An unmanned vehicle distributed information security protection device

Abstract

The application discloses an unmanned vehicle distributed information security protection device, belonging to the field of vehicle information security, comprising a monitoring and early warning module, which performs environment sensing and state monitoring on the surrounding environment and early warning on abnormal operation behavior; a distributed protection module, which realizes collaborative protection among modules and dynamically adjusts protection strategies; a self-destruction module, which grades threats when detecting uncontrollable threats, automatically starts a self-destruction program and destroys sensitive data on storage media; a data module; a communication module, which realizes information transmission among unmanned vehicle nodes; and a comprehensive control module, which organically combines the above modules and centrally commands and allocates resources; the unmanned vehicle distributed information security protection device provided by the application combines the distributed protection module, the monitoring and early warning module, the self-destruction module and the communication module to provide all-round information security protection for the unmanned vehicle system and prevent data leakage and malicious control of the system by others.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle information security, in particular to a distributed information security protection device for unmanned vehicles. BACKGROUND

[0002] With the development of science and technology, unmanned vehicles are increasingly widely used in various fields. Unmanned vehicles can perform various tasks such as field exploration, inspection, monitoring and transportation, which not only improves work efficiency, but also reduces labor costs and the risk of working in adverse conditions. However, in complex environments, unmanned vehicles face more severe dangers, especially information security threats such as electromagnetic interference and hacker attacks. If the control and information devices of the unmanned vehicle are invaded or destroyed, not only will the ongoing work be stopped or fail, but also the vehicle may be controlled by malicious people to cause damage. Therefore, it is crucial to ensure the information security of unmanned vehicles during task execution.

[0003] Current traditional information security protection devices for unmanned vehicles mostly focus on single-point protection, which can be easily broken, and do not consider harmless treatment of sensitive information in emergency situations, which poses a great security risk and cannot fully meet the requirements. In view of the above problems, a distributed information security protection device for unmanned vehicles is designed. SUMMARY

[0004] The purpose of the present application is to provide a distributed information security protection device for unmanned vehicles to solve the problem that the existing technology mostly focuses on data encryption communication, firewall and intrusion detection, but cannot implement effective protection measures when facing complex and high-intensity security threats, especially when facing high-intensity intrusion.

[0005] To achieve the above purpose, the present application provides a distributed information security protection device for unmanned vehicles, comprising:

[0006] A monitoring and early warning module integrates multiple sensors to perform environmental perception and state monitoring of the surrounding environment, and uses machine learning algorithms to identify and monitor system status in real time, and to warn of abnormal operation behavior;

[0007] A distributed protection module realizes collaborative protection between modules, shares security information, and dynamically adjusts protection strategies;

[0008] A self-destruction module rates threats when detecting uncontrollable threats, automatically triggers a self-destruction program, and destroys sensitive data on storage media;

[0009] A data module includes a data storage unit, a data erasure unit, a data encryption and decryption unit, and a data redundancy unit;

[0010] A communication module realizes information transmission between unmanned vehicle nodes;

[0011] The comprehensive control module organically combines the above modules to perform centralized command and resource allocation.

[0012] Preferably, the monitoring and early warning module uploads data collected by the unmanned vehicle sensor to the shared network in real time to form global situation awareness, and the machine learning algorithm adopts an adaptive hybrid model machine learning algorithm composed of a neural network for feature learning, Bayesian inference for capturing the uncertainty of input data, and an adaptive mechanism for dynamically adjusting neural network parameters.

[0013] P(y|x,0)=∫P(y|z,0)P(z|x,0)P(θ|D)dzdθ;

[0014] Wherein, x is the input data; y is the output prediction; z is the latent variable; θ is the model parameter; D is the current training data set;

[0015] First, the features of the input data x are extracted through a deep neural network, and a latent variable distribution P(z|x, θ) is generated, and the feature extraction formula is:

[0016] z=f NN (x;θ NN );

[0017] Wherein, f NN is the forward propagation function of the neural network; θ NN is the parameter of the neural network, and then the uncertainty of the model parameter θ is captured through Bayesian inference, and the posterior distribution is:

[0018]

[0019] Wherein, P(D|θ) is the likelihood function of the data; P(θ) is the prior distribution of the model parameter;

[0020] Finally, the output distribution prediction model is outputted, and the output distribution is as follows:

[0021]

[0022] The real-time uploaded data is processed and analyzed through streaming big data analysis, and abnormal states are monitored and identified.

[0023] Preferably, the streaming big data analysis and state monitoring include a data stream processing framework, a state monitoring model, and an adaptive threshold adjustment mechanism, specifically:

[0024] First, the data stream is processed in slices, assuming that the data stream arrives in the form of a time series, denoted as Each x tData collected at time t; the data stream is processed by a sliding window, as follows:

[0025] W t = {x t-k+1 ,...,x t};

[0026] wherein W t is the data window at time t; k is the window size;

[0027] On each window W t , the behavior pattern of the data in the window is analyzed by a state monitoring model, and if the data deviates from the normal behavior, it is identified as an abnormal state;

[0028] The state monitoring model specifically includes:

[0029] Mean:

[0030] Standard deviation:

[0031] Abnormality judgment:

[0032] When S t > δ, wherein δ is a set threshold value, it is considered that there is an abnormal behavior at the current time point t;

[0033] Adaptively adjust the threshold value δ to adapt to the dynamic environment of the unmanned vehicle, and set the following formula:

[0034] δ t = α·σ t + β;

[0035] wherein α and β are dynamic adjustment parameters; δ t is the standard deviation of the current window, used to dynamically reflect data changes;

[0036] In combination with the state monitoring results in the window, abnormal behavior is identified and a response is generated, when S t > δ t , it is marked as abnormal, and the system triggers a protection mechanism; when entering the next time step t+1, the window is updated to W t +1, and the above steps are repeated for continuous early warning monitoring.

[0037] Preferably, in the distributed protection module, a TLS two-way authentication protocol is used between nodes to protect the transmission of shared security information, dangerous identification data and protection strategies, and when a node detects a threat, the system takes a dynamic protection strategy through a cooperative mechanism, specifically:

[0038] The system monitors the environment threats in real time through the sensor data of the unmanned vehicle, and evaluates the threat score S threat , and the calculation formula is as follows:

[0039] S threat =α1·T impact +α2·T proximity +α3·T confidence ;

[0040] Wherein, T impact is the potential severity of the threat; T proximity is the spatial proximity of the threat; T confidence is the confidence degree of threat identification; α1α2α3 are all weight factors;

[0041] According to the threat score, the priority of each protection strategy is dynamically allocated to ensure that the key strategy is executed first, which is represented as:

[0042]

[0043] Wherein, w i is the weight of strategy P i ; w j is the priority of strategy P i .

[0044] At the same time, according to the current resource state R avail and the priority of the strategy, the resources are allocated to each protection strategy, which is represented as:

[0045] R i =P i ·R avail ;

[0046] Wherein, R i is the resource allocated to strategy P i .

[0047] Preferably, in the distributed protection module, it also includes adjusting the sensor parameter T k according to the threat score, specifically:

[0048] Laser radar scanning frequency adjustment:

[0049] f lidar =min(f max ,β1·S threat );

[0050] Wherein, f lidar is the laser radar scanning frequency; f max is the maximum scanning frequency of the laser radar; β1 is the adjustment coefficient;

[0051] Camera frequency adjustment:

[0052] f camera = min(f max , β2·S threat );

[0053] wherein f camera is the camera frame rate; β2 is an adjustment coefficient;

[0054] At each time step t real , the threat score and protection strategy are updated, and the threat score is updated as follows:

[0055]

[0056] wherein γ is a smoothing coefficient; is the measured threat score at the current time step;

[0057] According to the new score, priorities and resources are redistributed, and the self-destruction module is started for self-destruction when the threat is high.

[0058] Preferably, the starting premise of the self-destruction module is threat detection, including external threat detection and internal threat detection. The external threat detection detects abnormal behavior by monitoring the external laser radar and camera of the early warning module, and the internal threat detection targets attack behaviors such as malicious tampering, violent disassembly or signal interference, and utilizes the built-in acceleration sensor to monitor the internal state of the vehicle in real time, specifically:

[0059] Through the acceleration sensor, it is detected whether there is an abnormal environment, and whether there is a violent vibration or disassembly behavior is monitored:

[0060]

[0061] When A(t) > A threshold , there is a disassembly threat;

[0062] wherein is a three-axis acceleration component; A threshold is a vibration threshold;

[0063] Through the electronic shielding signal strength P jam , it is detected whether the communication frequency band is interfered, and if P jam > P threshold , it is threatened by signal interference, wherein P threshold is a signal interference threshold;

[0064] The internal threat score S internal is calculated by comprehensively considering the above factors:

[0065] S internal = β1·A(t) + β2·P jam ;

[0066] Through external threat score S threat And internal score S internal Comprehensive judgment whether to start self-destruction program; trigger mechanism uses logical judgment and preset threshold, when the comprehensive threat score exceeds the threshold, trigger the self-destruction module:

[0067] S total = γ1·S threat + γ2·S internal ;

[0068] Wherein, S total For the comprehensive threat score; γ1, γ2 is the weight factor, used to balance the internal and external threat influence;

[0069] When S total >S threshold , start self-destruction module, S threshold For the threat score threshold triggering self-destruction; through the start of self-destruction module to realize data destruction, data destruction way through data covering, storage chip destruction, key data encryption destruction;

[0070] Covering the storage medium several times to clear data traces, using pseudo-random number generator to generate covering data:

[0071] D fianl = Random(k, n);

[0072] Wherein, k is the number of covering, n is the storage medium size;

[0073] Send high-voltage pulse to the storage chip, physical destruction chip, pulse formula as follows:

[0074] Vpulse = Vmax·sin(2πft);

[0075] Wherein, V max Is the maximum pulse voltage, f is the pulse frequency.

[0076] Preferably, each unit of the data module is specifically:

[0077] Data storage unit uses distributed database for data storage, including hierarchical storage strategy and role-based access control mechanism;

[0078] Data erasing unit provides logical erasing and physical erasing method, through multiple writing random data covering or using disk fragmentation physical means, and configure the automatic erasing process; When detecting that the storage medium is no longer safe or data is no longer needed, automatically execute data erasing operation;

[0079] The data encryption and decryption unit uses the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm to encrypt and decrypt data, and integrates a TPM or HSM hardware encryption module, while a secure key management system is configured;

[0080] The data redundancy unit adopts a multiple-copy strategy and an off-site backup strategy, guarantees data consistency and high availability through a distributed consistency algorithm, and sets an automatic recovery mechanism to automatically recover data from a backup or redundant node when a data node failure is detected, thereby ensuring continuous availability of the system.

[0081] Preferably, the communication module uses multiple methods to achieve communication security, specifically including:

[0082] First, symmetric encryption and asymmetric encryption algorithms are used to encrypt the communication content. The symmetric encryption process will use the high-strength AES symmetric encryption algorithm to divide the input plaintext into fixed-size blocks, generate ciphertext through multiple rounds of encryption operations, specifically as follows: first, the plaintext is XORed with the initial key to obtain the encrypted initial state; then, each byte is nonlinearly replaced using the S-Box through byte substitution; then, the rows of the state matrix are cyclically shifted; subsequently, in the column confusion step, each column of data is confused through matrix multiplication operations on a finite field; finally, the key addition round and the subkey are XORed again, and the entire process is repeated for 10 rounds, and the encrypted ciphertext is finally output; the AES decryption process is the inverse operation of encryption, and the plaintext is gradually restored through inverse processing, specifically as follows: first, the ciphertext block is subjected to the initial key addition round, and the ciphertext is XORed with the subkey of the last round; subsequently, inverse column confusion is performed, and each column of data is subjected to inverse finite field matrix multiplication operations; then, inverse row shifting cyclically shifts the rows of the state matrix back to their original positions according to the shifting rule during encryption; then, in the inverse byte substitution, each byte is nonlinearly replaced using the inverse S-Box to restore the original state, and the plaintext data is finally recovered through repeated inverse processing of each round;

[0083] RSA is used as the asymmetric encryption algorithm in the communication module, and data is encrypted using the public key and decrypted using the private key, specifically as follows: first, a key pair is generated, two large prime numbers p and q are randomly generated, n = p·q and the Euler function φ(n) = (p-1)(q-1) are calculated, an encryption exponent e is selected such that 1 < e < φ(n) and gcd(e, φ(n)) = 1, and a decryption exponent d is calculated to satisfy the following formula:

[0084] e·d≡1(modφ(n));

[0085] where the public key is (e, n) and the private key is (d, n);

[0086] The symmetric key K is encrypted using the public keyδ Encryption, as shown below:

[0087]

[0088] Where C k is the encrypted ciphertext;

[0089] Finally, the symmetric key is restored using the private key, as shown below:

[0090]

[0091] The communication module verifies the identity of both parties using a multi-factor authentication mechanism, and at the same time, uses hash functions and digital signature technology to ensure the integrity and authenticity of the communication content. SHA-256 is a one-way encryption hash function that maps input data of any length to a 256-bit hash value. Specifically, first, pad the input data to make its length an integer multiple of 512 bits; then, initialize a 256-bit hash buffer containing eight 32-bit words of initial values; then, for each 512-bit block, perform a series of bit operations, logical operations, and cyclic shift operations to update the hash buffer value; finally, concatenate the eight 32-bit words in the hash buffer to form a 256-bit hash value as the output; ECDSA is a digital signature algorithm based on elliptic curve cryptography, which generates a hash value H(M) for a message M using a hash algorithm SHA-256, and generates a signature pair (r, s) using a private key d and a random number k, as shown below:

[0092] r = (kG) x mod n;

[0093] s = k -1 (H(M) + dr) mod n;

[0094] Where G is the elliptic curve base point;

[0095] Use public key Q = dG to verify the signature pair (r, s):

[0096] w = s -1 mod n;

[0097] μ1 = H(M)w mod n, μ2 = rw mod n;

[0098] (x, y) = μ1G + μ2Q;

[0099] If r ≡ x mod n, the signature is valid, otherwise the signature is invalid;

[0100] Finally, use the secure network protocol to establish an encrypted communication tunnel.

[0101] Therefore, the unmanned vehicle distributed information security protection device aims to improve the information security and reliability of the unmanned vehicle during task execution, so that it can effectively deal with complex and variable security threats. Real-time sharing of security information between nodes using encryption communication protocols prevents the system from having the ability to dynamically adjust protection strategies. Each subsystem of the unmanned vehicle is modularly designed, so that each protection module can work independently or be flexibly combined to meet diverse protection needs and have good scalability. By integrating a self-destruction device in the laser radar, camera and other modules, when an uncontrollable high-intensity threat is detected, the self-destruction program can be automatically triggered to completely destroy sensitive data. The camera, laser radar and other modules also have multiple sensors and monitoring software that use machine learning algorithms to monitor system status in real time and respond quickly to abnormal behavior. Finally, encryption algorithms and authentication mechanisms ensure the security and reliability of information transmission between each module of the unmanned vehicle and the command center node.

[0102] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0103] Figure 1 Figure 1 is a structural schematic diagram of the unmanned vehicle distributed information security protection device of the present application;

[0104] Figure 2 Figure 2 is a workflow diagram of the unmanned vehicle distributed information security protection device of the present application. DETAILED DESCRIPTION

[0105] The following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0106] Referring to Figure 1 , the unmanned vehicle distributed information security protection device comprises:

[0107] The monitoring and early warning module performs all-around environmental perception and state monitoring on the surrounding environment by integrating various sensors such as laser radar, camera, infrared sensor, ultrasonic sensor, etc. An adaptive hybrid model machine learning algorithm combining deep learning and Bayesian inference is used to dynamically adjust the threat detection capability in a complex system. The neural network models the nonlinear characteristics, and the Bayesian inference captures the uncertainty of the model to dynamically adjust the detection capability of the system. Specifically, this model can adaptively adjust its parameters so that the model remains robust when facing changing input distribution. The core of the adaptive hybrid model can be divided into several parts: neural network for feature learning, Bayesian inference for capturing the uncertainty of input data, and adaptive mechanism for dynamically adjusting neural network parameters. The expression of the adaptive hybrid model is as follows:

[0108] P(y|x,0)=∫P(y|z,0)P(z|x,0)P(θ|D)dzdθ;

[0109] where x is the input data (such as unmanned vehicle radar and camera data); y is the output prediction (such as whether there is a safety threat); z is the latent variable (hidden variable generated by the neural network); θ is the model parameter; D is the current training data set;

[0110] First, the features of the input data x are extracted by a deep neural network (DNN), and the latent variable distribution P(z|x, θ) is generated. The feature extraction formula is as follows:

[0111] z=f NN (x;θ NN );

[0112] where f NN is the forward propagation function of the neural network; θ NN is the parameter of the neural network, and then the uncertainty of the model parameter θ is captured by Bayesian inference, and the posterior distribution is as follows:

[0113]

[0114] where P(D|θ) is the likelihood function of the data; P(θ) is the prior distribution of the model parameter;

[0115] The final output distribution prediction model is as follows:

[0116]

[0117] Real-time uploaded data is processed and analyzed through streaming big data analysis, and abnormal states are monitored and identified. Streaming big data analysis and state monitoring include three parts: data stream processing framework, state monitoring model, and adaptive threshold adjustment mechanism, which are as follows:

[0118] Firstly, the data stream is processed in pieces, assuming that the data stream arrives in the form of time series, denoted as Each x t is the data collected at time t; the data stream is processed in pieces by sliding window, as follows:

[0119] W t = {x t-k+1 ,...,x t};

[0120] Wherein, W t is the data window at time t; k is the window size;

[0121] On each window W t , the behavior pattern of the data in the window is analyzed by the state monitoring model, and if the data deviates from the normal behavior, it is identified as an abnormal state;

[0122] The state monitoring model specifically includes:

[0123] Mean:

[0124] Standard deviation:

[0125] Abnormal judgment:

[0126] When S t > δ, wherein δ is a set threshold, it is considered that there is an abnormal behavior at the current time point t;

[0127] Adaptive adjustment of threshold δ, so as to adapt to the dynamic environment of unmanned vehicle, set the following formula:

[0128] δ t = α·σ t + β;

[0129] Wherein, α and β are dynamic adjustment parameters; δ t is the standard deviation of the current window, which is used to dynamically reflect the data change;

[0130] In combination with the state monitoring result in the window, the abnormal behavior is identified and the response is generated, when S t > δ t , it is marked as abnormal, and the system triggers the protection mechanism; when entering the next time step t+1, the window is updated as W t +1, and the above steps are repeated for continuous early warning monitoring.

[0131] The distributed protection module is a collaborative protection between various subsystems in the unmanned vehicle, sharing security information, dynamically adjusting protection strategies, and improving the overall protection capability of the system. By equipping the unmanned vehicle with independent protection units, including firewalls, intrusion detection systems (IDS), and antivirus software, network attacks and electronic interference are prevented, and the radar and camera used for navigation in the unmanned vehicle are reused as the main sensing devices to build a security protection system. A TLS two-way authentication protocol is used between nodes to verify the identity twice, securely exchange in the session key through asymmetric encryption, and generate independently each time, reducing the risk of key leakage. In the distributed protection module, TLS is mainly used to protect the transmission of shared security information, threat identification data, and protection strategies between nodes to prevent eavesdropping, tampering, and masquerading attacks. To meet the low-latency, high-security, and resource-constrained requirements of the unmanned vehicle environment, the TLS 1.3 protocol and its 0-RTT handshake feature are used to reduce latency, lightweight encryption algorithms (such as ChaCha20-Poly1305) and hardware acceleration techniques are used to improve encryption performance, and session recovery and dynamic key update mechanisms are used to enhance key management security. In the distributed scenario, multicast TLS and multi-path transmission are used to optimize network adaptation, and dynamic certificate distribution and decentralized key management are combined to improve communication node trust management efficiency. Through these optimizations, TLS can better meet the requirements of the distributed protection module of the unmanned vehicle for efficient, secure, and low-latency communication.

[0132] In the distributed protection module, when a node detects a threat, the system adopts a dynamic protection strategy through a collaborative mechanism to improve the overall protection capability. First, the node will immediately send an alarm to surrounding nodes and the cloud control system, triggering collaborative protection by sharing threat source location information, attributes, and severity. At the same time, the vehicle path is adjusted according to the threat situation, and the threat area is re-planned to avoid the threat area, and safety constraints are introduced to optimize the path. For resource allocation, the system dynamically allocates computing power and communication bandwidth, prioritizing threat-related tasks. In addition, sensor parameters are dynamically adjusted, such as increasing the scanning frequency of laser radar, the frame rate of the camera, or enabling backup sensors (such as infrared sensors) to enhance sensing capability, ensuring high-precision threat perception in complex environments. For higher threat levels, the system can activate electronic shielding devices for active interference, while faking GPS signals to confuse attackers and notify surrounding command vehicles and command centers. In the face of serious threats, an emergency response mechanism is started, data modules are activated for data backup, and self-destruction modules are activated for self-destruction. All threat information is uploaded to the cloud control system in real time, which analyzes and optimizes the global protection strategy and allocates support nodes. These protection strategies are driven by threat scoring, dynamically adjusting priority, resource allocation, and parameter settings to achieve flexible and adaptive protection capabilities, ensuring real-time responsiveness and safety of the unmanned vehicle system.

[0133] The core of dynamically adjusting protection strategy is to perceive the threat situation in real time and respond flexibly. The system monitors the environmental threat in real time through the sensor data (laser radar and camera) of the unmanned vehicle, evaluates the threat score S threat , and the calculation formula is as follows:

[0134] S threat = a1T impact + a2T proximity + a3T confidence ;

[0135] Wherein, T impact is the potential severity of the threat (range: [0, 1]); T proximity is the spatial proximity of the threat (range: [0, 1]); T confidence is the confidence degree of threat identification (range: [0, 1]); a1, a2, a3 are weight factors;

[0136] According to the threat score, the priority of each protection strategy is dynamically allocated to ensure that key strategies are executed first, such as path adjustment, sensor optimization, etc., which is represented as:

[0137]

[0138] Wherein, w i is the weight of strategy P i (pre-set according to the importance of the strategy); w j is the priority of strategy P i (range: [0, 1]);

[0139] At the same time, according to the current resource state R avail and the priority of the strategy, the resources are allocated to each protection strategy, which is represented as:

[0140] R i = P i ·R avail ;

[0141] Wherein, R i is the resource allocated to strategy P i .

[0142] Adjust the sensor parameter T k according to the threat score, specifically:

[0143] Laser radar scanning frequency adjustment:

[0144] f lidar = min(f max , b1S threat );

[0145] Wherein, f lidaris the scanning frequency of the laser radar; f max is the maximum scanning frequency of the laser radar; β1 is an adjustment coefficient;

[0146] Camera frequency adjustment:

[0147] f camera = min(f max , β2·S threat );

[0148] wherein f camera is the camera frame rate; β2 is an adjustment coefficient;

[0149] At each time step t real , the threat score and the protection strategy are updated, and the threat score is updated as follows:

[0150]

[0151] wherein γ is a smoothing coefficient; is the measured threat score at the current time step;

[0152] According to the new score, priorities and resources are reassigned, and the self-destruction module is started to self-destruct when the threat is high.

[0153] The self-destruction module is the last line of defense for data security and hardware protection in an unmanned vehicle system. When the protection system detects an uncontrollable threat, it rates the threat and automatically triggers the self-destruction program to completely destroy sensitive data on the storage medium, achieving the purpose of preventing information leakage. The prerequisite for starting the self-destruction module is threat detection. Through the monitoring and warning module, abnormal behavior is detected, and the distributed protection module classifies external threats and dynamically adjusts protection strategies. When high-intensity threats or uncontrollable security incidents are detected, the self-destruction module triggers the data destruction function through the trigger mechanism to respond quickly to prevent key data from being leaked or devices from being maliciously used. This includes external threat detection and internal threat detection. External threat detection detects abnormal behavior through external laser radars and cameras in the monitoring and warning module. Internal threat detection targets malicious tampering, brute force disassembly, or signal interference attacks. It uses built-in acceleration sensors to monitor the internal state of the vehicle in real time. Specifically:

[0154] Through the acceleration sensor, abnormal environment detection is performed to monitor whether there is violent vibration or disassembly behavior:

[0155]

[0156] When A(t) > A threshold , there is a disassembly threat.

[0157] wherein is a three-axis acceleration component; A threshold is a vibration threshold;

[0158] Through the electronic shielding signal strength P jam Detect whether the communication frequency band is interfered, if P jam >P threshold , the signal is interfered by threat, wherein, P threshold is the signal interference threshold;

[0159] The above factors are calculated by the internal threat score S internal :

[0160] S internal =β1·A(t)+β2·P jam ;

[0161] The external threat score S threat and the internal score S internal are integrated to judge whether to start the self-destruction program; the trigger mechanism adopts logic judgment and preset threshold, when the comprehensive threat score exceeds the threshold, the self-destruction module is triggered:

[0162] S total =γ1·S threat +γ2·S internal ;

[0163] Wherein, S total is the comprehensive threat score; γ1, γ2 is the weight factor, used to balance the internal and external threat influence;

[0164] When S total >S threshold , the self-destruction module is started, S threshold is the threat score threshold of triggering self-destruction; through the start of self-destruction module, the data destruction is realized, and the sensitive data stored by the unmanned vehicle is prevented from being illegally acquired. The data destruction mode mainly includes data covering, storage chip destruction and key data encryption destruction.

[0165] The storage medium is covered for many times to clear the data traces, and the covering data is generated by using a pseudo-random number generator:

[0166] D fianl =Random(k,n);

[0167] Wherein, k is the covering times, and n is the storage medium size;

[0168] High-voltage pulse is sent to the storage chip, and the chip is physically destroyed, and the pulse formula is as follows:

[0169] Vpulse=Vmax·sin(2πft);

[0170] Wherein, V max is the maximum pulse voltage, and f is the pulse frequency.

[0171] Data module, mainly including data storage unit, data erasure unit, data encryption and decryption unit and data redundancy unit;

[0172] The data storage unit uses a distributed database for data storage, and through a hierarchical storage strategy and a role-based access control (RBAC) mechanism, ensures the high availability, integrity and legal access of data.

[0173] The data erasure unit provides logical erasure and physical erasure methods, through multiple writing of random data covering or using physical means such as disk fragmentation, ensures that data cannot be recovered, and configures an automatic erasure process, when it is detected that the storage medium is no longer secure or the data is no longer needed, automatically performs data erasure operation.

[0174] The data encryption and decryption unit uses symmetric encryption algorithms such as AES and asymmetric encryption algorithms such as RSA to encrypt and decrypt data, ensures the confidentiality and privacy of data, and through the integration of hardware encryption modules such as TPM or HSM improves efficiency and security, and at the same time configures a secure key management system (such as KMS) to ensure the secure storage and use of keys.

[0175] The data redundancy unit adopts multiple copy strategy (such as three copy strategy) and offsite backup strategy, through distributed consistency algorithm to ensure the consistency and high availability of data, and sets up automatic recovery mechanism, when it is detected that the data node fails, automatically recovers data from backup or redundant node, ensures the continuous availability of system. Through these functions and specific implementation ways, the data module in the unmanned vehicle information security distributed protection device provides comprehensive security protection, effectively ensures the confidentiality, integrity and high availability of data.

[0176] With data storage and backup, data encryption and decryption, permission management, data integrity check, redundancy and fault tolerance, and data destruction function, it is very important to ensure the security and integrity of data. Data storage and backup store data on multiple nodes through a distributed file system, and configure periodic or real-time backup strategy, backup critical data to remote storage or cloud storage.

[0177] In the data module, to ensure the security and integrity of data, data encryption and decryption functions are the core part. High-strength encryption algorithms are used to protect data, preventing sensitive information from being stolen or tampered with during storage or transmission. Permission management ensures that only authorized entities can access data through role-based access control and multi-factor authentication mechanisms; data integrity checks are achieved through the combination of secure hash algorithms and blockchain technology to protect data comprehensively. The system uses Hyperledger Fabric as the blockchain framework, taking advantage of its high performance, access control, and privacy protection features to record data operation logs. Each data operation (such as adding, modifying, or deleting) generates a unique verification code through the SHA-256 hash algorithm, which is packaged into a block along with operation type, timestamp, and other information, and added to the blockchain after being verified by the PBFT consensus algorithm. The chain structure and distributed storage of the blockchain ensure the non-tamperability of data logs, and tampering with any log will cause the entire chain to fail. In addition, the system supports tracing data history operations based on the blockchain ledger, verifying whether the current data verification code is consistent with the record in the blockchain, and detecting whether the data has been tampered with. This mechanism, combined with the data operation rules defined by the smart contract, not only guarantees the integrity and non-tamperability of data, but also improves the transparency and traceability of data operations. Redundancy and fault tolerance ensure the continuous availability of data when storage nodes fail by configuring data replication strategies and automatic fault recovery mechanisms. Data destruction triggers a self-destruction mechanism when serious security threats are detected, quickly and safely destroying sensitive data on storage media through hardware melting or disk erasing technology. Through the above methods, the data storage module effectively ensures the security, integrity, and availability of unmanned vehicle data, ensuring that data can be safely and reliably stored and managed in various complex environments and potential threats. In addition, critical data encryption keys can be directly removed, making data inaccessible. Data destruction can effectively protect the sensitive data and system security of unmanned vehicles, preventing malicious use.

[0178] The communication module realizes communication security by using multiple methods to prevent the interception of communication content. First, symmetric encryption (such as AES) and asymmetric encryption (such as RSA) algorithms are used to encrypt the communication content, ensuring data confidentiality. The AES encryption process will use the AES (Advanced Encryption Standard) high-strength symmetric encryption algorithm to divide the input plaintext into fixed-size blocks (128 bits) and generate ciphertext through multiple rounds of encryption operations. First, the plaintext is XORed with the initial key to obtain the encrypted initial state. Then, each byte is nonlinearly replaced using the S-Box through byte substitution; then, the rows of the state matrix are cyclically shifted through row shifting; then, in the column confusion step, each column of data is confused through matrix multiplication operations on a finite field. Finally, the key is XORed with the subkey again. The entire process is repeated for 10 rounds, and the final output is the encrypted ciphertext. Each round of operation fully utilizes linear and nonlinear transformations, making AES encryption highly secure. The AES decryption process is the inverse operation of encryption, which gradually restores the plaintext through inverse processing. First, the ciphertext block is subjected to the initial key round, and the ciphertext is XORed with the subkey of the last round. Then, perform inverse column confusion to perform inverse finite field matrix multiplication operations on each column of data; then, inverse row shifting moves the rows of the state matrix back to their original positions according to the shifting rules during encryption; then, in the inverse byte substitution, each byte is nonlinearly replaced using the inverse S-Box to restore the original state. Through repeated inverse processing of the above operations, the plaintext data is finally recovered. In the decryption process, strictly follow the inverse order of the encryption steps to ensure data consistency and integrity.

[0179] RSA is used as an asymmetric encryption algorithm in the communication module, which encrypts data using a public key and decrypts data using a private key. Specifically, first, generate a key pair, randomly generate two large prime numbers p and q, calculate n = p · q and the Euler function φ(n) = (p-1)(q-1), select the encryption exponent e such that 1 < e < φ(n) and gcd(e, φ(n)) = 1, and calculate the decryption exponent d that satisfies the following formula:

[0180] e·d≡1(modφ(n));

[0181] where the public key is (e, n) and the private key is (d, n);

[0182] The symmetric key K δ is encrypted using the public key pair as follows:

[0183]

[0184] where C k is the encrypted ciphertext;

[0185] Finally, the symmetric key is restored using the private key as follows:

[0186]

[0187] The communication module verifies the identity of both parties using a multi-factor authentication mechanism, such as digital certificates and biometrics, to prevent unauthorized access. At the same time, it ensures the integrity and authenticity of the communication content through hash functions and digital signature technology to prevent data tampering during transmission. SHA-256 is a one-way cryptographic hash function that maps input data of any length to a 256-bit hash value. First, the input data is padded to make its length an integer multiple of 512 bits. Then, a 256-bit hash buffer is initialized with eight 32-bit words of initial values. Then, for each 512-bit block, a series of bit operations, logical operations, and cyclic shift operations are performed to update the hash buffer value. Finally, the eight 32-bit words in the hash buffer are concatenated to form a 256-bit hash value as output. SHA-256 has properties such as one-wayness, collision resistance, and sensitivity, making it widely used in security fields such as data integrity verification and digital signature. Its one-wayness makes it difficult to reverse the original data from the hash value, collision resistance ensures that the probability of finding two different inputs with the same hash value is extremely low, and sensitivity ensures that any slight data change will cause the hash value to change significantly. ECDSA (Elliptic Curve Digital Signature Algorithm) is a digital signature algorithm based on elliptic curve cryptography, which uses the computational complexity of the elliptic curve discrete logarithm problem to ensure security. By using the hash algorithm SHA-256 on the message M to generate the hash value H(M), the private key d and the random number k are used to generate the signature pair (r, s), as shown in the following formula:

[0188] r = (kG) x mod n;

[0189] s = k -1 (H(M) + dr) mod n;

[0190] where G is the elliptic curve base point;

[0191] Use the public key Q = dG to verify the signature pair (r, s):

[0192] w = s -1 mod n;

[0193] μ1 = H(M)w mod n, μ2 = rw mod n;

[0194] (x, y) = μ1G + μ2Q;

[0195] If r ≡ x mod n, the signature is valid, otherwise the signature is invalid;

[0196] Finally, an encrypted communication tunnel is established using secure network protocols such as TLS / SSL and VPN, further preventing data from being intercepted and tampered with. Through these measures, the communication module effectively safeguards the communication security of the unmanned vehicle system, ensuring the confidentiality, integrity, and authenticity of data during transmission.

[0197] The comprehensive control module plays a core coordinating and managing role in the information security protection of the unmanned vehicle, responsible for the unified scheduling, policy management, and real-time response of the overall security architecture. It organically combines various security function modules of the unmanned vehicle (including the monitoring and early warning module, distributed protection module, data module, communication module, and self-destruction module) to form an efficient security closed loop. For the monitoring and early warning module, the comprehensive control module is responsible for aggregating threat intelligence, dynamically adjusting the monitoring range and frequency, and triggering response strategies when early warning signals occur. For the distributed protection module, it manages node state and strategy allocation, schedules defense resources to intercept threats in real time, and integrates protection logs to optimize defense strategies. For the data module, it is responsible for unified management of storage, encryption, and backup operations, ensuring data integrity and rapid recovery capabilities, while coordinating with the communication module to ensure data transmission security. For the communication module, it dynamically adjusts encryption strategies, authenticates the identities of both parties, and commands the communication module to suspend or switch communication channels in abnormal situations. For the self-destruction module, it intelligently decides the triggering conditions based on threat levels and destroys data and hardware when necessary to prevent core information leakage. Through the organic integration and precise scheduling of each module, the comprehensive control module ensures that the unmanned vehicle has the capabilities of rapid response, security protection, data management, and self-protection, achieving efficient, stable, and comprehensive information security protection in complex operating environments.

[0198] Figure 2The workflow diagram of the distributed information security protection device of the unmanned vehicle of the embodiment specifically comprises: the monitoring and early warning module uploads the data collected by the laser radar and the camera of the unmanned vehicle to a shared network in real time to realize global situation awareness, abnormality is monitored by processing the data through stream data analysis by means of an adaptive hybrid model machine learning algorithm combining deep learning and Bayesian inference. The identified threats are evaluated, data storage and secure communication are performed for non-dangerous threats; the threats are graded and evaluated, the security protection strategy is dynamically adjusted for low threats, and logs are recorded before secure communication; a warning signal is sent on the basis of the medium threats, surrounding vehicles and the command center are notified. The distributed protection module shares security information between nodes, is equipped with protection units such as firewalls, adopts a TLS two-way authentication protocol to guarantee communication security, and dynamically adjusts the protection strategy according to the threat situation. When facing high threats, the self-destruction module is started again. The self-destruction module detects threats internally and externally on the basis of the work of the monitoring and early warning module and the distributed protection module, is started when the comprehensive threat score exceeds a threshold value, an emergency response mechanism is started, data backup is performed in the data module and is transmitted to the cloud, data is destroyed by sending a high-voltage electric pulse to a storage chip, physically destroying the chip, data covering and the like. The data module is responsible for data storage and backup, encryption and decryption, permission management, integrity checking, redundancy fault tolerance and destruction, and data security is guaranteed by using multiple technologies. The communication module uses symmetric and asymmetric encryption algorithms, multi-factor identity authentication, hash functions and digital signature technology, combines a secure network protocol, and guarantees the confidentiality, integrity and authenticity of communication. The comprehensive control module unifies and coordinates each module, performs real-time monitoring, security strategy management and resource scheduling, forms an efficient and safe closed loop, and ensures that the unmanned vehicle realizes efficient, stable and comprehensive information security protection in a complex environment. Through the above process, the all-around information security protection of the unmanned vehicle is detailedly combed, and the safety and reliability of the system are significantly improved.

[0199] Therefore, the unmanned vehicle distributed information security protection device is adopted, the distributed protection module, the monitoring and early warning module, the self-destruction module and the communication module are combined, the all-around information security protection is provided for the unmanned vehicle system, and data leakage and malicious control of the system by others are prevented.

[0200] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application but not to limit them, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can still be modified or replaced by equivalents, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. An unmanned vehicle distributed information security protection device, characterized in that, Comprise: A monitoring and early warning module integrates multiple sensors to perceive the environment and monitor the state of the surrounding environment, and uses machine learning algorithms to identify and monitor the system state in real time, and to warn of abnormal operating behavior; A distributed protection module enables collaborative protection between modules, shares security information, and dynamically adjusts protection strategies; A self-destruction module rates threats when it detects uncontrollable threats, automatically triggers a self-destruction program, and destroys sensitive data on storage media; A data module includes a data storage unit, a data erasure unit, a data encryption and decryption unit, and a data redundancy unit; A communication module enables information transmission between unmanned vehicle nodes; An integrated control module organically combines the above modules for centralized command and resource allocation; In the distributed protection module, TLS two-way authentication protocol is used between nodes to protect the transmission of shared security information, dangerous identification data, and protection strategies. When a node detects a threat, the system adopts a dynamic protection strategy through a collaborative mechanism, which is: The system monitors the environment threats in real time through the sensor data of the unmanned vehicle, evaluates the threat score The calculation formula is as follows: ； wherein, a potential severity of the threat; a spatial proximity of the threat; a confidence level of the threat identification; , , are weight factors; According to the threat score, dynamically allocate the priority of each protection strategy to ensure that critical strategies are executed first, represented as: ； wherein, is a weight for the policy ; is a priority for the policy ; At the same time, according to the current resource state and policy priority, the resource is allocated to each protection policy, expressed as: ； wherein, is a resource assigned to the policy ; In the distributed protection module, it further comprises adjusting sensor parameters according to the threat score , specifically: Laser radar scanning frequency adjustment: ； wherein, is a laser radar scan frequency; is a maximum scan frequency of the laser radar; is an adjustment coefficient; Camera frame rate adjustment: ； wherein, is the camera frame rate; is the adjustment coefficient; At each time step Update the threat score and the protection policy, updating the threat score as follows: ； wherein, is a smoothing coefficient; is a measured threat score for the current time step; According to the new score, reassign priorities and resources, and start the self-destruction module for self-destruction when there is a high threat; The prerequisite for starting the self-destruction module is threat detection, including external threat detection and internal threat detection. External threat detection detects abnormal behavior through external laser radars and cameras in the monitoring and early warning module. Internal threat detection targets malicious tampering, brute force disassembly, or signal interference attacks. It uses an internal acceleration sensor to monitor the vehicle's internal state in real time, which is: Detect abnormal environments through acceleration sensors to monitor whether there are violent vibrations or disassembly behaviors: ； When a disassembly threat exists; wherein, is a three-axis acceleration component; is a shock threshold; By electronic screening signal strength detecting whether a communication band is interfered, if is threatened by signal interference, wherein, is a signal interference threshold value; Taking into account the above factors, the internal threat score is calculated : ； By threat score And internal threat score Comprehensive judgment whether to start self-destruction program; trigger mechanism uses logical judgment and preset threshold, and triggers self-destruction module when the comprehensive threat score exceeds the threshold: ； wherein, is a comprehensive threat score; , is a weight factor for balancing the impact of internal and external threats; When > the self-destruction module is started, a threat score threshold for triggering self-destruction; data destruction is achieved by starting the self-destruction module, and the data destruction mode is carried out through data covering, storage chip destruction, and key data encryption destruction. Multiple coverages are performed on storage media to remove data traces, using a pseudo-random number generator to generate coverage data: ； wherein, is the number of coverages, is the storage medium size; Send a high-voltage pulse to the storage chip to physically damage the chip, with the pulse formula as follows: ； wherein, is the maximum pulse voltage, is the pulse frequency.

2. The distributed information security protection device for unmanned vehicles according to claim 1, characterized in that, The monitoring and early warning module uploads data collected by the sensors of the unmanned vehicle to the shared network in real time to form a global situational awareness. The machine learning algorithm uses an adaptive hybrid model machine learning algorithm, which consists of a neural network for feature learning, a Bayesian inference for capturing the uncertainty of input data, and an adaptive mechanism for dynamically adjusting neural network parameters. The expression of the adaptive hybrid model is as follows: ； wherein, is input data; is output prediction; is latent variable; is model parameter; is current training data set; First, the input data is extracted by a neural network to obtain the feature and generate the latent variable distribution , and the feature extraction formula is ； where, is a forward propagation function of the neural network; are parameters of the neural network, then the uncertainty of the model parameters is captured by Bayesian inference, with the posterior distribution being: ​ ； wherein is a likelihood function of the data; is a prior distribution of the model parameters; The final output distribution prediction model has the following form: ； Real-time uploaded data is processed and analyzed through stream big data analysis, and abnormal states are monitored and identified.

3. The distributed information security protection device for unmanned vehicles of claim 2, wherein, Real-time uploaded data is processed and analyzed through stream big data analysis, and abnormal states are monitored and identified, including data stream processing framework, state monitoring model, and adaptive threshold adjustment mechanism, which are as follows: Firstly, the data stream is divided into pieces. Assuming that the data stream arrives in the form of time series, denoted as , where each is the data collected at time ; the data stream is divided into pieces by sliding window, as follows: ； wherein is time of the data window; is the data window size; At each data window The behavior pattern of the data within the data window is analyzed by the condition monitoring model, and if the data deviates from the normal behavior, it is identified as an abnormal condition. The state monitoring model specifically includes: Average: ; Standard deviation: ; Abnormality judgment: ; When wherein, is a set threshold value, the current point in time is considered to exhibit abnormal behavior; Adaptive adjustment threshold Thus, to adapt to the dynamic environment of unmanned vehicles, the following formula is set: ； wherein, and is a dynamic adjustment parameter; is the standard deviation of the current data window, for dynamically reflecting data changes; In combination with the state monitoring results within the data window, abnormal behaviors are identified and responses are generated, when the abnormality is marked, the system triggers the protection mechanism; when entering the next time step the data window is updated to and the above steps are repeated for continuous early warning monitoring.

4. The distributed information security protection device for unmanned vehicles of claim 1, wherein, Each unit of the data module is as follows: The data storage unit uses a distributed database for data storage, including a hierarchical storage strategy and a role-based access control mechanism; The data erasing unit provides logical erasing and physical erasing methods, which overwrite random data through multiple writing or use physical means of disk fragmentation, and configures an automatic erasing process; The data erasing operation is automatically performed when it is detected that the storage medium is no longer secure or the data is no longer needed; The data encryption and decryption unit uses the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm to encrypt and decrypt data, and integrates a TPM or HSM hardware encryption module, while configuring a secure key management system; The data redundancy unit adopts a multiple copy strategy and an off-site backup strategy, ensures data consistency and high availability through a distributed consistency algorithm, and sets an automatic recovery mechanism to automatically recover data from backup or redundant nodes when a data node failure is detected, ensuring the continuous availability of the system.

5. The distributed information security protection device for unmanned vehicles of claim 1, wherein, The communication module uses multiple methods to achieve communication security, including: First, symmetric encryption and asymmetric encryption algorithms are used to encrypt the communication content. The symmetric encryption process will use the Advanced Encryption Standard (AES) high-strength symmetric encryption algorithm to divide the input plaintext into fixed-size blocks, and generate ciphertext through multiple encryption operations. Specifically, first, the plaintext is XORed with the initial key to obtain the encrypted initial state. Then, each byte is nonlinearly replaced using the S-Box through byte substitution. Then, the rows of the state matrix are cyclically shifted. In the column confusion step, each column of data is confused through matrix multiplication over a finite field. Finally, the key is XORed with the sub-key again. The entire process is repeated for 10 rounds, and the encrypted ciphertext is finally output. The AES decryption process is the inverse operation of encryption, which gradually restores the plaintext through inverse processing. Specifically, first, the ciphertext block is subjected to initial key rounds, and the ciphertext is XORed with the last round of sub-key. Then, perform inverse column confusion to perform inverse finite field matrix multiplication on each column of data. Next, inverse row shifting moves the rows of the state matrix back to their original positions according to the shifting rules during encryption. Then, in the inverse byte substitution, each byte is nonlinearly replaced using the inverse S-Box to restore the original state. Through repeated operations, the plaintext data is finally recovered through inverse processing. RSA is used as an asymmetric encryption algorithm in the communication module, and the data is encrypted by the public key and decrypted by the private key. Specifically, a key pair is first generated, two large prime numbers are randomly generated and , the prime numbers are calculated and the Euler function , the encryption exponent is selected, so that and , the decryption exponent is calculated, and the following formula is satisfied: ； Wherein, the public key is , the private key ; Using a public key pair to a symmetric key Encryption, as shown by the following equation: ； wherein, is the encrypted ciphertext; Finally, the symmetric key is restored using the private key, as shown in the following formula: ； The communication module utilizes a multi-factor authentication mechanism to verify the identities of both communicating parties. Simultaneously, it employs hash functions and digital signature technology to ensure the integrity and authenticity of the communication content. SHA-256 is a one-way cryptographic hash function that maps input data of arbitrary length to a 256-bit hash value. Specifically: first, the input data is padded to make its length a multiple of 512 bits; then, a 256-bit hash buffer is initialized, containing eight 32-bit words as initial values; then, for each 512-bit block, a series of bitwise operations, logical operations, and circular shift operations are performed to update the hash buffer value; finally, the eight 32-bit words in the hash buffer are concatenated to form a 256-bit hash value as the output. ECDSA is a digital signature algorithm based on elliptic curve cryptography, which verifies the identity of both parties by analyzing the message... Generate hash values ​​using the SHA-256 hash algorithm. Using private key and random numbers Generate signature pairs As shown in the following formula: ； ； wherein is an elliptic curve base point; Using public keys Verifying a signature pair : ； mod n, ; ； If , the signature is valid, otherwise the signature is invalid. Finally, a secure network protocol is used to establish an encrypted communication tunnel.

Images