Attack detection and security defense control method for cluster unmanned system under network attack

Abstract

The application provides a kind of attack detection and security defense control method of cluster unmanned system under network attack, attack detection algorithm and security defense strategy are designed respectively, under the monitoring of attack detection strategy, when system suffers malicious attack, the control input signal destroyed will be replaced into reliable control input signal, defense strategy is realized based on virtual network layer, physical state variable is not exchanged in actual, only through virtual node and neighbor node in virtual network layer exchange information, obtain reliable control input, realize the safe tracking control of cluster unmanned system under network attack;That is to say, the security defense strategy of the application is based on the communication mechanism of virtual variable in virtual network layer, aims to ensure that follower can safely and effectively track leader under unknown attack condition, the strategy can provide reliable control input signal in time when detecting attack, so as to significantly improve the attack resistance of cluster unmanned system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of cluster unmanned system, and particularly relates to an attack detection and security defense control method for a cluster unmanned system under network attack. BACKGROUND

[0002] In recent years, cluster unmanned systems have attracted widespread attention due to their efficient coordination capabilities in complex environments. Through distributed information processing and cooperative control, these systems can effectively monitor and manage large-scale distributed networks of unmanned aerial vehicles, unmanned vehicles and other devices. However, as the application scenarios continue to expand, how to ensure the security and consistency control of the system in the face of network attacks has become a core problem that needs to be solved.

[0003] Traditional consensus protocols can achieve information sharing and decision consistency through network communication information between neighbors of the unmanned system to some extent, but in the face of network attacks, especially false data injection attacks, the effectiveness and reliability of these protocols are severely challenged. The vulnerability of network communication enables attackers to interfere with control inputs, leading to a decline in system performance or even failure. Existing consensus control methods often have shortcomings in the face of such attacks. Therefore, how to design an attack detection strategy based on a linear function observer and design a reliable security defense strategy to improve the resistance of the cluster unmanned system to network attacks is a problem that needs to be solved. SUMMARY

[0004] To solve the above problems, the application provides an attack detection and security defense control method for a cluster unmanned system under network attack, which can realize safe tracking control of the cluster unmanned system under network attack and improve the security and stability of the cluster unmanned system to network attacks.

[0005] An attack detection and security defense control method for a cluster unmanned system under network attack, according to a set of attack detection criteria, determines whether each follower is attacked. For the follower whose determination result is no, the control signal u i (t) is used to track the state of the leader, and for the follower whose determination result is yes, the control signal is used to track the state of the leader, wherein i=1, 2, …, N, N represents the number of followers in the cluster unmanned system, and δ i (t) represents a false data injection attack signal on the i-th follower. At the same time, when each tracking follower tracks the state of the leader, a virtual variable is used to exchange the state information of each other in the virtual network layer, is the control signal of the i-th follower obtained according to the exchanged state information of each other in the virtual network layer, and x i(t) represents the physical state of the i-th follower, k 1,i 、k 2,i With k 3,i Both represent coefficients, v i and ω i Both represent the dummy variables used by the i-th follower in the virtual network layer.

[0006] Furthermore, the method for determining whether any follower is under attack according to the set attack detection criteria is as follows:

[0007] Step 1: Define the initial values ​​of the variables for attack detection criteria as follows:

[0008]

[0009] Among them, i represents the attack detection metric corresponding to the i-th follower, α i To represent T c Within the cycle i Reaching the measurement threshold Ψ i,max The cumulative variable of the number of times, s(t) is used to record and judge the attack detection metric Ψ i Whether the cumulative calculation time exceeds the first time period T o The clock function, r(t) is used to record and judge the cumulative variable α i Whether the cumulative calculation time exceeds the second time period T c The clock function of

[0010] Step 2: Continuously calculate the attack detection metric Ψ according to the set time step i as follows:

[0011]

[0012] in, is the actual input control signal u i The estimated value of (t), μ i is the integral weight corresponding to the i-th follower, τ is the integral variable;

[0013] Step 3: Determine the currently calculated attack detection metric Ψ i Is it greater than the measurement threshold Ψ i,max And whether s(t) exceeds the first time period T o If the judgment results are all negative, then continue to execute steps 2 to 3. i Not greater than the measurement threshold Ψ i,max And s(t) exceeds the first time period T o , will i After resetting s(t) to 0, re-execute steps 2 to 3. If Ψi Greater than the measurement threshold Ψ i,max , then α i Add 1 and go to step S4;

[0014] Step 4: Determine α i Whether it exceeds the cumulative threshold α i,max And whether r(t) exceeds the second time period T c If the judgment results are all negative, then continue to execute steps 2 to 4. If α i Does not exceed the cumulative threshold α i,max And r(t) exceeds the second time period T c , then Ψ i , s(t), r(t) and α i After resetting to 0, execute steps 2 to 4 again. If α i Exceeding the cumulative threshold α i,max And r(t) does not exceed the second time period T c , it means the current follower is under attack.

[0015] Furthermore, the dynamic model of the leader of the swarm unmanned system is as follows:

[0016]

[0017] Among them, x0(t) represents the physical state of the leader, y0(t) represents the output of the leader's dynamic model, represents the first-order derivative of x0(t), A represents the state coefficient matrix, and C represents the output coefficient matrix.

[0018] Furthermore, the dynamic model of each follower in the swarm unmanned system is as follows:

[0019]

[0020] Among them, x i (t) represents the physical state of the i-th follower, u i (t) represents the actual control input signal of the ith follower, y i (t) represents the output of the dynamic model of the i-th follower, Represents x i (t), A represents the state coefficient matrix of the follower dynamics model, B represents the input coefficient matrix of the follower dynamics model, C represents the output coefficient matrix of the follower dynamics model, and matrices A, B and C are known.

[0021] Furthermore, the actual input control signal u of any follower i Estimated value of (t) The method to obtain is as follows:

[0022] A linear function observer system for estimating the control input signal is constructed for each follower of the cluster unmanned system as follows:

[0023]

[0024] wherein, represents an estimated value of the control signal of the actual input of each follower represents a compact form of (·) T represents a transpose of ·, represents a compact form of the control signal u1(t)~u N (t) of the actual input of each follower, represents a compact form of the observer state w1(t)~w N (t) of each follower, represents a first derivative of w(t), T, F, J and P represent linear function observer system matrices selected according to stability of the linear function observer system, respectively, the matrix S is a Hermitian matrix, and the linear function observer system matrices satisfy the following equation:

[0025]

[0026] wherein, A' represents a compact form of the state coefficient matrix A, the symbol represents a Kronecker product, I N represents an N-order unit matrix, B' represents a compact form of the input coefficient matrix B, C' represents a compact form of the output coefficient matrix C, K' represents a compact form of the first feedback gain matrix K, H' represents a compact form of the second feedback gain matrix H, is a Laplace matrix, Δ is a diagonal matrix, represents an estimated value of the compact form w(t) of the observer state, L is an error coefficient matrix, represents a compact form of the tracking error e1(t)~e N (t) of each follower, and the tracking error corresponding to any one follower is expressed as e i (t) = x i (t) - x0(t);

[0027] An estimated value of the control signal u i (t) of the actual input of each follower is obtained using the linear function observer system

[0028] Further, the Laplacian matrix The weight coefficient a of the communication network ij ∈{0,1}, and a ij =1 indicates that the i-th follower has a communication flow with the j-th follower, otherwise no communication flow, a i0 =1 indicates that the i-th follower has a communication flow with the leader, otherwise no communication flow, and the diagonal matrix Δ = diag{a 10 ,...,a N0}.

[0029] Further, the method for obtaining the actual input control signal u i (t) of any one follower is as follows:

[0030]

[0031] u i (t) = Ku' i (t)

[0032] Wherein, u' i (t) is an intermediate control signal.

[0033] Further, the variable dynamics equation in the virtual network layer corresponding to any one follower is as follows:

[0034]

[0035] Wherein, v i , ω i and θ i all represent three virtual variables corresponding to the i-th follower, v j , ω j and θ j all represent three virtual variables corresponding to the i-th follower, T vi is the coefficient corresponding to the virtual variable v i , T θi is the coefficient corresponding to the virtual variable θ i , T ωi is the coefficient corresponding to the virtual variable ω i , η i represents the weight coefficient, γ i,j ∈{0,1}, γ i,j and γ j,i are the weight coefficients of the topology between any two followers in the virtual network layer, γ i0 is the proportional coefficient of the topology between the follower and the leader in the virtual network layer, k is the sum weight of the topology between any two followers, and β is the proportional coefficient of the topology between the follower and the leader.

[0036] Beneficial effects:

[0037] 1. The application provides a network attack under the cluster unmanned system attack detection and security defense control method, attack detection algorithm and security defense strategy are designed respectively, under the monitoring of attack detection strategy, when the system is attacked maliciously, the damaged control input signal will be replaced into reliable control input signal, the defense strategy is realized based on virtual network layer, physical state variable is not exchanged in actual, only through the information exchange of virtual node and neighbor node in virtual network layer, reliable control input is obtained, the safe tracking control of cluster unmanned system under network attack is realized; That is to say, the security defense strategy of the application is based on the communication mechanism of virtual variable in virtual network layer, aiming at ensuring that the follower can safely and effectively track the leader under unknown attack condition, the strategy can provide reliable control input signal in time when detecting attack, so as to significantly improve the attack resistance of cluster unmanned system.

[0038] 2. The application provides a network attack under the cluster unmanned system attack detection and security defense control method, linear function observers for estimating the control input signal u i (t) of each follower of cluster unmanned system are constructed for each follower of cluster unmanned system, then attack detection criterion Ψ i is constructed, and according to the comparison of Ψ i and its threshold value Ψ i,max , variable α i is used to represent the number of accumulative times exceeding the threshold value, finally, according to α i reaching its threshold value α i,max moment, it represents that the system is under attack at this time, so that false alarm can be avoided, and then the control input signal is switched from u i (t) to reliable input signal , so as to realize the tracking of each follower to leader x0(t); As can be seen, the application can realize the safe tracking control of cluster unmanned system under network attack, and improve the security and invulnerability of the system to network attack.

[0039] 3. The application provides a network attack under the cluster unmanned system attack detection and security defense control method, a virtual network layer control framework is provided, wherein v i , ω i and θ i are designed in virtual network layer, the virtual variables have no physical meaning, exchange information with neighbor virtual node and the i-th unmanned system in physical layer in virtual network layer, through the design, each unmanned system can switch the damaged control input signal in time, so as to realize the flexibility and attack resistance of the system.

[0040] 4. The application provides a network attack cluster unmanned system attack detection and security defense control method, the attack detection strategy is based on a linear function observer system, has a simple observer structure, can compare the estimated control input signal with the actual input signal, detect the existence of attack signals in time, and can enhance the security of the cluster unmanned system when encountering false data injection attacks. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 The application provides a network attack cluster unmanned system attack detection and security defense control method. DETAILED DESCRIPTION

[0042] In order to make the person skilled in the art better understand the scheme of the present application, the technical scheme in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application.

[0043] In order to avoid the non-elasticity of the traditional consensus control to attacks and the unmanned system cannot work normally when unknown attacks occur, an observer is constructed for each follower of the cluster unmanned system to estimate the control input signal u i (t) of each follower, and then an attack detection criterion Ψ i is constructed, an attack detection algorithm is used to avoid false alarms, and then the control input signal is switched from u i (t) to wherein A virtual network layer is designed, so as to realize the tracking of the leader x0(t) by each follower, realize the safe tracking control of the cluster unmanned system under network attacks, and improve the security and invulnerability of the system to network attacks.

[0044] To achieve the above object, the application provides a network attack cluster unmanned system attack detection and security defense control method, wherein the cluster unmanned system is composed of one leader and N follower systems, which communicate through a directed topological graph, and the topological graph contains a spanning tree with the leader as the root node, based on which, the overall process of the application is as follows:

[0045] According to the set attack detection criterion, it is judged whether each follower is attacked, for the follower with a negative judgment result, the actual input control signal u i (t) is used to track the state of the leader, and for the follower with a positive judgment result, the control signal is used to track the state of the leader, wherein i=1, 2, …, N, N represents the number of followers in the cluster unmanned system, and δ i(t) represents the false data injection attack signal on the i-th follower. At the same time, when the attacked followers track the status of the leader, they use virtual variables to exchange their respective status information in the virtual network layer. is the control signal of the ith follower obtained by exchanging their respective state information according to the virtual network layer, and x i (t) represents the physical state of the i-th follower, k 1,i 、k 2,i With k 3,i All represent coefficients and meet the system stability requirements, requiring k 1,i >0, k 3,i >0,v i and ω i Both represent the dummy variables used by the i-th follower in the virtual network layer.

[0046] The following is a detailed derivation of the construction process of the attack detection and security defense control method of the cluster unmanned system under network attack provided by the present invention. Figure 1 As shown, the construction process includes the following steps:

[0047] S1: The dynamic model of the leader of the swarm unmanned system is established as follows:

[0048]

[0049] in represents the state of the leader dynamics model, represents the output of the leader dynamics model, represents the first-order derivative of x0(t), A represents the state coefficient matrix of the leader dynamics model, and C represents the output coefficient matrix of the leader dynamics model;

[0050] S2: The dynamic model of each follower of the swarm unmanned system is established as follows:

[0051]

[0052] Where i = 1, 2, ..., N, which means the swarm unmanned system has N followers. represents the physical state of the i-th follower dynamic model, represents the control input signal of the ith follower dynamics model, represents the output of the i-th follower dynamics model, Represents x ithe first derivative of (t), A represents a state coefficient matrix of the follower dynamics model, B represents an input coefficient matrix of the follower dynamics model, C represents an output coefficient matrix of the follower dynamics model, the matrices A, B and C are known;

[0053] S3: Establish a false data injection attack model as follows:

[0054]

[0055] wherein δ i (t) represents a false data injection attack signal on the i-th follower, the attack signal acts on the actuator, and the amplitude is limited in a predetermined range , that is represents a damaged control input signal of the i-th follower;

[0056] S4: Construct a linear function observer system for estimating the control input signal for each follower of the swarm unmanned system as follows:

[0057]

[0058] wherein represents an estimated value of the actual input control signal of each follower in a compact form, (·) T represents the transpose of ·, represents a compact form of the actual input control signals u1(t)~u N (t) of each follower, represents a compact form of the observer states w1(t)~w N (t) of each follower, represents the first derivative of w(t), T, F, J and P represent linear function observer system matrices selected according to the stability of the linear function observer system, respectively, the matrix S is a Hermitian matrix, and the linear function observer system matrices satisfy the following equation:

[0059]

[0060] wherein A' represents a compact form of the state coefficient matrix A, the symbol represents a Kronecker product, I N represents an N-order unit matrix, B' represents a compact form of the input coefficient matrix B, C' represents a compact form of the output coefficient matrix C, K' and H' are known, K' represents a compact form of the known first feedback gain matrix K, H' represents a compact form of the second feedback gain matrix H, is a Laplacian matrix, and Δ is a diagonal matrix, represents an estimated value of the compact form w(t) of the observer state, and L is an error coefficient matrix, represents a compact form of the tracking error e1(t)~e N (t) of each follower, and the tracking error corresponding to any one follower is represented as e i (t) = x i (t) - x0(t); the Laplacian matrix is a weight coefficient of the communication network a ij ∈{0,1}, and a ij =1 indicates that the i-th follower and the j-th follower have a communication flow, otherwise no communication flow, a i0 =1 indicates that the i-th follower and the leader have a communication flow, otherwise no communication flow, and the diagonal matrix Δ = diag{a 10 ,...,a N0}.

[0061] An estimated value of the control signal u i (t) of the actual input of each follower is obtained by using a linear function observer system

[0062] It should be noted that the variable is considered from the perspective of the original unmanned system, that is, the expected value of the observer state, so the observation state error is represented as Then, the observer matrix is designed to satisfy that the error e w (t) approaches 0 when time approaches infinity.

[0063] S5: Determine the attack detection criterion for each follower of the cluster unmanned system as follows:

[0064]

[0065] Ψ i represents the cumulative error measure of the i-th unmanned system, which measures the deviation between the actual control input and the estimated value of the control input on the time [0, T o ] integral, and Ψ i is used as an attack detection criterion, and the subsequent algorithm will be detected based on this criterion, μ i and T o are known parameters;

[0066] Further, the attack detection algorithm of the present application is:

[0067] S51: Define the attack detection algorithm variables and variable initial values:

[0068]

[0069] wherein, Ψ i represents the attack detection metric corresponding to the i-th follower, γ represents a flag variable, γ∈{0, 1}, γ=1 represents resetting the above variable, α i represents a frequency accumulation variable, used to store the number of times that the attack detection metric Ψ i reaches the threshold value Ψ i,max , s(t) and r(t) represent clock functions, s(t) is a clock function used to record and judge whether the accumulated calculation time of the attack detection metric Ψ i exceeds the first time period T o , r(t) is a clock function used to record and judge whether the accumulated calculation time of the frequency accumulation variable α i exceeds the second time period T c ; the specific forms of the clock functions are as follows:

[0070]

[0071] represents the first derivative of s(t), represents the first derivative of r(t);

[0072] S52: continuously calculating the attack detection metric Ψ i as follows according to the set time step:

[0073]

[0074] wherein, is the estimated value of the actual input control signal u i (t), μ i is the integral weight corresponding to the i-th follower, and τ is an integral variable;

[0075] S53: judging whether the attack detection metric Ψ i calculated at present is greater than the metric threshold value Ψ i,max and whether s(t) exceeds the first time period T o ; if the judgment results are both negative, directly continuing to execute steps S52-S53; if Ψ i is not greater than the metric threshold value Ψ i,max and s(t) exceeds the first time period T o , resetting Ψ i and s(t) to 0 and then re-executing steps S52-S53; if Ψ i is greater than the metric threshold value Ψ i,max , α i is added by 1, and step S54 is entered;

[0076] It should be noted that the dynamic calculation function Ψ i In the process, under safe conditions, with u i The values ​​of are approximately equal, resulting in Ψ i The value of is relatively small, and when the attack occurs, Significant deviation from u i , so that Ψ i The value of is relatively large, when Ψ i The value does not exceed the threshold Ψ i,max When the clock function s(t) is set to a period of T o Reset i and s, and recalculate Ψ in the new cycle i Once i The value of reaches the threshold, then it is necessary to judge α i Whether the conditions are met, see step S54 for details;

[0077] S54: Judgment α i Whether it exceeds the cumulative threshold α i,max And whether r(t) exceeds the second time period T c If the judgment results are all negative, then directly continue to execute steps S52 to S54. If α i Does not exceed the cumulative threshold α i,max And r(t) exceeds the second time period T c , then Ψ i , s(t), r(t) and α i After resetting to 0, re-execute steps S52 to S54. If α i Exceeding the cumulative threshold α i,max And r(t) does not exceed the second time period T c , it means that the current follower is under attack, so the control input signal is changed from u i (t) Switch to

[0078] S6: First, to simplify the writing, this equation and some formulas in the following text omit the time variable t, for example, using x i Represents x i (t), in order to achieve consistency control between the leader and followers, the traditional leader-follower consistency control protocol is adopted, and its control signal is defined as follows:

[0079]

[0080] u i =Ku' i

[0081] Among them, u' i (t) is the intermediate control signal;

[0082] It should be noted that in the present application, the control input designed by the virtual layer is defined as a reliable control input signal. Under the monitoring of the attack detection strategy, when a potential attack is detected, the control input signal can be switched in time.

[0083] Considering that the control input channel has both safe and attacked cases, the control input signal is expressed as:

[0084]

[0085] wherein, represents the control input signal of the ith unmanned system under attack;

[0086] Further, the virtual variable dynamics equation in the virtual network layer of the present application is:

[0087] S61: The virtual variable dynamics equation is as follows:

[0088]

[0089] wherein, v i , ω i and θ i all represent the three virtual variables corresponding to the ith follower, v j , ω j and θ j all represent the three virtual variables corresponding to the ith follower, T vi is the coefficient corresponding to the virtual variable v i , T θi is the coefficient corresponding to the virtual variable θ i , T ωi is the coefficient corresponding to the virtual variable ω i , η i represents a weight coefficient, γ i,j ∈{0, 1}, γ i,j and γ j,i are weight coefficients of the topology between any two followers in the virtual network layer, γ i0 is a proportion coefficient of the topology between the follower and the leader in the virtual network layer, k is a sum weight of the topology between any two followers, and β is a proportion coefficient of the topology between the follower and the leader.

[0090] It should be noted that in the virtual network layer, the sensor is assumed to be safe, and only the actuator can be attacked, which is reasonable because in the presence of a large number of variables, the attacker cannot effectively attack all variables.

[0091] S7: The attack detection criterion in step S5 estimates the control input signal by the attack detection algorithm in steps S51-S54, and triggers the conversion of the control signal when an attack is detected, i.e. The control signal is then substituted into step S2 to achieve tracking of the leader state x0 by each follower. The control signal is then substituted into step S2 to achieve tracking of the leader state x0 by each follower.

[0092] In summary, the present application provides an attack detection and security defense control method for a cluster unmanned system under network attack. Although the traditional consensus control protocol is simpler and more straightforward, it can save communication resources, but lacks flexibility against attacks, resulting in the inability to guarantee the overall security and stability of the system when it is attacked. Therefore, the present application designs an attack detection algorithm and a security defense strategy. Under the monitoring of the attack detection strategy, when the system is subjected to malicious attacks, the damaged control input signal will be replaced by a reliable control input signal. The defense strategy is implemented based on a virtual network layer, and physical state variables are not exchanged in reality. Only information is exchanged between virtual nodes and neighbor nodes in the virtual network layer to obtain reliable control input, thereby achieving safe tracking control of the cluster unmanned system under network attack.

[0093] Of course, the present application can have other various embodiments, and those skilled in the art can certainly make various corresponding changes and modifications according to the present application without departing from the spirit and essence of the present application. However, these corresponding changes and modifications should all belong to the protection scope of the claims attached to the present application.

Claims

1. A method for attack detection and security defense control of a cluster unmanned system under network attack, characterized in that: According to the set attack detection criteria, each follower is judged whether it is attacked. For followers whose judgment result is no, the actual input control signal u is used. i (t) Track the leader's status and use control signals for followers whose judgment result is yes Track the status of the leader, where i = 1, 2, ..., N, N represents the number of followers in the swarm unmanned system, δ i (t) represents the false data injection attack signal on the i-th follower. At the same time, when the attacked followers track the status of the leader, they use virtual variables to exchange their respective status information in the virtual network layer. is the control signal of the ith follower obtained by exchanging their respective state information according to the virtual network layer, and x i (t) represents the physical state of the i-th follower, k 1,i 、k 2,i With k 3,i Both represent coefficients, v i and ω i Both represent the dummy variables used by the i-th follower in the virtual network layer; The method for determining whether any follower is under attack according to the set attack detection criteria is as follows: Step 1: Define the initial values ​​of the variables for attack detection criteria as follows: Among them, i represents the attack detection metric corresponding to the i-th follower, α i To represent T c Within the cycle i Reaching the measurement threshold Ψ i,max The cumulative variable of the number of times, s(t) is used to record and judge the attack detection metric Ψ i Whether the cumulative calculation time exceeds the first time period T o The clock function, r(t) is used to record and judge the cumulative variable α i Whether the cumulative calculation time exceeds the second time period T c The clock function of Step 2: Continuously calculate the attack detection metric Ψ according to the set time step i as follows: in, is the actual input control signal u i The estimated value of (t), μ i is the integral weight corresponding to the i-th follower, τ is the integral variable; Step 3: Determine the currently calculated attack detection metric Ψ i Is it greater than the measurement threshold Ψ i,max And whether s(t) exceeds the first time period T o If the judgment results are all negative, then continue to execute steps 2 to 3. i Not greater than the measurement threshold Ψ i,max And s(t) exceeds the first time period T o , will i After resetting s(t) to 0, re-execute steps 2 to 3. If Ψ i Greater than the measurement threshold Ψ i,max , then α i Add 1 and go to step S4; Step 4: Determine α i Whether it exceeds the cumulative threshold α i,max And whether r(t) exceeds the second time period T c If the judgment results are all negative, then continue to execute steps 2 to 4. If α i Does not exceed the cumulative threshold α i,max And r(t) exceeds the second time period T c , then Ψ i , s(t), r(t) and α i After resetting to 0, execute steps 2 to 4 again. If α i Exceeding the cumulative threshold α i,max And r(t) does not exceed the second time period T c , it means the current follower is under attack.

2. The attack detection and security defense control method for a cluster unmanned system under network attack according to claim 1, characterized in that: The dynamic model of the leader of a swarm unmanned system is as follows: Among them, x0(t) represents the physical state of the leader, y0(t) represents the output of the leader's dynamic model, represents the first-order derivative of x0(t), A represents the state coefficient matrix, and C represents the output coefficient matrix.

3. The attack detection and security defense control method for a cluster unmanned system under network attack according to claim 2, characterized in that: The dynamic model of each follower in the swarm unmanned system is as follows: Among them, x i (t) represents the physical state of the i-th follower, u i (t) represents the actual control input signal of the ith follower, y i (t) represents the output of the dynamic model of the i-th follower, Represents x i (t), B represents the input coefficient matrix of the follower dynamics model, and matrices A, B and C are known.

4. The attack detection and security defense control method for a cluster unmanned system under network attack according to claim 3, characterized in that: The actual input control signal u of any follower i Estimated value of (t) The method to obtain is as follows: The linear function observer system for estimating the control input signal is constructed for each follower of the cluster unmanned system as follows: in, Represents the estimated value of the actual input control signal of each follower The compact form of (·) T represents the transpose of , The actual input control signals u1(t)~u N The compact form of (t), Denotes the observer state w1(t)~~w of each follower N The compact form of (t), represents the first-order derivative of w(t), T, F, J, and P represent the linear function observer system matrix selected according to the stability of the linear function observer system, the matrix S is the Hurwitz matrix, and the linear function observer system matrix satisfies the following equation: in, A′ represents the compact form of the state coefficient matrix A, and the symbol represents the Kronecker product, I N represents the N-order identity matrix, B′ represents the compact form of the input coefficient matrix B, C′ represents the compact form of the output coefficient matrix C, K′ represents the compact form of the first feedback gain matrix K, H′ represents the compact form of the second feedback gain matrix H, is the Laplace matrix, Δ is the diagonal matrix, represents the estimate of the compact form w(t) of the observer state, L is the error coefficient matrix, represents the tracking error of each follower e1(t)~e N (t), and the tracking error corresponding to any follower is expressed as e i (t) = x i (t)-x0(t); The linear function observer system is used to obtain the actual input control signal u of each follower i Estimated value of (t) 5. The attack detection and security defense control method for a cluster unmanned system under network attack according to claim 4, characterized in that: Laplacian matrix The weight coefficient a of the communication network ij ∈{0,1}, and a ij =1 indicates that there is a communication flow between the ith follower and the jth follower, otherwise there is no communication flow, a i0 =1 indicates that there is a communication flow between the ith follower and the leader, otherwise there is no communication flow, and the diagonal matrix Δ = diag{a 10 ,...,a N0 }.

6. The attack detection and security defense control method for a cluster unmanned system under network attack according to claim 5, characterized in that: The actual input control signal u of any follower i The method to obtain (t) is: u i (t)=Ku' i (t) Among them, u' i (t) is the intermediate control signal.

7. The attack detection and security defense control method for a cluster unmanned system under network attack according to claim 5, characterized in that: The variable dynamics equation in the virtual network layer corresponding to any follower is as follows: Among them, v i 、ω i and θ i All represent the three dummy variables corresponding to the ith follower, v j 、ω j and θ j All represent the three dummy variables corresponding to the i-th follower, T vi is the dummy variable v i The corresponding coefficient, T θi is the dummy variable θ i The corresponding coefficient, T ωi is the dummy variable ω i The corresponding coefficient, η i represents the weight coefficient, γ i,j ∈{0, 1}, γ i,j with γ j,i is the weight coefficient of the topological structure between any two followers in the virtual network layer, γ i0 is the proportional coefficient of the topological structure between followers and leaders in the virtual network layer, k is the sum weight of the topological structure between any two followers, and β is the proportional coefficient of the topological structure between followers and leaders.

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