dynamic identity tags to form a closed authentication loop. The authentication risk feedback subsystem detects abnormal responses during the authentication process and calculates the risk intensity to provide feedback on the threat level signal; The computing power and power consumption adaptive adjustment subsystem dynamically adjusts the authentication and encryption algorithms based on the real-time status and threat level signals of smart home devices; The session cache management subsystem predicts user behavior and pregenerates session keys, adjusting cache strategies based on threat level signals; The Star Flash isolation channel priority scheduling subsystem allocates the resources of the channel to which the current channel establishment unit switches based on the task weight and bandwidth pressure.

2. The IoT management system with StarFlash chip isolation and end-to-end encryption according to claim 1 is characterized by: The dynamic identity feature generation subsystem includes a feature acquisition unit, a label generation unit, and a label verification unit; The feature acquisition unit is used to obtain the star flash channel signal characteristics according to the channel switched to by the current channel establishment unit, and generate a unique random number for each session according to the hardware random number generator; The tag generation unit is used to generate a 64-byte dynamic identity tag Is based on the star flash channel signal characteristics obtained by the feature collection unit and the unique random number of each session using the SHA-384 hash algorithm; The tag verification unit is used to compare the hash value of the newly generated dynamic identity tag with the previous tag to obtain the similarity. If the similarity exceeds the pre-set similarity threshold, the feature collection unit is triggered to re-collect features and generate a new tag.

3. The IoT management system with StarFlash chip isolation and end-to-end encryption according to claim 2 is characterized by: The multi-round session recursive authentication subsystem includes a challenge generation unit, a response processing unit, and an authentication closed-loop control unit; The challenge generation unit is responsible for generating challenge information for verifying the identity of the smart home device during the multi-round session recursive authentication process. The challenge information includes generating a 128-bit random number as the initial challenge in the first round of authentication, and outputting a response value after the initial challenge is generated. A pseudo-random number generator is used, with the dynamic identity tag of the smart home device as the seed, to generate a random number sequence with the same length as the response value output by the initial challenge. The response value output by the initial challenge is XORed with the generated random number bit by bit to obtain a new binary sequence. The XOR result is encrypted using the master key in the block encryption mode. The encrypted result is the challenge of the current round. The response processing unit is used to receive the challenge of the current round, perform a bit-by-bit XOR operation on the challenge of the current round and the dynamic identity tag of the smart home device itself to obtain a new binary sequence, use the session key as the key, and perform an HMAC calculation on the XOR result in the response processing unit, wherein the hash algorithm adopts the SHA-512 hash algorithm. The calculation result is the response value of the smart home device to the current challenge, and is sent to the cloud server for verification to obtain the response result; The authentication closed-loop control unit is used to record the length of time from sending a challenge to the smart home device to receiving a response from the smart home device during each round of authentication, that is, the time spent on each round of authentication. If the time spent on authentication does not fall within the safe time range or the response result is a verification error, the authentication is terminated immediately and an abnormal signal is sent to the authentication risk feedback subsystem.

4. The IoT management system with StarFlash chip isolation and end-to-end encryption according to claim 3 is characterized by: The authentication risk feedback subsystem includes an anomaly detection unit, a risk assessment unit, and a signal feedback unit; The anomaly detection unit is used to receive anomaly signals, calculate the response error rate and time offset based on the time and response results of each round of authentication collected in the historical time period and in real time, and determine the abnormal response type parameter according to the type of abnormal response, where the types of abnormal responses include response timeout, timing abnormality, frequency abnormality and pattern abnormality; The risk assessment unit is used to perform weighted summation of the response error rate, time offset, and abnormal response type parameters to calculate the risk intensity; The signal feedback unit is used to compare the calculated risk intensity with the preset risk threshold to divide the calculated risk intensity into low threat level signals, medium threat level signals and high threat level signals. If it is a high threat level signal, it will be sent to the Star Flash physical isolation access subsystem, computing power and power consumption adaptive adjustment subsystem and session cache management subsystem.

5. The IoT management system with StarFlash chip isolation and end-to-end encryption according to claim 4 is characterized by: The computing power and power consumption adaptive adjustment subsystem includes a status monitoring unit, a computing power allocation unit, and an algorithm adjustment unit; The status monitoring unit is used to obtain real-time data on the power consumption and computing load of smart home devices, as well as threat level signals from the authentication risk feedback subsystem; The computing power allocation unit is used to dynamically allocate computing power resources when executing the authentication task when the threat level signal of the authentication risk feedback subsystem changes, so as to obtain the final computing power. The final computing power is obtained by collecting the real-time status of the smart home device in real time to obtain the remaining power, CPU temperature, available memory and risk intensity, and calculating the dynamic adjustment factor based on the real-time status of the smart home device. The final computing power is calculated based on the basic computing power requirements; and when the remaining power of the smart home device is less than 10%, the non-critical authentication rounds in the multi-round session recursive authentication subsystem are closed; The algorithm adjustment unit is used to dynamically switch the encryption algorithm based on the threat level signal of the smart home device. If the remaining power is less than 10% and the threat level signal is a low threat level signal, the AES-256 encryption algorithm is switched to the AES-128 encryption algorithm.

6. The IoT management system with StarFlash chip isolation and end-to-end encryption according to claim 5, characterized in that: The session cache management subsystem includes a behavior prediction unit, a key pre-generation unit, and a cache optimization unit; The prediction unit is used to predict user behavior based on historical data using a long short-term memory network, output the probability distribution of the user's next operation, and use a histogram to count the operation frequency in each time period. Then, the K-means algorithm is used to cluster high-density points into time intervals to obtain high-frequency operation periods. The key pre-generation unit is used to set the preparation time. Before the predicted preparation time of the high-frequency period, the session key is pre-generated by using the channel switched to by the current channel establishment unit. The session key is encrypted using the Star Flash chip private key and the encryption algorithm is double-encrypted to cache all generated keys. When the algorithm adjustment unit determines that the remaining power is less than 10% and the threat level signal is a low threat level signal, the encryption algorithm is switched to the AES-128 encryption algorithm; The cache optimization unit is used to analyze the effectiveness of the cache strategy to calculate the cache hit rate. If the cache hit rate is lower than the preset hit threshold, the reinforcement learning algorithm is started to retrain the long short-term memory network through historical authentication data, and the cache strategy is adjusted according to the threat level signal of the authentication risk feedback subsystem. When the threat level signal is a high threat level signal, only the core instruction key is cached.

7. The IoT management system with StarFlash chip isolation and end-to-end encryption according to claim 6 is characterized by: The star flash isolation channel priority scheduling subsystem includes a task classification unit, a bandwidth allocation unit, and a scheduling strategy unit; The task classification unit is used to receive task types and weight data from the business logic module, and classify tasks into emergency, real-time, and non-real-time levels based on the task types and weight data. When a task requests bandwidth, the bandwidth requirements are guaranteed in order of the task classification levels. The bandwidth allocation unit is used to configure independent token buckets for tasks of different weights and determine the minimum bandwidth guarantee ratio for tasks of different weights. According to the token bucket algorithm, the current number of tokens in each token bucket is periodically calculated. If the token bucket corresponding to the emergency task is insufficient, tokens are temporarily preempted from the token buckets of tasks of other weights to meet the corresponding minimum bandwidth guarantee ratio for data transmission. The scheduling strategy unit is used to monitor the channel bandwidth utilization in real time. When the channel bandwidth utilization exceeds the threshold bandwidth, a delay scheduling strategy is adopted for non-real-time tasks. The delay time is calculated by the formula Calculate, where is the basic delay time, is the used bandwidth, is the threshold bandwidth, is the total bandwidth, is the delay time.

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