RFID anti-tracking bidirectional authentication method and system based on lightweight hash chain

Abstract

The invention discloses an RFID anti-tracking bidirectional authentication method and system based on a lightweight hash chain, and relates to the technical field of wireless radio frequency identification security. The system comprises a label initialization module, a bidirectional authentication module, an anti-tracking mechanism module, a hash chain updating module, a secure communication module and a system feedback optimization module. The label initialization module generates initial hash chain data; the bidirectional authentication module adopts a bidirectional challenge-response mechanism to generate a dynamic authentication certificate; the anti-tracking mechanism module generates an untraceable session key through Hash chain value dynamic rotation and randomization mask technology; the Hash chain updating module iteratively updates the Hash chain value; the secure communication module adopts a lightweight encryption algorithm to protect communication data; and the system feedback optimization module dynamically adjusts the hash chain length and the key strength. The real-time performance and the anti-attack capability of the RFID system are remarkably improved, and a high-safety and low-power-consumption bidirectional authentication solution is provided for Internet of Things equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of radio frequency identification security, and particularly to an RFID anti-tracking two-way authentication method and system based on a lightweight hash chain. Background Art

[0002] Radio frequency identification (RFID) technology, as a non-contact automatic identification technology, is widely used in fields such as the Internet of Things, supply chain management, intelligent logistics, and industrial automation. Its core advantage lies in quickly identifying target objects and obtaining data through radio frequency signals. However, in practical applications, the security issues of RFID systems are particularly prominent. In particular, the two-way authentication mechanism between tags and readers is vulnerable to threats such as tracking attacks, replay attacks, and data tampering.

[0003] Currently, traditional RFID authentication schemes mainly rely on static keys or fixed hash chains to implement identity verification. For example, some systems adopt a one-way authentication mechanism, where only the reader verifies the tag's identity, lacking the legal validity check of the tag on the reader, resulting in a relatively high risk of man-in-the-middle attacks. In addition, the authentication method based on static keys has the defect that it cannot be dynamically updated after the key is leaked. Attackers can use the long-term fixed key to track the tag's location, seriously threatening user privacy.

[0004] In the prior art, some schemes introduce hash chain technology to enhance dynamicity, but there are still the following key problems: insufficient anti-tracking ability: the update mode of traditional hash chains is easily predicted by attackers, resulting in the association of session keys and being unable to effectively resist tracking attacks; excessive computational overhead: complex encryption algorithms (such as AES, RSA) are difficult to adapt to resource-constrained IoT tags, resulting in increased authentication latency and energy consumption; lack of dynamic adaptability: existing systems do not dynamically adjust security parameters (such as hash chain length, key strength) in combination with real-time communication status and cannot cope with diverse attack scenarios; weak two-way authentication: most schemes do not implement strict two-way authentication, and the identity of the reader is easily forged, resulting in man-in-the-middle attacks and data leakage.

[0005] Therefore, there is an urgent need for an RFID two-way authentication system with anti-tracking, low power consumption, and dynamic adaptability to solve the contradictions in security, efficiency, and scalability of the prior art and meet the comprehensive requirements of IoT devices for high security, real-time performance, and resource optimization. Summary of the Invention

[0006] The purpose of the present invention is to provide an RFID anti-tracking two-way authentication method and system based on a lightweight hash chain, aiming to solve the problems of weak anti-tracking ability, high computational resource consumption, poor dynamic adaptability, and weak two-way authentication mechanism existing in the existing RFID systems. By introducing lightweight hash chain technology, dynamic seed injection, randomized mask mechanism, and adaptive learning algorithm, the present invention can efficiently and securely achieve two-way authentication between tags and readers, while providing anti-tracking protection and dynamic parameter optimization functions, and providing a high-security, low-power, and highly real-time two-way authentication solution for Internet of Things devices. Through modular design and collaborative working mechanism, the present invention significantly improves the security, efficiency, and scalability of the RFID system, providing reliable technical support for device authentication and data security in the field of the Internet of Things.

[0007] To achieve the above object, the present invention provides an RFID anti-tracking two-way authentication system based on a lightweight hash chain, which includes the following main modules: a tag initialization module, a two-way authentication module, an anti-tracking mechanism module, a hash chain update module, a secure communication module, and a system feedback optimization module.

[0008] The tag initialization module generates an initial value of the lightweight hash chain based on a preset key, completes tag identity binding through dynamic seed injection technology, and generates initial hash chain data;

[0009] The two-way authentication module, based on the initial hash chain data, uses a two-way challenge-response mechanism to implement interactive authentication between the tag and the reader, and generates a dynamic authentication credential by combining lightweight hash operations;

[0010] The anti-tracking mechanism module, based on the dynamic authentication credential, generates an untraceable session key through hash chain value dynamic rotation and randomized mask technology;

[0011] After the hash chain update module completes authentication based on the session key, it applies a one-way hash function to iteratively update the hash chain value and generate the next round of authentication data;

[0012] The secure communication module, based on the updated hash chain value, uses a lightweight encryption algorithm to provide end-to-end protection for communication data and generates a tamper-resistant encrypted communication stream;

[0013] The system feedback optimization module, based on the security status of the encrypted communication stream, dynamically adjusts the hash chain length and key strength through an adaptive learning algorithm and generates a system optimization strategy.

[0014] Furthermore, the tag initialization module includes a key generation sub-module, a seed injection sub-module, and an identity binding sub-module;

[0015] The key generation sub-module generates an initial key based on a preset security protocol and generates the root node of the hash chain using a lightweight hash function;

[0016] The seed injection sub-module injects a variable seed through a dynamic random number generator and generates the initial value of the dynamic hash chain in combination with the tag unique identifier;

[0017] The identity binding sub-module completes the identity binding between the tag and the system based on the initial value of the dynamic hash chain using asymmetric encryption technology.

[0018] Furthermore, the dynamic random number generator uses a hardware entropy source to ensure the randomness and unpredictability of the seed; the hardware entropy source is based on a physically unclonable function (PUF) and generates a unique seed by extracting the hardware characteristics of the tag.

[0019] Furthermore, the mutual authentication module includes a challenge generation sub-module, a response verification sub-module, and a credential generation sub-module;

[0020] The challenge generation sub-module generates a dynamic challenge code based on the reader random number and attaches a timestamp to prevent replay attacks;

[0021] The response verification sub-module verifies the legitimacy of the tag response through the hash chain value and adopts a dynamic window mechanism to tolerate communication delays;

[0022] The credential generation sub-module generates a dynamic authentication credential based on the verification result and embeds the encrypted digest of the session key.

[0023] Furthermore, in the mutual challenge-response mechanism, the interaction process between the reader and the tag adopts the zero-knowledge proof principle to ensure that sensitive information is not leaked.

[0024] Furthermore, the anti-tracking mechanism module includes a rotation control sub-module, a mask generation sub-module, and a key derivation sub-module;

[0025] The rotation control sub-module dynamically adjusts the rotation frequency of the hash chain value according to the number of sessions to prevent the leakage of fixed patterns;

[0026] The mask generation sub-module uses a pseudo-random function to generate a one-time mask and randomizes and confuses the hash chain value;

[0027] The key derivation sub-module generates an uncorrelated session key based on the confused hash chain value using a key expansion algorithm;

[0028] The randomization mask technology specifically combines a chaotic sequence and a hash operation to generate an unpredictable mask value.

[0029] Furthermore, the hash chain update module includes an iterative operation sub-module, a data synchronization sub-module, and a failure control sub-module;

[0030] The iterative operation sub-module applies a one-way hash function to iteratively update the current hash chain value to ensure forward security;

[0031] The data synchronization sub-module synchronizes the hash chain status of the reader and the tag through a ciphertext channel to prevent data inconsistency;

[0032] The failure control sub-module detects abnormal update behaviors and triggers a key reset mechanism to resist physical attacks.

[0033] Furthermore, the secure communication module includes an encryption protocol sub-module, an integrity verification sub-module, and an anti-interference sub-module;

[0034] The encryption protocol sub-module uses a lightweight block cipher algorithm to encrypt communication data in segments to reduce computational overhead;

[0035] The integrity verification sub-module verifies data integrity based on a hash message authentication code (HMAC) to prevent tampering;

[0036] The anti-interference sub-module resists wireless channel interference through frequency hopping technology to ensure communication stability;

[0037] The lightweight block cipher algorithm is the PRESENT algorithm, which is suitable for resource-constrained Internet of Things devices.

[0038] Furthermore, the system feedback optimization module includes a security assessment sub-module, a parameter adjustment sub-module, and a policy implementation sub-module;

[0039] The security assessment sub-module analyzes the attack characteristics and security vulnerabilities of communication flows in real time to generate a risk assessment report;

[0040] The parameter adjustment sub-module, based on the evaluation results, uses an adaptive learning algorithm to dynamically optimize the hash chain length, key update period, and encryption strength;

[0041] The policy implementation sub-module deploys the optimization policy to the tag and the reader through remote firmware upgrade;

[0042] The adaptive learning algorithm is specifically a dynamic parameter optimization model based on deep reinforcement learning, which can automatically adjust system parameters according to the security status and historical data of communication flows to improve security and performance.

[0043] An RFID anti-tracking mutual authentication method based on a lightweight hash chain includes the following steps:

[0044] Step S1: Generate an initial value of a lightweight hash chain through tag initialization, and complete tag identity binding using dynamic seed injection technology;

[0045] Step S2: Implement the interactive authentication between the tag and the reader using a two-way challenge-response mechanism, and generate a dynamic authentication credential.

[0046] Step S3: Combine the dynamic rotation of the hash chain value and the randomized masking technology to generate an untraceable session key.

[0047] Step S4: After completing the authentication based on the session key, apply a one-way hash function to iteratively update the hash chain value.

[0048] Step S5: Use a lightweight encryption algorithm to provide end-to-end protection for the communication data and generate a tamper-resistant encrypted communication stream.

[0049] Step S6: Analyze the security status of the communication stream in real time, dynamically adjust the hash chain length and key strength, and optimize the system performance.

[0050] Compared with the prior art, the advantages of the present invention are as follows:

[0051] (1) The anti-tracking ability is significantly enhanced: Through the dynamic rotation of the hash chain value and the randomized masking technology, the present invention effectively blocks the long-term tracking of the tag by the attacker, ensures the unlinkability of the session key, and solves the problem that traditional static keys or fixed hash chains are easily predictable and traceable.

[0052] (2) The consumption of computing resources is greatly reduced: By using lightweight hash operations and block encryption algorithms, the present invention significantly reduces the computing overhead while ensuring security, adapts to resource-constrained IoT devices, and solves the problems of authentication delay and excessive energy consumption caused by traditional complex encryption algorithms (such as AES, RSA).

[0053] (3) The dynamic adaptability is significantly improved: Based on the adaptive learning algorithm, the present invention can analyze the security status of the communication stream in real time, dynamically adjust the hash chain length, key update period and encryption strength, and solves the limitation that the existing system cannot cope with diverse attack scenarios.

[0054] (4) The two-way authentication mechanism is more reliable: Through the two-way challenge-response interaction and zero-knowledge proof technology, the present invention realizes the two-way legitimacy verification between the tag and the reader, effectively resists man-in-the-middle attacks and forgery risks, and solves the security vulnerabilities of traditional one-way authentication mechanisms.

[0055] (5) The closed-loop feedback system is improved: Through the system performance feedback optimization module, the present invention combines the remote firmware upgrade strategy to realize the dynamic deployment of security policies and the continuous improvement of long-term operation performance, and solves the problem that the existing system lacks a closed-loop optimization mechanism. Description of the Drawings

[0056] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0057] Figure 1 It is the system module diagram of the present invention;

[0058] Figure 2 It is the structural diagram of the secure communication module of the present invention;

[0059] Figure 3 It is the structural diagram of the system feedback optimization module of the present invention. Specific embodiments

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0061] Embodiment 1: Please refer to Figure 1 As shown, the RFID anti-tracking two-way authentication system based on a lightweight hash chain in this embodiment includes a tag initialization module, a two-way authentication module, an anti-tracking mechanism module, a hash chain update module, a secure communication module, and a system feedback optimization module;

[0062] The tag initialization module generates an initial value of the lightweight hash chain based on a preset key, completes the binding of the tag identity through the dynamic seed injection technology, and generates initial hash chain data;

[0063] The tag initialization module includes a key generation sub-module, a seed injection sub-module, and an identity binding sub-module;

[0064] The key generation sub-module generates an initial key based on a preset security protocol and generates the root node of the hash chain using a lightweight hash function; the security protocol adopts a standardized key generation algorithm to ensure the randomness and uniqueness of the key;

[0065] The seed injection sub-module injects a variable seed through a dynamic random number generator, and generates a dynamic initial value of the hash chain in combination with the unique identifier of the tag; the dynamic random number generator uses a hardware entropy source to ensure the randomness and unpredictability of the seed; the hardware entropy source is based on a physically unclonable function and generates a unique seed by extracting the hardware characteristics of the tag.

[0066] The identity binding sub-module completes the identity binding between the tag and the system based on the initial value of the dynamic hash chain, using asymmetric encryption technology. The specific implementation includes the following steps: Encryption and storage: The system encrypts the initial value of the dynamic hash chain using the public key to generate encrypted data, and stores the encrypted data in the memory of the tag; Decryption and verification: During the authentication process, the tag decrypts the encrypted data using the private key to restore the initial value of the dynamic hash chain, and compares it with the value pre-stored in the system to verify the legality of the tag's identity; Dynamic update: The identity binding sub-module supports dynamic update of the binding information. Through the remote firmware upgrade mechanism, the system can regularly update the public and private key pairs, and re-encrypt and store the initial value of the dynamic hash chain to ensure the security and flexibility of identity management during long-term operation.

[0067] The two-way authentication module realizes the interactive authentication between the tag and the reader based on the initial hash chain data, using the two-way challenge-response mechanism, and generates a dynamic authentication credential by combining lightweight hash operations;

[0068] The two-way authentication module includes a challenge generation sub-module, a response verification sub-module, and a credential generation sub-module;

[0069] The challenge generation sub-module generates a dynamic challenge code based on the reader random number, and attaches a timestamp to prevent replay attacks; The random number is generated by a hardware random number generator to ensure the unpredictability of the challenge code; The timestamp is provided by the system clock, accurate to the millisecond level. The addition of the timestamp effectively prevents replay attacks and ensures the uniqueness and timeliness of each challenge; The challenge code is transmitted to the tag through a secure channel as the initial input for two-way authentication.

[0070] The response verification sub-module verifies the legality of the tag response through the hash chain value, and adopts a dynamic window mechanism to tolerate communication delays; The response verification sub-module receives the response data returned by the tag, and the response data contains the result calculated by the tag based on the challenge code and the current hash chain value; By comparing the hash chain value returned by the tag with the expected value stored in the system, the legality of the response is verified;

[0071] The dynamic window mechanism dynamically adjusts the verification time range according to the network delay, avoids misjudgment caused by communication delays, and at the same time maintains the security of the system.

[0072] The credential generation sub-module generates a dynamic authentication credential based on the verification result and embeds the encrypted digest of the session key.

[0073] If the hypothesis verification result passes, the sub-module generates an encrypted digest containing the session key and embeds it into the authentication credential; the encrypted digest of the session key is generated by a lightweight symmetric encryption algorithm to ensure the confidentiality and integrity of the digest; the authentication credential is transmitted to the tag through a secure channel and serves as the authentication basis for subsequent communications. The validity period of the authentication credential is dynamically set by the system and automatically expires after expiration to prevent the credential from being maliciously exploited.

[0074] In the two-way challenge-response mechanism, the interaction process between the reader and the tag adopts the principle of zero-knowledge proof. The reader sends a challenge code to the tag, and the tag generates a response based on the challenge code and the hash chain value stored in itself without disclosing any sensitive information; the zero-knowledge proof is a cryptographic protocol that allows a prover to prove the truth of a statement to a verifier without disclosing any additional information.

[0075] Based on the dynamic authentication credential, the anti-tracking mechanism module generates an untraceable session key through the dynamic rotation of the hash chain value and the randomized masking technology; the randomized masking technology specifically combines a chaotic sequence and a hash operation to generate an unpredictable masking value; the anti-tracking mechanism module includes a rotation control sub-module, a masking generation sub-module, and a key derivation sub-module.

[0076] The rotation control sub-module dynamically adjusts the rotation frequency of the hash chain value according to the number of sessions to prevent an attacker from inferring the hash chain value through a fixed pattern; the dynamic adjustment of the rotation frequency is based on a session counter and a preset rotation strategy. After each session ends, the session counter increments, and the rotation frequency is adjusted according to the value of the counter; the rotation strategy is designed using a piecewise function: in the initial stage (when the number of sessions is small), the rotation frequency is low to reduce the computational overhead; as the number of sessions increases, the rotation frequency gradually increases to enhance security; the rotation trigger condition is based on the state change of the hash chain value. When the hash chain value reaches a preset rotation threshold, the sub-module triggers a rotation operation to generate a new hash chain value.

[0077] The masking generation sub-module uses a pseudo-random function to generate a one-time mask to randomly confuse the hash chain value and prevent an attacker from inferring the tag identity by analyzing the hash chain value; the pseudo-random function uses a hash-based pseudo-random number generator (Hash-based PRNG), and its seed is jointly generated by the unique identifier of the tag and the random number of the current session to ensure the uniqueness and unpredictability of the mask.

[0078] The generation process of the disposable mask includes the following steps: generating a random number sequence of a fixed length using a pseudo-random function; performing a bitwise exclusive OR operation on the random number sequence and the hash chain value to generate a confused hash chain value; after each session ends, the mask generation sub-module destroys the current mask and generates a new mask to ensure the one-time use of the mask.

[0079] The key derivation sub-module generates an unlinkable session key based on the confused hash chain value using a key expansion algorithm to ensure that the key for each session is unique and unpredictable; the key expansion algorithm uses a hash-based key derivation function (HKDF), and its inputs include the confused hash chain value, the unique identifier of the tag, and the random number of the current session.

[0080] The key derivation process includes the following steps: using HKDF to expand the input data to generate a key material of a fixed length; splitting the key material into multiple sub-keys for encryption, integrity verification, and session management respectively; after each session ends, the key derivation sub-module destroys the current session key and generates a new session key to ensure the unlinkability of the key.

[0081] After the hash chain update module completes authentication based on the session key, it applies a one-way hash function to iteratively update the hash chain value to generate the authentication data for the next round; the hash chain update module includes an iterative operation sub-module, a data synchronization sub-module, and a failure control sub-module;

[0082] The iterative operation sub-module applies a one-way hash function to iteratively update the current hash chain value to ensure forward security, that is, even if the current hash chain value is leaked, an attacker cannot deduce the previous hash chain value; the one-way hash function uses a lightweight hash algorithm (SHA-256) to ensure the efficiency and security of the hash operation;

[0083] The iterative update process includes the following steps: reading the current hash chain value as the input; performing a hash operation on the current hash chain value using a one-way hash function to generate a new hash chain value; storing the new hash chain value as the input for the next iteration, and at the same time destroying the old hash chain value to ensure forward security; the frequency of the iterative operation is dynamically adjusted by the rotation control sub-module to balance security and computational overhead.

[0084] The data synchronization sub-module synchronizes the hash chain status of the reader and the tag through a ciphertext channel to prevent data inconsistency problems caused by communication interruptions or attacks; the ciphertext channel uses a lightweight encryption algorithm (AES-128) to ensure the confidentiality and integrity of data transmission;

[0085] The data synchronization process includes the following steps: The reader generates a synchronization request containing the current hash chain status and encrypts the request using the session key; After receiving the synchronization request, the tag decrypts it using the session key and verifies the legitimacy of the request; The tag encrypts its own hash chain status and sends it back to the reader, which decrypts and compares the hash chain status of both parties; If the statuses are inconsistent, the reader triggers the hash chain reset mechanism to re-initialize the hash chain value; The frequency of data synchronization is dynamically adjusted by the system to adapt to different communication environments and security requirements.

[0086] The failure control sub-module detects abnormal update behaviors (including at least the hash chain value being tampered with or reused) and triggers the key reset mechanism to resist physical attacks and malicious operations; The abnormal detection algorithm is based on the update pattern and historical records of the hash chain value, and the steps to detect abnormalities are as follows: Monitor the update frequency and change pattern of the hash chain value to identify abnormal patterns (including at least multiple updates in a short period or no update for a long time); Compare the current hash chain value with the historical records to detect whether there are duplicates or conflicts.

[0087] The key reset mechanism includes the following steps: When an abnormal behavior is detected, the sub-module generates a new initial key and transmits it to the tag through the ciphertext channel; After receiving the new key, the tag re-initializes the hash chain value and destroys the old hash chain value; The reader and the tag synchronize the new hash chain status to ensure the normal operation of the system is restored.

[0088] Based on the updated hash chain value, the secure communication module uses a lightweight encryption algorithm to provide end-to-end protection for communication data and generates a tamper-resistant encrypted communication stream; The secure communication module includes an encryption protocol sub-module, an integrity verification sub-module, and an anti-interference sub-module.

[0089] The encryption protocol sub-module segments and encrypts the communication data using a lightweight block encryption algorithm to reduce the computational overhead and adapt to resource-constrained IoT devices; The lightweight block encryption algorithm is the PRESENT algorithm, whose key length is 80 bits or 128 bits, the block length is 64 bits, and it achieves efficient encryption through the Substitution-Permutation Network (SPN) structure. The encryption process is as follows: Cut the communication data into 64-bit blocks, and fill the insufficient part with random data; Use the PRESENT algorithm to encrypt each block of data, and the core operations include round key addition, S-box substitution (mapping 4-bit input to 4-bit output), and bit permutation; The encrypted data is transmitted through the secure channel, and the reverse round key and inverse S-box are used to restore the plaintext during decryption.

[0090] The integrity verification sub-module verifies data integrity based on the Hash Message Authentication Code (HMAC) to prevent data from being tampered with during transmission. The HMAC generation process includes concatenating the session key and communication data according to specific rules (such as padding the key to an integer multiple of the block length), performing two hash operations (inner hash and outer hash) on the concatenated data to generate a 256-bit HMAC value, appending the HMAC value to the tail of the communication data, and transmitting it together with the encrypted data.

[0091] The verification indicates that the receiver extracts the HMAC value, recalculates the HMAC using the same session key and the received data, and compares the received HMAC value with the calculated value. If they are the same, the data integrity is confirmed.

[0092] The anti-interference sub-module resists wireless channel interference through frequency hopping technology to ensure communication stability. The frequency hopping technology generates a frequency hopping sequence based on a Pseudo-Random Number Generator (PRNG). The seed is jointly derived from the tag unique identifier and the session key. The frequency hopping process is as follows: Before communication, the reader and the tag synchronize the frequency hopping sequence to ensure that both parties use the same frequency switching rule. After each data transmission, switch to the next predetermined frequency according to the frequency hopping sequence, and the frequency hopping interval is at the millisecond level. If channel interference is detected (such as a sudden drop in signal strength or an increase in the bit error rate), trigger the adaptive frequency hopping mechanism to dynamically select an alternative frequency band. At the same time, regularly synchronize the frequency hopping sequence and the clock through the encrypted channel to prevent frequency mismatch caused by communication delay.

[0093] The system feedback optimization module, as shown in Figure 3 Based on the security status of the encrypted communication flow, dynamically adjusts the hash chain length and key strength through an adaptive learning algorithm to generate system optimization strategies. The system feedback optimization module includes a security assessment sub-module, a parameter adjustment sub-module, and a policy implementation sub-module.

[0094] The security assessment sub-module analyzes the attack characteristics and security vulnerabilities of the communication flow in real time to generate a risk assessment report. The attack characteristics are identified using a traffic analysis algorithm (rule-based signature matching) to identify known attack patterns. Vulnerability detection uses fuzz testing or static code analysis techniques to detect potential vulnerabilities in the protocol implementation (including buffer overflows and key management defects). The risk assessment report includes the comprehensive attack frequency, vulnerability severity, and historical data to generate a quantitative risk score.

[0095] The parameter adjustment sub-module dynamically optimizes the hash chain length, key update period, and encryption strength based on the evaluation results using an adaptive learning algorithm. The adaptive learning algorithm is specifically a dynamic parameter optimization model based on deep reinforcement learning, which can automatically adjust system parameters according to the security state and historical data of the communication flow to improve security and performance. The model architecture of the deep reinforcement learning is the Actor-Critic framework:

[0096] Actor network (Actor): The input is the state s t , including the security score S t , performance metric P t , and the current parameter configuration C t ; The output is the action a t , representing the parameter adjustment instructions including the increment or decrement of the hash chain length, the adjustment ratio of the key update period, and the encryption strength level. The policy function is used to select the action a t under the given state s t , that is, the parameter adjustment instructions, expressed as follows:

[0097] π(a t |s t ; θ) = Softmax(W a ·ReLu(W s· s t +b s )+b a )

[0098] where π(a t |s t ; θ) is the policy function, θ is the parameter of the actor network, Softmax is the normalization function that converts the output into a probability distribution to ensure the rationality of action selection; ReLu is the activation function; W a is the weight matrix from the hidden layer to the action output layer, mapping the hidden layer output to the action space; b a is the bias vector of the action output layer; W s is the weight matrix of the state input layer; b s is the bias vector of the state input layer;

[0099] Critic network (Critic): The input is the state s t ; The state value evaluates the comprehensive security-performance value of the current state; The value function is used to evaluate the long-term reward of the current state s t , guiding policy optimization, expressed as follows:

[0100]

[0101] where, are the parameters of the critic network, ReLu is the activation function; W v is the weight matrix from the hidden layer to the action output layer, mapping the output of the hidden layer to the action space; b v is the bias vector of the value output layer; W c is the weight matrix of the state input layer; b c is the bias vector of the state input layer;

[0102] The reward function consists of a security reward function, a performance reward function, and a penalty term. The security reward function R sec encourages the system to intercept more attacks and improve security, and is expressed as follows:

[0103]

[0104] where cs is the number of intercepted attacks, CS is the total number of attacks, and α is the weight coefficient of the security reward; The performance reward function R perf encourages the system to reduce communication latency and energy consumption and improve performance, and is expressed as follows:

[0105]

[0106] where β is the weight coefficient of the latency reward, γ is the weight coefficient of the energy consumption reward, D max is the current latency, E is the current energy consumption, D max is the maximum allowable latency, E max is the maximum allowable energy consumption; The penalty term R penalty penalizes the unstable behavior of the system to ensure the reliability of parameter adjustment, and is expressed as follows:

[0107] R penalty =-δ·F

[0108] where δ is the weight coefficient of the penalty term and F is the authentication failure rate; The total reward function R t , is expressed as follows:

[0109] R t =R sec +R penalty +R perf

[0110] where R sec is the security reward function, R perf is the performance reward function, R penalty is the penalty term;

[0111] The training process of the reinforcement learning is as follows: First, store the quadruple (s t , a t , R t , s t+1) to the replay buffer;

[0112] Secondly, the critic network update and the actor network update: The critic network is updated using the minimized temporal difference error, expressed as follows:

[0113]

[0114] where R t is the reward value at the current time step, calculated by the reward function; is the expected value operator, used to calculate the average value of the loss function to ensure the stability of the training process; is the discount factor; is the target network parameter, periodically copied from the parameters of the critic network for stable training; is the value estimation of the target network for the next state s t+1 ; is the value estimation of the critic network for the current state s t ; The actor network update is performed by maximizing the expected reward, expressed as follows:

[0115]

[0116] where J(θ) is the objective function of the policy function, representing the expected value of the long-term cumulative reward, is the gradient of J(θ), and θ is the parameter of the actor network; is the expected value operator; π(a t |s t ; θ) is the policy function; s t is the current state; A(s t ,a t ) is the advantage function, measuring the quality of the action a t relative to the average level;

[0117] The trained actor network outputs the optimal parameter adjustment action a t according to the real-time state s t .

[0118] The policy implementation sub-module deploys the optimized policy to the tag and the reader through remote firmware upgrade;

[0119] An RFID anti-tracking two-way authentication method based on a lightweight hash chain, comprising the following steps:

[0120] Step S1: Generate an initial value of the lightweight hash chain through tag initialization, and complete tag identity binding using the dynamic seed injection technique;

[0121] Step S2: Implement the interactive authentication between the tag and the reader using a two-way challenge-response mechanism to generate a dynamic authentication credential;

[0122] Step S3: Combine the dynamic rotation of the hash chain value and the randomized mask technology to generate an untraceable session key;

[0123] Step S4: After completing the authentication based on the session key, apply a one-way hash function to iteratively update the hash chain value;

[0124] Step S5: Use a lightweight encryption algorithm to perform end-to-end protection on the communication data to generate a tamper-resistant encrypted communication stream;

[0125] Step S6: Analyze the security status of the communication stream in real time, dynamically adjust the hash chain length and key strength, and optimize the system performance.

[0126] The above formulas are all in dimensionless form and only use numerical values for calculation. These formulas are obtained based on a large amount of data and through software simulation, aiming to be as close to the actual situation as possible. The preset parameters in the formulas can be adjusted by those skilled in the art according to specific requirements.

[0127] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0128] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not elaborate on all the details, nor do they limit the present invention to only the specific embodiments. Obviously, many modifications and variations can be made according to the content of this specification. The present specification selects and specifically describes these embodiments to better explain the principles and practical applications of the present invention, so that those skilled in the art in the relevant technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A lightweight hash chain-based RFID anti-tracking two-way authentication system, characterized in that The system includes: A tag initialization module that generates an initial value of a lightweight hash chain based on a preset key, completes tag identity binding through dynamic seed injection technology, and generates initial hash chain data; A two-way authentication module that, based on the initial hash chain data, implements interactive authentication between the tag and the reader using a two-way challenge-response mechanism, and generates a dynamic authentication credential by combining lightweight hash operations; An anti-tracking mechanism module that, based on the dynamic authentication credential, generates an untraceable session key through hash chain value dynamic rotation and randomized masking technology; A hash chain update module that, after completing authentication based on the session key, iteratively updates the hash chain value using a one-way hash function to generate the next round of authentication data; A secure communication module that, based on the updated hash chain value, uses a lightweight encryption algorithm to provide end-to-end protection for communication data and generates a tamper-resistant encrypted communication stream; A system feedback optimization module that, based on the security status of the encrypted communication stream, dynamically adjusts the hash chain length and key strength through an adaptive learning algorithm to generate a system optimization strategy.

2. The system according to claim 1, wherein The tag initialization module includes a key generation sub-module, a seed injection sub-module, and an identity binding sub-module; The key generation sub-module generates an initial key based on a preset security protocol and generates the root node of the hash chain using a lightweight hash function; The seed injection sub-module injects a variable seed through a dynamic random number generator and generates a dynamic hash chain initial value in combination with the tag unique identifier; The identity binding sub-module completes the identity binding between the tag and the system using asymmetric encryption technology based on the dynamic hash chain initial value.

3. The system according to claim 2, wherein The dynamic random number generator uses a hardware entropy source to ensure the randomness and unpredictability of the seed; the hardware entropy source is based on a physical unclonable function (PUF) and generates a unique seed by extracting the hardware characteristics of the tag.

4. The system according to claim 1, characterized in that, The two-way authentication module includes a challenge generation sub-module, a response verification sub-module, and a credential generation sub-module; The challenge generation sub-module generates a dynamic challenge code based on the reader random number and attaches a time stamp to prevent replay attacks; The response verification sub-module verifies the legitimacy of the tag response through the hash chain value and uses a dynamic window mechanism to tolerate communication delays; The credential generation sub-module generates a dynamic authentication credential based on the verification result and embeds the encrypted digest of the session key.

5. The system according to claim 1, wherein In the two-way challenge-response mechanism, the interaction process between the reader and the tag adopts the zero-knowledge proof principle to ensure that sensitive information is not leaked.

6. The system according to claim 1, wherein The anti-tracking mechanism module includes a rotation control sub-module, a mask generation sub-module, and a key derivation sub-module; The rotation control sub-module dynamically adjusts the rotation frequency of the hash chain value according to the number of sessions to prevent the leakage of fixed patterns; The mask generation sub-module uses a pseudo-random function to generate a one-time mask to randomly confuse the hash chain value; The key derivation sub-module generates an uncorrelated session key based on the confused hash chain value using a key expansion algorithm; The randomized masking technology specifically combines a chaotic sequence and a hash operation to generate an unpredictable mask value.

7. The system according to claim 1, characterized in that, The hash chain update module includes an iterative operation sub-module, a data synchronization sub-module, and a failure control sub-module; The iterative operation sub-module applies a one-way hash function to iteratively update the current hash chain value to ensure forward security; The data synchronization sub-module synchronizes the hash chain states of the reader and the tag through a ciphertext channel to prevent data inconsistency; The failure control sub-module detects abnormal update behaviors and triggers a key reset mechanism to resist physical attacks.

8. The system according to claim 1, characterized in that The secure communication module includes an encryption protocol sub-module, an integrity verification sub-module, and an anti-jamming sub-module; The encryption protocol sub-module uses a lightweight block encryption algorithm to segmentally encrypt communication data to reduce computational overhead; The integrity verification sub-module verifies data integrity based on the Hash Message Authentication Code (HMAC) to prevent tampering; The anti-jamming sub-module resists wireless channel interference through frequency hopping technology to ensure communication stability; The lightweight block encryption algorithm is the PRESENT algorithm, which is suitable for resource-constrained Internet of Things devices.

9. The system according to claim 1, wherein The system feedback and optimization module includes a security assessment sub-module, a parameter adjustment sub-module, and a policy implementation sub-module; The security assessment sub-module analyzes the attack characteristics and security vulnerabilities of communication flows in real time to generate a risk assessment report; The parameter adjustment sub-module, based on the assessment results, uses an adaptive learning algorithm to dynamically optimize the hash chain length, key update period, and encryption strength; The policy implementation sub-module deploys the optimization policy to the tag and the reader through remote firmware upgrade; The adaptive learning algorithm is specifically a dynamic parameter optimization model based on deep reinforcement learning, which can automatically adjust system parameters according to the security state and historical data of communication flows to improve security and performance.

10. A two-way authentication method for RFID anti-tracking based on a lightweight hash chain, characterized in that, It includes the following steps: Step S1: Generate an initial value of a lightweight hash chain through tag initialization, and complete tag identity binding using the dynamic seed injection technology; Step S2: Implement mutual authentication between the tag and the reader using a two-way challenge-response mechanism to generate dynamic authentication credentials; Step S3: Combine the dynamic rotation of the hash chain value and the randomized mask technology to generate an untraceable session key; Step S4: After authentication is completed based on the session key, apply a one-way hash function to iteratively update the hash chain value; Step S5: Use a lightweight encryption algorithm to provide end-to-end protection for communication data to generate a tamper-resistant encrypted communication flow; Step S6: Analyze the security state of the communication flow in real time, dynamically adjust the hash chain length and key strength, and optimize system performance.

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