Anti-collaborative detection eavesdropping method and system in unmanned aerial vehicle covert secure network

Abstract

The application provides a method and system for resisting cooperative detection eavesdropping in a hidden safe network of a UAV, comprising: based on a UAV uplink communication network scene of multiple legal users and multiple cooperative illegal users, obtaining a maximum safe communication rate of each legal user in each time slot under cooperation eavesdropping of the multiple illegal users; based on a UAV uplink communication network scene of multiple legal users and multiple cooperative illegal users, obtaining a minimum detection error rate of communication behavior of each legal user under cooperation detection of the multiple illegal users; based on the maximum safe communication rate and the minimum detection error rate, establishing a joint optimization problem with the maximum system minimum safe throughput as a target, and setting a constraint condition; based on a continuous convex approximation algorithm, solving the joint optimization problem to obtain an optimized UAV trajectory, user transmission power and time slot segmentation scheme.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) communication technology, and in particular relates to a method and system for resisting cooperative detection and eavesdropping in a covert security network for UAVs. Background Technology

[0002] In recent years, unmanned aerial vehicles (UAVs) have attracted widespread attention as an effective means to overcome the physical transmission limitations of traditional terrestrial communication networks, due to their high probability of Loss of Service (LoS) links, high flexibility, and low cost. In particular, leveraging the network design freedom of UAVs' high mobility, and through the joint design of UAV flight paths and resource allocation, the quality of air-to-ground line-of-sight channels can be improved, further enhancing communication performance.

[0003] However, the broadcast nature of UAV air-to-ground channels poses serious security and privacy challenges to networks. Physical Layer Security (PLS) technology has proven to be a promising approach to protect UAV communication privacy. One well-known PLS technology, known as Secure Communication, primarily aims to protect confidential data. In 2024, Mamaghani et al. investigated secure UAV communication schemes, improving secure throughput through joint optimization of trajectory and resource allocation. Also in 2024, Liu et al. proposed a dual-UAV cooperative secure communication mode for scenarios with multiple eavesdroppers, optimizing trajectories to maximize secure transmission rates. Beyond information security, covert communication technology is a crucial component of PLS, aiming to protect communication activities by concealing their presence. In 2022, Chen et al. achieved covertness in UAV communication networks using jammers. Extending Chen's work to scenarios with multiple monitors, in 2025, Deng et al. investigated the joint design of beamforming vectors and UAV trajectories to maximize achievable covertness while preventing detection by multiple monitors.

[0004] The above work on covert and secure communication between drones and multiple unauthorized users neglects the potential cooperation among these users, primarily assuming that unauthorized users conduct unauthorized detection and eavesdropping independently. It is important to emphasize that in real-world scenarios, unauthorized users can enhance their detection and eavesdropping capabilities through collaborative mechanisms. This necessitates research into countermeasures within a multi-unauthorized user collaborative framework. Therefore, further research is needed on anti-cooperative detection and eavesdropping methods and systems for covert and secure drone networks. Summary of the Invention

[0005] In view of the current lack of methods for detecting and eavesdropping on multiple unauthorized users in UAV wireless communication networks, this invention proposes a method and system for resisting cooperative detection and eavesdropping in UAV uplink communication networks with multiple legitimate users, multiple cooperative unauthorized users, and a friendly jammer, in order to improve the concealment and security of UAV wireless communication networks.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A method for resisting cooperative detection and eavesdropping in a covert security network for unmanned aerial vehicles includes the following steps:

[0008] Step S1: Based on the drone uplink communication network scenario with multiple legitimate users and multiple collaborative illegal users, obtain the maximum secure communication rate of each legitimate user in each time slot under the collaborative eavesdropping of multiple illegal users;

[0009] Step S2: Based on the drone uplink communication network scenario with multiple legitimate users and multiple collaborative illegal users, obtain the minimum detection error rate for the communication behavior of each legitimate user under the collaborative detection of multiple illegal users;

[0010] Step S3: Based on the maximum secure communication rate and the minimum detection error rate, establish a joint optimization problem with the goal of maximizing the system and minimizing secure throughput, and set constraints;

[0011] Step S4: Solve the joint optimization problem based on the continuous convex approximation algorithm to obtain the optimized UAV trajectory, user transmit power, and time slot segmentation scheme.

[0012] Furthermore, the drone uplink communication network scenario involving multiple legitimate users and multiple cooperative illegal users includes: a drone, multiple legitimate ground users, a friendly jammer, and multiple cooperative illegal users; the legitimate users transmit data to the drone via the uplink, the illegal users cooperate to detect the communication behavior and eavesdrop on the transmitted data, and the friendly jammer emits artificial noise to interfere with the illegal users while avoiding affecting legitimate communication.

[0013] Further, step S1 includes:

[0014] Based on the locations of legitimate users, illegitimate users, jammers, and drones, calculate the channel gain from legitimate user to illegitimate user, from jammer to illegitimate user, and from legitimate user to drone. Based on the channel gain, derive the signal-to-noise ratio (SNR) expressions for the illegitimate user and drone locations. Based on the SNR expressions, obtain the maximum secure communication rate expression for the legitimate user in each time slot under the multi-illegitimate user cooperative eavesdropping scenario.

[0015] Furthermore, the expression for the maximum secure communication rate of a legitimate user in each time slot under the multi-illegal-user collaborative eavesdropping scenario is:

[0016]

[0017] in This represents a collaborative eavesdropping operation by multiple unauthorized users. The first time slot Maximum secure communication rate for each legitimate user represent A joint distribution of independent exponential distributions with a dimension parameter of 1; and It follows an independent exponential distribution with parameter 1. ; Representing the The drone in the first time slot Signal-to-noise ratio of each legitimate user; Representing the The first time slot The illegal user was dealt with. The signal-to-noise ratio of a legitimate user.

[0018] Further, step S2 includes: obtaining the probability density function of the received signal power at each illegal user based on the channel gain; obtaining a non-explicit expression for the minimum detection error rate based on the likelihood ratio detection principle and Pinsker's inequality; and transforming the non-explicit expression into an explicit expression based on Taylor series expansion and the properties of the Digamma function to obtain the minimum detection error rate for the communication behavior of each legitimate user under the cooperative detection of multiple illegal users.

[0019] Furthermore, under the aforementioned collaborative detection of multiple illegal users, the minimum detection error rate for the communication behavior of each legitimate user is:

[0020]

[0021] in and represent and intermediate function, Representing the Interference power distribution parameters at the location of an unauthorized user. Representing the The first time slot At the location of an illegal user The signal power distribution parameters of each legitimate user; Represents the Euler-Mascheroni constant. For the Digamma function, the minimum detection error rate The transmit power of legitimate users decreases monotonically.

[0022] Further, step S3 includes: deriving the maximum transmit power of a legitimate user that satisfies preset covert communication constraints based on the monotonicity of the expression for the minimum detection error rate; constructing the objective function based on the expression for the maximum secure communication rate, with the objective function being the minimum secure throughput among all legitimate users; defining constraints regarding UAV mobility, user transmit power, and time slot segmentation; and combining the objective function with the constraints to form the joint optimization problem.

[0023] Furthermore, the joint optimization problem is:

[0024]

[0025] in, Representing the The first time slot The time slot segmentation coefficient for each legitimate user Represents the time slot length. Representing the Secure communication throughput for each legitimate user; To jointly optimize drone trajectories User transmit power and time slot division Maximize secure communication throughput and minimize secure throughput for legitimate users; This represents the total number of time slots. This represents the first time slot position of the drone. Representing the first drone Each time slot position, Representing the The location of the drone in each time slot Representing the The location of the drone in each time slot This represents the maximum speed of the drone; Representing the Maximum transmit power limit for each legitimate user Representing the Average transmit power limit for each legitimate user; K This represents the total number of legitimate users. The first one represents the one that satisfies the covert communication constraints. Maximum transmit power for each legitimate user.

[0026] Furthermore, the continuous convex approximation iterative algorithm in step S4 is as follows:

[0027] Step S4.1: Initialize the UAV initial trajectory, user initial transmit power, and initial time slot segmentation variables. And use it as a local point in the iteration;

[0028] Step S4.2: In each iteration, use the convex approximation method at local points. The joint optimization problem established in step S3, which aims to maximize the system and minimize the safe throughput, is approximated as a compact convex problem.

[0029] Step S4.3: Solve the compact convex problem constructed in step S4.2 using a convex optimization algorithm to obtain the local optimum solution for this iteration. ;

[0030] Step S4.4: If the performance improvement in this iteration is less than a given threshold compared to the previous iteration, then stop the iteration. The local optimum of this iteration is then determined. This is the optimal solution to the problem; otherwise, it will be used as a local point in the next iteration. Return to step S4.2 and continue iterating.

[0031] On the other hand, the present invention provides an anti-cooperative detection and eavesdropping system in a covert security network for unmanned aerial vehicles, comprising:

[0032] Security rate derivation module: It is used in drone uplink communication network scenarios with multiple legitimate users and multiple cooperative illegal users to obtain the maximum secure communication rate of each legitimate user in each time slot under the cooperative eavesdropping of multiple illegal users;

[0033] Error rate derivation module: It is used in drone uplink communication network scenarios with multiple legitimate users and multiple cooperative illegal users to obtain the minimum error rate for the communication behavior of each legitimate user under the cooperative detection of multiple illegal users;

[0034] Joint optimization problem construction module: It is used to establish a joint optimization problem with the goal of maximizing the minimum safe throughput of the system based on the maximum safe communication rate and the minimum detection error rate, and to set constraints.

[0035] The optimization solution module is used to solve the joint optimization problem based on the continuous convex approximation algorithm, and obtain the optimized UAV trajectory, user transmit power and time slot segmentation scheme.

[0036] Compared with existing technologies, the present invention has the following advantages: Taking into account the cooperative mechanisms of unauthorized users, the present invention balances the communication concealment and security performance in UAV communication networks. By jointly designing UAV trajectories, user transmission power, and time slot segmentation, it significantly improves network throughput performance. Simulation experiments verify that the present invention significantly outperforms existing technologies in terms of secure throughput performance. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in this 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 some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of a covert security network for unmanned aerial vehicles (UAVs) according to an embodiment of the present invention.

[0039] Figure 2 This is a flowchart illustrating the method implemented in this invention;

[0040] Figure 3 The drone trajectory map is designed and optimized for embodiments of the present invention;

[0041] Figure 4 This invention provides a comparison of uplink secure throughput with the user's maximum transmit power for embodiments of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0043] Example 1

[0044] The following is combined Figure 1-3 The present invention provides a method and system for resisting cooperative detection and eavesdropping in a covert security network for unmanned aerial vehicles (UAVs), as detailed below:

[0045] Figure 1 A covert security network for unmanned aerial vehicles (UAVs) is considered for a specific embodiment of the present invention. This network includes a UAV, One legitimate ground user, one friendly jammer, and A collaborative illegal user, Figure 1 In this context, "time slot" represents a time interval. Legitimate ground users transmit data to the UAV via the uplink channel. Their communication with the UAV is subject to cooperative detection and eavesdropping by unauthorized users. Simultaneously, a friendly jammer transmits artificial noise to interfere with the cooperative detection and eavesdropping by unauthorized users without interfering with legitimate communication. Let the UAV's flight altitude be denoted as... Maximum flight speed is The jammer's interference power is To improve the performance of covert and secure UAV networks, it is necessary to jointly design the UAV trajectory, user transmit power, and time slot division coefficient to maximize system communication performance. This problem is highly complex.

[0046] like Figure 2 The diagram shown is a flowchart of the method of the present invention, which includes the following steps:

[0047] Step 1: Derivation of the maximum secure communication rate for legitimate users under multi-illegal-user cooperative eavesdropping. First, calculate the channel gain from the legitimate user and jammer to the illegal user, and from the legitimate user to the drone, based on the locations of the legitimate user, illegal user, jammer, and drone. Then, derive the signal-to-noise ratio (SNR) expressions for the illegal user and drone based on the channel gain expression. Finally, obtain the expression for the maximum secure communication rate for legitimate users under multi-illegal-user cooperative eavesdropping based on the SNR expressions for the illegal user and drone.

[0048] The channel gain from the legitimate user to the illegitimate user mentioned in step 1 is:

[0049]

[0050] in Represents a valid user ID. This represents the ID of an illegal user. Representing the The number of legitimate users to the number Channel gain of an illegal user This represents the additional path loss coefficient of the ground Rayleigh channel compared to the LoS channel. This represents the channel power gain per unit distance. represent The number of legitimate users to the number The distance to an unauthorized user represent A legitimate user location, represent An unauthorized user location. Represents the Rayleigh Road Damage Index. This represents a standard complex Gaussian distribution variable.

[0051] The channel gain from the jammer to the illegal user mentioned in step 1 is:

[0052]

[0053] in Representing the jammer to the number Channel gain of an illegal user Representing the jammer to the number The distance to an unauthorized user Represents the location of the jammer. This represents a standard complex Gaussian distribution variable.

[0054] The channel gain from the legitimate user to the drone mentioned in step 1 is:

[0055]

[0056] in, Represents the time slot number. The total number of time slots, Representing the Each time slot Channel gain from a legitimate user to the drone; Representing the Each time slot The distance from a legitimate user to the drone. Representing the The location of the drone in each time slot This represents the Loss of Speed ​​(LoS) road damage index.

[0057] The signal-to-noise ratio expression for the illegal user mentioned in step 1 is as follows:

[0058]

[0059] in Representing the The first time slot Signal-to-noise ratio at the location of an unauthorized user Representing the The first time slot Transmit power of each legitimate user Represents the power of the jammer. Represents the variance of ground background noise; and For intermediate parameters; and It follows an independent exponential distribution with parameter 1. .

[0060] The signal-to-noise ratio expression for the UAV mentioned in step 1 is:

[0061]

[0062] in Representing the The drone in the first time slot The signal-to-noise ratio of a legitimate user This represents the variance of ambient background noise.

[0063] The expression for the maximum secure communication rate of legitimate users under multi-illegal-user cooperative eavesdropping described in step 1 is:

[0064]

[0065] in This represents a collaborative eavesdropping operation by multiple unauthorized users. The first time slot Maximum secure communication rate for each legitimate user represent A joint distribution of independent exponential distributions with a dimension parameter of 1.

[0066] Step 2: Derivation of the minimum detection error rate expression for cooperative detection of multiple illegal users. First, based on the channel gain from legitimate users and the jammer to illegal users, we obtain the expression for the received signal power at the illegal user and the corresponding probability density function of the received signal power. Then, based on the likelihood ratio detection principle and Pinsker's inequality, we obtain the non-explicit expression for the minimum detection error rate under cooperative detection of multiple illegal users. Finally, based on the properties of Taylor series and the Digamma function, we obtain the expression for the minimum detection error rate under cooperative detection of multiple illegal users.

[0067] The expression for the received signal power at the illegal user's location mentioned in step 2 is:

[0068]

[0069] in Representing the The first time slot detection The first legitimate user Signal power received by an unauthorized user. represent The assumption that no legitimate user communicates. represent The assumption that a legitimate user is communicating; Representing the The first time slot Interference power at the location of an unauthorized user Representing the The first time slot At the location of an illegal user The signal power of each legitimate user. Obtain the parameter as The exponential distribution of has a probability density function as:

[0070]

[0071] in represent The probability density function, Represents a random variable.

[0072] Obtain the parameter as The exponential distribution of has a probability density function as:

[0073]

[0074] in represent The probability density function.

[0075] remember Its probability density function is:

[0076]

[0077] in represent The probability density function.

[0078] The non-explicit expression for the minimum detection error rate under multi-illegal-user cooperative detection, obtained in step 2 based on the likelihood ratio detection principle and Pinsker's inequality, is as follows:

[0079]

[0080] in Represents collaborative detection of multiple illegal users. Minimum detection error rate when there are 100 legitimate users. represent In the case of multiple unauthorized users receiving signal power jointly distributed, represent In the case of joint distribution of received signal power by multiple unauthorized users, represent and Inverse KL divergence.

[0081] The expression for the minimum detection error rate under multi-illegal-user collaborative detection described in step 2 is:

[0082]

[0083] in and All represent and intermediate function, Represents the Euler-Mascheroni constant. This is the Digamma function.

[0084] The derivation of the expression is as follows: Because Since they are independent random variables, ,in The expression is:

[0085]

[0086] Where B is the calculation The intermediate variable expression, because Therefore, the closed expression for B is:

[0087]

[0088] in, To calculate the intermediate variable expression of the closed expression B. Because... Therefore, C can be expanded using Taylor series as follows:

[0089]

[0090] Among them, infinite series The closed expression is:

[0091]

[0092] in Represents a Taylor series index.

[0093] In conclusion, The closed expression is:

[0094]

[0095] Therefore, we can obtain .

[0096] Step 3: Establish a joint optimization problem for drone trajectory, user transmit power, and time slot segmentation in a covert drone security network resistant to cooperative detection and eavesdropping. First, derive the expression for the maximum user transmit power that satisfies covert communication constraints based on the monotonicity of the minimum detection error rate expression under multi-illegal-user cooperative detection. Second, construct the objective of the optimization problem based on the expression for the maximum secure communication rate of legitimate users under multi-illegal-user cooperative eavesdropping. Then, define the mobility constraints, user transmit power constraints, and time slot segmentation constraints for the optimization problem. Finally, based on the constructed objective and constraints, establish a joint optimization problem for drone trajectory, user transmit power, and time slot segmentation in a covert drone security network resistant to cooperative detection and eavesdropping.

[0097] The expression for the maximum transmit power of the user satisfying the covert communication constraints mentioned in step 3 is:

[0098]

[0099] in The first one represents the one that satisfies the covert communication constraints. Maximum transmit power for each legitimate user This represents the maximum permissible probability of exposure. The meaning of the above formula is: The value of is such that hour The value of .

[0100] The goal of step 3 in constructing the optimization problem is:

[0101]

[0102] in Representing the The first time slot The time slot segmentation coefficient for each legitimate user Represents the time slot length. Representing the Secure communication throughput for legitimate users. The aforementioned objective means achieving this through joint optimization of drone trajectories. (The drone's location in all time slots, i.e., the drone's trajectory), user transmit power and time slot division Maximize secure communication throughput and minimize secure throughput for legitimate users. .

[0103] The mobility constraint of the optimization problem described in step 3 is:

[0104]

[0105] in This represents the first time slot position of the drone. Representing the first drone Each time slot position, Representing the The location of the drone in each time slot. The constraint means the position of the first time slot of the UAV and the position of the second time slot. The time slots are all at the same location, meaning the drone's trajectory is a closed curve. The constraint means that the speed of the drone should be less than its maximum speed.

[0106] The user transmit power constraint for the optimization problem described in step 3 is:

[0107]

[0108] in Representing the Maximum transmit power limit for each legitimate user Representing the Average transmit power limit for each legitimate user.

[0109] The time slot partitioning constraint of the optimization problem described in step 3 is:

[0110]

[0111] The meaning of the above constraints is as follows: The value of is between 0 and 1, and the sum of the internship segmentation coefficients of all legitimate users in any time slot must not be greater than 1.

[0112] Step 3 describes the joint optimization problem of drone trajectory, user transmission power, and time slot segmentation in the anti-cooperative detection and eavesdropping network within the drone covert security network:

[0113]

[0114] The aforementioned design optimization problem means maximizing the secure communication throughput and minimizing the secure throughput of legitimate users by optimizing the unmanned aircraft trajectory, user transmit power, and time slot segmentation, under mobility constraints, user transmit power constraints, and time slot segmentation constraints.

[0115] Step 4: Solve the above problem based on the continuous convex approximation to obtain the optimized solutions for UAV trajectory, user transmit power, and time slot segmentation.

[0116] The continuous convex approximation iterative algorithm described in step 4 is as follows:

[0117] Step 4.1: Initialize the UAV initial trajectory, user initial transmit power, and initial time slot segmentation variables. And use it as a local point in the iteration;

[0118] Step 4.2: In each iteration, use the convex approximation method at local points. The joint optimization problem of drone trajectory, user transmission power and time slot segmentation in the anti-cooperative detection and eavesdropping network of the drone covert security network established in step 3 is approximated as a compact convex problem.

[0119] Specifically, approximating the joint optimization problem of drone trajectory, user transmission power, and time slot segmentation in the anti-cooperative detection and eavesdropping network of the drone covert security network as a compact convex problem is mainly achieved by... It is approximately implemented as a lower-bound concave function. First, because the function The single-decreasing convexity at iterative local points At this point, the following inequalities hold:

[0120]

[0121] in, To iterate at local points The lower bound is concave approximation. This represents the approximation coefficient, and its values ​​are:

[0122]

[0123] The tight convexity problem mentioned in step 4.2 is as follows:

[0124]

[0125] Step 4.3: Solve the compact convex problem constructed in Step 4.2 using a convex optimization algorithm to obtain the local optimum solution for this iteration. ;

[0126] Step 4.4: If the performance improvement in this iteration is less than a given threshold compared to the previous iteration, then stop the iteration. The local optimum of this iteration is then determined. This is the optimal solution to the problem; otherwise, it will be used as a local point in the next iteration. Return to step 4.2 and continue iterating.

[0127] Figure 3 The UAV trajectory diagram optimized in the embodiment of the present invention is given, where users represent legitimate users, eves represent legitimate users, jammer represents jammer, and the BCD comparison scheme represents the block coordinate descent method. It can be seen that the scheme proposed in this invention can effectively optimize the UAV trajectory. Figure 4 The uplink secure throughput is compared with the user's maximum transmit power. It can be seen that as the user's maximum transmit power increases, the uplink secure throughput of both the proposed and comparative schemes increases, and the performance of the proposed scheme is consistently greater than that of the comparative scheme, which verifies the performance advantage of the proposed scheme.

[0128] Example 2

[0129] This embodiment provides an anti-cooperative detection and eavesdropping system in a covert security network for unmanned aerial vehicles, including:

[0130] Security rate derivation module: It is used in drone uplink communication network scenarios with multiple legitimate users and multiple cooperative illegal users to obtain the maximum secure communication rate of each legitimate user in each time slot under the cooperative eavesdropping of multiple illegal users;

[0131] Error rate derivation module: It is used in drone uplink communication network scenarios with multiple legitimate users and multiple cooperative illegal users to obtain the minimum error rate for the communication behavior of each legitimate user under the cooperative detection of multiple illegal users;

[0132] Joint optimization problem construction module: It is used to establish a joint optimization problem with the goal of maximizing the minimum safe throughput of the system based on the maximum safe communication rate and the minimum detection error rate, and to set constraints.

[0133] The optimization solution module is used to solve the joint optimization problem based on the continuous convex approximation algorithm, and obtain the optimized UAV trajectory, user transmit power and time slot segmentation scheme.

[0134] It should be understood that any parts not described in detail in this specification belong to the prior art.

[0135] It should be understood that the above description of preferred embodiments is quite detailed, but this should not be construed as limiting the scope of protection of this invention. It is neither necessary nor possible to exhaustively describe all possible embodiments. Those skilled in the art, guided by this invention, can make substitutions or modifications without departing from the scope of the claims, all of which fall within the scope of protection of this invention. The scope of protection of this invention should be determined by the appended claims.

Claims

1. A method for resisting cooperative detection and eavesdropping in a covert security network for unmanned aerial vehicles (UAVs), characterized in that, The method comprises the following steps: Step S1: obtaining the maximum safe communication rate of each legal user in each time slot under the cooperation of multiple illegal users based on the scenario of a multi-legal user and multi-colluding illegal user unmanned aerial vehicle uplink communication network; Step S2: obtaining the minimum detection error rate of the communication behavior of each legal user under the cooperation of multiple illegal users based on the scenario of a multi-legal user and multi-colluding illegal user unmanned aerial vehicle uplink communication network; Step S3: based on the maximum safe communication rate and the minimum detection error rate, a joint optimization problem is established to maximize the system and minimize the safe throughput, and a constraint condition is set; including: According to the monotonicity of the expression of the minimum detection error rate, the maximum transmission power of the legal user satisfying the preset covert communication constraint is derived; the maximum safe communication rate expression is taken as the basis to construct the objective function of maximizing the minimum safe throughput of all legal users; the constraint conditions related to the unmanned aerial vehicle mobility, user transmission power and time slot segmentation are defined; the objective function and the constraint conditions are combined to form the joint optimization problem; the joint optimization problem is: wherein, represents the maximum safe communication rate of the th legitimate user in the th time slot, represents the time slot segmentation factor of the th legitimate user in the th time slot, represents the time slot length, represents the safe communication throughput of the th legitimate user; to maximize the safe throughput of the minimum legitimate user by jointly optimizing the UAV trajectory , user transmit power and time slot segmentation ; is the total number of time slots; represents the first time slot position of the UAV, represents the th time slot position of the UAV, represents the position of the UAV in the th time slot, represents the position of the UAV in the th time slot, represents the maximum speed of the UAV; represents the maximum transmit power limit of the th legitimate user, represents the average transmit power limit of the th legitimate user; K denotes the total number of legitimate users, represents the maximum transmit power of the th legitimate user satisfying the concealment communication constraint; Step S4: solving the joint optimization problem based on a continuous convex approximation algorithm to obtain an optimized unmanned aerial vehicle trajectory, user transmission power and time slot segmentation scheme. 2.The method for resisting cooperative detection eavesdropping in a hidden secure network of a UAV according to claim 1, wherein, The multi-legal user and multi-colluding illegal user unmanned aerial vehicle uplink communication network scenario includes one unmanned aerial vehicle, multiple ground legal users, one friendly jammer and multiple colluding illegal users; the legal users transmit data to the unmanned aerial vehicle through the uplink, the illegal users cooperatively detect the communication behavior and eavesdrop on the transmitted data, and the friendly jammer transmits artificial noise to interfere with the illegal users while avoiding affecting the legal communication. 3.The method of claim 2, wherein, The step S1 comprises: According to the positions of the legal users, illegal users, jammers and unmanned aerial vehicles, the channel gains of the legal users to the illegal users, jammers to the illegal users and legal users to the unmanned aerial vehicle are calculated respectively; based on the channel gains, the signal-to-noise ratio expressions at the illegal users and the unmanned aerial vehicle are derived respectively; based on the signal-to-noise ratio expressions, the maximum safe communication rate expression of the legal user in each time slot under the cooperation of multiple illegal users is obtained.

4. The method according to claim 3, wherein, The maximum safe communication rate expression of the legal user in each time slot under the cooperation of multiple illegal users is: wherein, represents a joint distribution of independent exponential distributions with parameter 1 ; and is a joint distribution of independent exponential distributions with parameter 1 ; ; represents the signal-to-noise ratio of the i-th legitimate user at the j-th time slot drone; represents the signal-to-noise ratio of the i-th legitimate user at the j-th time slot drone; represents the signal-to-noise ratio of the i-th legitimate user at the j-th time slot drone; represents the signal-to-noise ratio of the i-th legitimate user at the j-th time slot drone; represents the signal-to-noise ratio of the i-th legitimate user at the j-th time slot drone; represents the signal-to-noise ratio of the i-th legitimate user at the j-th time slot drone; represents the signal-to-noise ratio of the i-th legitimate user at the j-th time slot drone.

5. The method of claim 4, wherein, The step S2 comprises: obtaining the probability density function of the received signal power at each illegal user based on the channel gain; based on the likelihood ratio detection principle and Pinsker inequality, a non-explicit expression of the minimum detection error rate is obtained; based on Taylor series expansion and Digamma function properties, the non-explicit expression is converted into an explicit expression to obtain the minimum detection error rate of the communication behavior of each legal user under the cooperation of multiple illegal users.

6. The method according to claim 5, wherein, The minimum detection error rate of the communication behavior of each legal user under the cooperation of multiple illegal users is: in and represent and intermediate function, Representing the Interference power distribution parameters at the location of an unauthorized user. Representing the The first time slot At the location of an illegal user The signal power distribution parameters of each legitimate user; Represents the Euler-Mascheroni constant. For the Digamma function, the minimum detection error rate The transmit power of legitimate users decreases monotonically.

7. The method of claim 1, wherein, The continuous convex approximation iterative algorithm in step S4 is as follows: Step S4.1 : initializing the initial trajectory of the drone, the initial launch power of the user and the initial time slot segmentation variable and take it as the local point of iteration; Step S4.2: In each iteration, the joint optimization problem established in step S3 to maximize system, minimum safety throughput is approximated as a tight convex problem at the local point Step S4.2: In each iteration, the joint optimization problem established in step S3 to maximize system, minimum safety throughput is approximated as a tight convex problem at the local point Step S4.3: solve the convex problem constructed in step S4.2 by using a convex optimization algorithm to obtain the local optimal solution of this iteration ; Step S4.4: If the performance increment of this iteration is less than a given threshold compared to the last iteration, stop the iteration, then the local optimal solution of this iteration is the optimal solution of the problem; otherwise, it is the local point of the next iteration Go back to step S4.2 to continue the iteration.

8. An anti-colluding eavesdropping system in a drone covert secure network, characterized in that, including: A security rate derivation module is configured to obtain a maximum security communication rate of each legitimate user in each time slot under multi-illegal user cooperative eavesdropping based on a multi-legitimate user and multi-cooperative illegal user UAV uplink communication network scenario. A detection error rate derivation module is configured to obtain a minimum detection error rate of the communication behavior of each legitimate user under multi-illegal user cooperative detection based on a multi-legitimate user and multi-cooperative illegal user UAV uplink communication network scenario. A joint optimization problem construction module is configured to establish a joint optimization problem with the maximum system minimum security throughput as the target based on the maximum security communication rate and the minimum detection error rate, and set a constraint condition. An optimization solving module is configured to solve the joint optimization problem based on a continuous convex approximation algorithm to obtain an optimized UAV trajectory, user transmission power, and time slot segmentation scheme. The anti-cooperative detection eavesdropping system in the UAV concealed security network is configured to perform the steps of the anti-cooperative detection eavesdropping method in the UAV concealed security network according to any one of claims 1-7.

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