Fixed-Time Security State Estimation Method for Unmanned Surface Vessels Against Multi-Channel Hybrid Network Attacks

Abstract

This invention belongs to the interdisciplinary field of cyber-physical systems and intelligent ship network security control, specifically relating to a fixed-time security state estimation method for unmanned surface vessels to resist multi-channel hybrid network attacks. First, a dynamic data encryption and detection scheme is employed to achieve zero-latency, accurate detection and isolation of FDI attacks, effectively reducing the complex data tampering problem to a bounded data packet loss form. Then, a composite fixed-time security state estimation architecture is constructed, consisting of an initial run, a fixed-time period, and a real-time observer, to absorb joint communication interruptions caused by DoS and FDI attacks. This solves the problems of slow convergence and high dependence on the initial state in existing anti-attack strategies, significantly improving the reliability of security monitoring and the ability to rapidly reconstruct states in complex adversarial environments for cyber-physical systems.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of cyber-physical systems and intelligent ship network security control, specifically involving a fixed-time security state estimation method for unmanned surface vessels to resist multi-channel hybrid network attacks. Background Technology

[0002] As a typical representative of Cyber-Physical Systems (CPSs) in the field of automation, Unmanned Surface Vessels (USVs) heavily rely on multi-channel wireless communication networks to transmit massive amounts of multi-source sensor measurement data to a remote control center for fusion and processing in real time. However, during missions, USVs are constantly exposed to the highly dynamic and highly disruptive open ocean environment. The harsh and unpredictable external physical disturbances, such as wave fluctuations, complex ocean currents, and strong winds and gusts, require USVs to maintain high-precision real-time status for trajectory tracking and obstacle avoidance decisions. Even more challenging is the inherent physical constraint of long-distance wireless communication at sea, including severe signal attenuation, multipath effects, and limited bandwidth. This open and vulnerable communication architecture makes it highly susceptible to severe network threats. DoS attacks block channels by sending massive amounts of useless data packets, causing data loss; while FDI attacks disrupt status monitoring by covertly tampering with sensor data. In actual combat, systems often face the challenge of hybrid attacks triggered by the combined forces of DoS and FDI.

[0003] To address the aforementioned cyberattacks, existing technical solutions mainly focus on two aspects: anomaly detection and state reconstruction. For FDI anomaly detection, existing solutions often employ detection mechanisms based on information residual evaluation, online reconstruction, or neural network adaptive approximation. In addressing DoS data loss, existing state estimation schemes often employ information compensation, finite-time estimation, or asymptotically stable control algorithms.

[0004] However, these existing technologies have the following obvious drawbacks and shortcomings when dealing with multi-channel hybrid attacks: First, existing FDI detection mechanisms have enormous computational overhead, making them unsuitable for edge control nodes with limited computing power. More critically, traditional residual detectors heavily rely on continuous-time integration of system dynamics models or multi-step discrete iterations of historical data to calculate statistical thresholds. When the system encounters a hybrid attack (i.e., a DoS attack causing frequent data stream interruptions), the continuous input data stream is disrupted, and traditional detection mechanisms are highly susceptible to failure, resulting in extremely high false negative and false positive rates, making it impossible to achieve real-time proactive isolation of malicious data.

[0005] Second, existing state estimation algorithms cannot provide deterministic, rapid recovery capabilities. Traditional asymptotically stable algorithms have long convergence times when eliminating errors; while the upper limit of convergence time in finite-time estimation theory is highly dependent on the initial state error of the system. In network attack scenarios, due to prolonged communication loss, the initial error of the system is often unknown and potentially very large, causing existing finite-time theories to fail. Therefore, current technologies cannot provide an absolutely certain and pre-set fixed time upper limit for state estimation and error elimination without relying on the initial state of the system, making it difficult to meet the engineering requirements for precise recovery time after malicious blocking. Summary of the Invention

[0006] The purpose of this invention is to overcome the problems of existing technologies when facing multi-channel DoS and FDI mixed attacks, such as the high computational overhead of FDI anomaly detection mechanisms, the high false alarm rate and high false alarm rate due to communication interruptions, and the slow convergence of traditional state estimation algorithms, which are highly dependent on the initial state of the system and cannot provide deterministic and rapid recovery time. This invention proposes a fixed-time security state estimation method for unmanned surface vessels to resist multi-channel mixed network attacks.

[0007] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: This invention provides a fixed-time security state estimation method for unmanned surface vessels to resist multi-channel hybrid network attacks, comprising the following steps: Step 1: Acquire multi-channel measurement data at the sensor transmitter of the USV, dynamically encrypt the multi-channel measurement data, and package it into a data packet to send to the communication network; Step 2: Decrypt the received data packets and detect FDI attacks at the receiving end; Step 3: Construct a unified discontinuous communication model to address hybrid network threats, and design a composite security state estimation architecture that includes an initial running observer, a fixed-time observer, and a real-time observer; Step 4: Establish a sliding window based on the latest recorded secure communication time, and use the amount of secure data accumulated within the sliding window to alternate between the initial running observer, fixed-time observer, and real-time observer mechanisms.

[0008] Further, in step 1, multi-channel measurement data is acquired at the sensor transmitter of the USV, and the multi-channel measurement data is dynamically encrypted, including: At the sensor transmitter of the USV, multi-channel measurement data will be transmitted. Quantized into a binary digital signal; encrypted using a dynamically random key sequence synchronized with the receiver to generate the dynamic key for the current moment. ; XOR logic is used to encrypt multi-channel measurement data to generate the master ciphertext for the sending end. Auxiliary ciphertext from the sender : ; in, This represents a bitwise XOR logical operation. Encryption functions for physical implantation.

[0009] Furthermore, in step 1, packaging the data into a data packet and sending it to the communication network includes: adopting a sliding window packet splitting strategy to package the main ciphertext of the sending end and the auxiliary ciphertext of the sending end into a data packet and sending it to the communication network.

[0010] Further, step 2 includes: After receiving a data packet, the receiving end synchronously generates a receiving end key, uses the receiving end key to decrypt the data packet, and generates an auxiliary decryption value and a decrypted main ciphertext. Perform an XOR operation on the decrypted main ciphertext and the auxiliary decryption value, and determine whether the XOR result is equal to the receiving key. If not, it is determined that the channel has been tampered with by FDI at the current moment, and the data packet is directly and actively discarded, which is recorded as a communication interruption.

[0011] Furthermore, the scenarios in which FDI tampering occurs at the present moment include: the attacker only modifying the main ciphertext, the attacker only modifying the auxiliary ciphertext, and the attacker modifying both the main ciphertext and the auxiliary ciphertext simultaneously.

[0012] Furthermore, in step 3, the composite security state estimation architecture includes: A state machine based on a sliding window adaptive evaluation mechanism is constructed, and three differentiated observers designed for different network conditions are organically integrated. The three differentiated observers include an initial running observer, a real-time observer, and a fixed-time observer.

[0013] Furthermore, step 4 includes: When the amount of data accumulated by the secure sliding window is insufficient, the system relies on the initial running observer or the real-time observer to work alternately to maintain basic state feedback and stable open-loop prediction during initial startup or when facing continuous intermittent attacks. When the amount of data accumulated within the safe sliding window meets the preset length, the fixed-time observer is started, and a one-time state calculation is performed using the historical cumulative gain matrix. The initial estimation error is then cleared to zero within the preset fixed time.

[0014] Compared with the prior art, the present invention has the following technical effects: (1) This invention proposes a new paradigm for zero-latency proactive dimensionality reduction defense against fake data based on cryptographic logic. Addressing the weakness of traditional residual detection, which is prone to false alarms and breakdowns when communication is interrupted by DoS attacks, this invention introduces lightweight dynamic XOR encryption into the sensor's underlying layer. By completely replacing complex dynamic integration with single-step algebraic logic verification, zero-latency precise isolation from FDI attacks is achieved with extremely low computational overhead. Furthermore, it cleverly reduces the dimensionality of the attack to a bounded communication packet loss problem, reconstructing a clean defense data stream.

[0015] (2) This invention constructs an adaptive discontinuous communication architecture with a fixed-time, high-speed reconfiguration capability that is independent of initial state. Addressing the shortcomings of traditional asymptotic algorithms, which have slow convergence, and finite-time algorithms, which heavily rely on initial errors, this invention proposes a composite observer architecture that alternates between IR / FT / RT stages. Through an algebraic cancellation mechanism of the historical gain matrix, the system can instantly clear accumulated divergence errors within a pre-set fixed time step, completely independent of initial unknown errors, under high-frequency mixed blocking environments, providing unmanned systems with a high-speed state feedback guarantee with strict time boundaries. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the fixed-time security state estimation method for unmanned surface vessels against multi-channel hybrid network attacks, as described in this invention. Figure 2 This is a system architecture diagram of the application of the security state estimation established in the present invention in the USV. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the technical solutions proposed according to the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. Specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] This study uses an unmanned surface vessel (USV) performing a surface tracking mission as a specific application of cyber-physical systems. A heterogeneous cluster of sensors, through Each independent wireless communication channel transmits measurement data to the remote control center. The discrete linear state-space model of the unmanned surface vessel (USV) system is defined as follows: (1); in, In time step k When +1, the system state vector containing information such as position and velocity is called the unmanned surface vessel system state equation. In time step k At that time, it is a system state vector containing information such as position and velocity; For multi-channel measurement output vectors; External disturbances to the unknown marine environment; Measure noise for sensors; matrix It is a known parameter matrix of the corresponding dimension. During transmission, the communication channel faces unknown, aperiodic DoS blocking and FDI tampering mixed attacks.

[0020] In this embodiment, a fixed-time security state estimation method for unmanned surface vessels to resist multi-channel hybrid network attacks is provided, including the following steps: Step 1: Acquire multi-channel measurement data at the sensor transmitter of the USV, dynamically encrypt the multi-channel measurement data, and package it into a data packet to send to the communication network; Step 2: Decrypt the received data packets and detect FDI attacks at the receiving end; Step 3: Construct a unified discontinuous communication model to address hybrid network threats, and design a composite security state estimation architecture that includes an initial running observer, a fixed-time observer, and a real-time observer; Step 4: Establish a sliding window based on the latest recorded secure communication time, and use the amount of secure data accumulated within the sliding window to alternate between the initial running observer, fixed-time observer, and real-time observer mechanisms.

[0021] The following is a detailed explanation of each of the above steps: Step 1: The multi-channel measurement data acquired at the sensor transmitter of the USV is dynamically encrypted before being sent to the communication network to protect the privacy of the sensor measurement data and prevent attackers from directly stealing real physical state information in open channels.

[0022] As an example, step 1 specifically includes: Step 1.1: Dynamic Encryption: Acquire multi-channel measurement data at the sensor transmitter of the USV and perform dynamic encryption processing on the multi-channel measurement data.

[0023] At the sensor transmitter of the USV, the acquired local multi-channel measurement data will be... The signal is quantized into a binary digital signal. Encryption is performed using a dynamically random key sequence synchronized with the receiver. Encryption utilizes a pre-defined pseudo-random sequence generator to generate the dynamic key for the current moment. , For at any time k Used for the first i A dynamic key for encrypting data in each channel. To generate a pseudo-random sequence at time 10:00. k Generated, used for the i Each channel generates a pseudo-random sequence value encrypted with its own seed. The pseudo-random sequence generator is initialized using the same seed as the receiver to ensure synchronization between the two parties.

[0024] XOR logic is used to process multi-channel measurement data. Encryption is performed to generate the master ciphertext for the sender. Auxiliary ciphertext from the sender : (2); in, This represents a bitwise XOR logical operation. Encryption functions are physically embedded. Utilizing the homomorphic mapping properties of XOR operations and random keys, lightweight dynamic encryption is provided for underlying digital signals without requiring massive computing power.

[0025] Step 1.2: Adopt a sliding window packetization strategy to package the encrypted data into data packets and send them to the communication network.

[0026] To combat disconnections caused by DoS attacks, set the sliding window length. .

[0027] in, The minimum number of history steps required by the observer. This represents the maximum number of consecutive packet loss steps that an unmanned surface vessel system can tolerate.

[0028] After setting the sliding window length, package the current and historical main ciphertext and auxiliary ciphertext into a data packet. And send it to the communication network.

[0029] Step 2: At the receiving end, the received data packets are decrypted and FDI attack detected. The tampered data packets are actively isolated and reduced to the equivalent of DoS data packet loss. This completely eliminates the defect of traditional residual detection relying on the continuity of historical data. At the logical level, the complex FDI data tampering problem is cleverly equivalent to a simple, bounded communication disconnection problem.

[0030] As an example, step 2 specifically includes: Step 2.1: Synchronous decryption: The receiving end uses the synchronization key to decrypt the data packet, generating an auxiliary decryption value and the decrypted main ciphertext.

[0031] After receiving the data packet, the remote control center (receiving end) synchronously generates a receiving end key. , It is a key generated synchronously by the remote control center and is the same as the key sent by the sending end.

[0032] Perform decryption using the receiver's key: (3); in, It is to decrypt the master ciphertext; This is an auxiliary decryption value.

[0033] In the absence of cyberattacks, dynamic data encryption schemes satisfy... and According to the mathematical expression of the decryption process (3), we can obtain: (4); Because both communicating parties are configured with the same random sequence, i.e. ,but Substituting into the above formula, we get: (5); Step 2.2: Algebraic Logic Verification and Dimensionality Reduction: Perform an XOR operation on the decrypted main ciphertext and the auxiliary decryption value, replacing the complex statistical dynamics integration with algebraic logic verification; determine whether the XOR result is equal to the current key. If not, it is determined that the channel has been tampered with by FDI at the current moment, and the data packet is directly and actively discarded, recorded as a communication interruption. With extremely low computational overhead per step, zero-latency, false alarm-free detection and active isolation of FDI attacks are achieved.

[0034] Determine the XOR equation Check if the equation holds true. If it does, then the data is deemed secure, and the secure communication time is updated. If the equation does not hold true (indicating FDI tampering), a zero-delay alarm is triggered, and the current data packets for that channel are actively discarded, with the communication status recorded as interrupted. ).

[0035] In the absence of an FDI attack, by utilizing the cancellation property of identical terms in the XOR operation, both sides of the second row of the formula in (5) are XORed simultaneously. It can be calculated that: (6); When a data packet is subjected to an FDI attack during channel transmission, assuming the attacker injects a non-zero attack signal... and Their attack strategies mainly include the following three situations: 1) Scenario 1: Attacker only modifies the master ciphertext The received modified master ciphertext is as follows: (7); In the above formula, This is the modified master ciphertext.

[0036] Deciphered as: ,in, To decrypt the modified master ciphertext value; the auxiliary decryption value is... .

[0037] because , Necessarily does not equal .

[0038] 2) Scenario 2: Attacker only modifies auxiliary ciphertext The received modified auxiliary ciphertext is as follows: (8); In the above formula, This is the modified auxiliary ciphertext.

[0039] Deciphered as: The auxiliary decryption value is , This is used to decrypt the modified auxiliary decryption value.

[0040] because , Necessarily does not equal .

[0041] 3) Scenario 3: Attacker simultaneously modifies the master ciphertext and auxiliary ciphertext The received ciphertext is as follows: (9); Because the XOR operation has highly non-linear obfuscation properties, and attackers cannot obtain the dynamic key. (Right now And the current real-world status of unmanned surface vessel systems. Lacking this crucial prior information, the attacker constructed matching... and Make the above expression strictly equal to The probability of this is close to zero in a cryptographic sense. Therefore, It must be true.

[0042] After detection, the complex FDI tampering is reduced to the equivalent of DoS packet loss.

[0043] Step 3: Construct a unified discontinuous communication model to address hybrid network threats, and design a composite security state estimation architecture (Fixed-time Secure State Estimation, FTSSE) that includes an InitialRunning (IR) observer, a Fixed-time (FT) observer, and a Real-time (RT) observer.

[0044] As an example, step 3 specifically includes: Step 3.1: Construct the decoupling matrix: To eliminate unknown disturbances from the physical environment, an orthogonal projection matrix is ​​defined inside the observer. Because it satisfies Therefore, noise measurement Eliminated: ; In the above formula, It is an orthogonal projection matrix; For safe measurement data; This is the system state vector; It is the identity matrix; for The pseudo-inverse matrix.

[0045] Step 3.2: Equivalent to unmanned surface vessel system: Based on the equations and decoupling matrix of the unmanned surface vessel system, construct a unified discontinuous communication model to cope with hybrid network threats.

[0046] Because in Safety measurement data As is known, based on the decoupling mechanism between the unmanned surface vessel system equations and the unknown input, the state can be rewritten as follows: (10); in, The current moment; The moment when the secure data packet is received; This is the system state vector from the previous moment; This refers to the unknown external disturbance from the previous moment; The interference cancellation matrix is ​​expressed as follows: ; The equivalent system state matrix is ​​expressed as follows: .because The definition of a matrix can be derived from this. This implies an unknown disturbance from the previous moment. It was offset.

[0047] Step 3.3: Design a composite security state estimation architecture that includes an initial running observer, a fixed-time observer, and a real-time observer.

[0048] The essence of the composite security state estimation architecture is to organically integrate three differentiated mathematical solvers (IR observer, FT observer, and RT observer) designed for different network conditions by constructing a "state machine" based on a sliding window adaptive evaluation mechanism.

[0049] The underlying mathematical design of the architecture: Within the observer architecture, an orthogonal projection matrix is ​​first defined to eliminate measurement noise, and an interference cancellation matrix is ​​constructed through algebraic substitution, thereby eliminating unknown external disturbances. Through this step, the complex system originally containing multiple unknown physical disturbances is equivalently transformed into a pure, perturbation-free evolution model, providing a unified mathematical foundation for the stable operation of the subsequent three observers.

[0050] The architecture's triggering and scheduling mechanism is designed as follows: A sliding window is used as the benchmark based on the latest secure communication time to count the number of continuously accumulated secure data packets within the window in real time. This accumulated secure data serves as a "scheduling instruction" within the composite architecture, dynamically controlling the adaptive switching of the three observers.

[0051] The design logic of Component A (IR Observer) is as follows: It is designed for situations where communication has just been restored and the amount of data accumulated in the safe sliding window is insufficient. In this case, the architecture schedules the IR observer to perform preliminary feedback correction using a limited number of historical safe data packets. The core purpose is to suppress the unbounded divergence of the initial error of the system during the disconnection period.

[0052] The core design logic of Scenario B (FT observer) is as follows: It is designed for cases where the secure data stream is continuous and the secure data within the window meets the preset length. In this case, the architecture triggers the FT observer, which uses the designed fixed-time gain matrix to extract the final state in one go, and multiplies the initial estimation error to zero within a preset fixed time, achieving extremely fast convergence without initial state dependence.

[0053] The design logic of Component C (RT predictor) is as follows: It is designed for situations where the system encounters a real DoS blockade or where FDI is actively isolated, resulting in no safe data updates. In this case, the architecture strips away measurement feedback and relies solely on open-loop dynamics for dynamic prediction. Since the FT observer has been forced to zero error in the previous stage, this predictor can maintain low-error state feedback for a long time due to inertia.

[0054] Step 4: Establish a sliding window based on the latest recorded secure communication time, and use the amount of secure data accumulated within the sliding window to alternate between the initial running observer, fixed-time observer, and real-time observer mechanisms.

[0055] A sliding window is established based on the latest recorded secure communication time, utilizing the amount of secure data accumulated within the window. It alternates between three observer mechanisms: (11); In the above formula, Security data accumulated within the window; The length of the sliding window.

[0056] Scenario A (Initial Operation Phase): When the unmanned surface vessel system successfully receives data ( However, the amount of data accumulated within the safety window is insufficient. When IR observer is executed, it utilizes a limited set of historical data packets. Perform preliminary feedback correction to suppress the divergence of the system's initial error: (12); Predict the state up to the current time: .

[0057] in, In the most recent secure communication moment The system state estimation vector; The state transition matrix of the closed-loop equivalent system with observer feedback gain. of The power represents the closed-loop dynamic evolution process in which the observer relies on historical data for feedback correction in the initial stage; For the system at the current moment The final state estimation vector; The discrete state transition matrix of the original system of The power is used to characterize the transition relationship of forward state step deduction based entirely on the open-loop dynamics of the physical system itself during the window period when there is no security data update. For any given initial state estimate; Indicates no attack A valid data packet containing historical measurement data received at any given time; This is the feedback gain matrix for the historical measurement data of the initial-run observer. This matrix is ​​used during the initial running phase, when data accumulation within the safe sliding window is insufficient, to weight the received limited number of valid historical data packets, thereby achieving preliminary feedback correction and divergence suppression of the system's initial state error. ; ; in, The gain matrix to be designed is configured by pole placement; This is the closed-loop equivalent system state transition matrix for the initial running observer; This is the closed-loop state transition transformation matrix for a specific dimension during the initial running phase.

[0058] Scenario B (Fixed-Time Convergence Phase): When the unmanned surface vessel system successfully receives data ( And the security data within the window meets the requirements ( ), When ), the FT observer is triggered.

[0059] Firstly, based on the diagonal block matrix inside the FT observer... continuous iteration Second-rate: (13); In the above formula, For the fixed-time observer at the time of the most recent secure communication The internal intermediate augmented state vector; For the diagonal block matrix inside the fixed-time observer of The power term represents the closed-loop dynamic evolution characteristics of the system within a preset fixed time window; The unknown initial value of the internal intermediate augmented state vector; This is the historical measurement data feedback gain matrix of the fixed-time observer, used for algebraic weighted calculation of continuous historical security data packets that meet a fixed window length; This is the set of safety history measurement data successfully accumulated by a fixed-time observer within a sliding window.

[0060] Then, the designed fixed-time gain matrix was utilized. Extract the final state: (14); in, ; For the state reconstruction transformation matrix in the fixed-time observer, for An identity matrix of 3D; This is the first feedback gain matrix to be designed in the fixed-time observer, used to configure the dynamic convergence characteristics of the internal observer; The second feedback gain matrix to be designed in the fixed-time observer is... Coordinated configuration to ensure internal augmentation matrix Satisfy the preset Algebraic conditions for achieving extremely rapid convergence within a sliding window. and This is the gain matrix to be designed. Because the matrix... Due to the special algebraic properties of this step, the unknown initial error can be multiplied to zero in one step, ensuring that the system operates within a preset fixed time step (at the latest within...). Within a certain time frame, the error is strictly limited to a minimum bound, achieving extremely fast convergence without initial state dependence.

[0061] Scenario C (Intermittent Communication Prediction Phase): When the system encounters physical DoS blocking or data is actively isolated in step 2.2 above, resulting in no secure data updates ( ), execute the RT observer, and rely on open-loop dynamics for dynamic prediction: (15); Because the initial error of the FT observer was cleared in the previous stage, the divergence of the prediction error was greatly suppressed during this disconnection period.

[0062] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A fixed-time security state estimation method for unmanned surface vessels to resist multi-channel hybrid network attacks, characterized in that, Includes the following steps: Step 1: Acquire multi-channel measurement data at the sensor transmitter of the USV, dynamically encrypt the multi-channel measurement data, and package it into a data packet to send to the communication network; Step 2: Decrypt the received data packets and detect FDI attacks at the receiving end; Step 3: Construct a unified discontinuous communication model to address hybrid network threats, and design a composite security state estimation architecture that includes an initial running observer, a fixed-time observer, and a real-time observer; Step 4: Establish a sliding window based on the latest recorded secure communication time, and use the amount of secure data accumulated within the sliding window to alternate between the initial running observer, fixed-time observer, and real-time observer mechanisms.

2. The fixed-time security state estimation method for unmanned surface vessels against multi-channel hybrid network attacks according to claim 1, characterized in that, In step 1, multi-channel measurement data is acquired at the sensor transmitter of the USV, and the multi-channel measurement data is dynamically encrypted, including: At the sensor transmitter of the USV, multi-channel measurement data will be transmitted. Quantized into a binary digital signal; encrypted using a dynamically random key sequence synchronized with the receiver to generate the dynamic key for the current moment. ; XOR logic is used to process multi-channel measurement data. Encryption is performed to generate the master ciphertext for the sender. Auxiliary ciphertext from the sender : ； in, This represents a bitwise XOR logical operation. Encryption functions for physical implantation.

3. The fixed-time security state estimation method for unmanned surface vessels against multi-channel hybrid network attacks according to claim 2, characterized in that, In step 1, packaging the data into a data packet and sending it to the communication network includes: adopting a sliding window packet splitting strategy to package the main ciphertext of the sending end and the auxiliary ciphertext of the sending end into a data packet and sending it to the communication network.

4. The fixed-time security state estimation method for unmanned surface vessels against multi-channel hybrid network attacks according to claim 1, characterized in that, Step 2 includes: After receiving a data packet, the receiving end synchronously generates a receiving end key, uses the receiving end key to decrypt the data packet, and generates an auxiliary decryption value and a decrypted main ciphertext. Perform an XOR operation on the decrypted main ciphertext and the auxiliary decryption value, and determine whether the XOR result is equal to the receiving key. If not, it is determined that the channel has been tampered with by FDI at the current moment, and the data packet is directly and actively discarded, which is recorded as a communication interruption.

5. The fixed-time security state estimation method for unmanned surface vessels against multi-channel hybrid network attacks according to claim 4, characterized in that, The current scenarios in which FDI has tampered with the ciphertext include: the attacker only modifying the main ciphertext, the attacker only modifying the auxiliary ciphertext, and the attacker modifying both the main ciphertext and the auxiliary ciphertext simultaneously.

6. The fixed-time security state estimation method for unmanned surface vessels against multi-channel hybrid network attacks according to claim 1, characterized in that, Step 3 includes: A decoupling matrix is ​​constructed, and a unified discontinuous communication model for dealing with hybrid network threats is built based on the unmanned surface vessel system equations and the decoupling matrix.

7. The fixed-time security state estimation method for unmanned surface vessels against multi-channel hybrid network attacks according to claim 6, characterized in that, In step 3, the composite security state estimation architecture includes: A state machine based on a sliding window adaptive evaluation mechanism is constructed, and three differentiated observers designed for different network conditions are organically integrated. The three differentiated observers include an initial running observer, a real-time observer, and a fixed-time observer.

8. The fixed-time security state estimation method for unmanned surface vessels against multi-channel hybrid network attacks according to claim 1, characterized in that, Step 4 includes: When the amount of data accumulated by the secure sliding window is insufficient, the system relies on the initial running observer or the real-time observer to work alternately to maintain basic state feedback and stable open-loop prediction during initial startup or when facing continuous intermittent attacks. When the amount of data accumulated within the safe sliding window meets the preset length, the fixed-time observer is started, and a one-time state calculation is performed using the historical cumulative gain matrix. The initial estimation error is then cleared to zero within the preset fixed time.

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