Security routing regulation method, device, system and equipment of unmanned aerial vehicle communication network and medium

Abstract

The application discloses a kind of security routing regulation method, device, system, equipment and medium of unmanned plane communication network, belong to the field of security regulation, the method is: through unmanned plane communication network, the real-time detection data of unmanned plane is obtained;Based on inspection data, data integrity attack detection, GPS spoofing attack detection and network availability attack detection are carried out respectively, and the suspicious node of unmanned plane communication network is obtained;The global threat level of suspicious node is calculated using belief function, and the corresponding routing security decision scheme is determined based on global threat level to carry out security regulation processing to unmanned plane communication network.The application can obtain the data of unmanned plane in real time, and carry out different detection on the data uploaded by unmanned plane, not only can be identified and processed for different scenes, but also reduce detection error, improve detection accuracy, subsequent can be regulated according to attack type, to fit real-time communication state, improve the accuracy of regulation.

Description

Technical Field

[0001] This invention relates to the technical field of communication control, and in particular to a secure routing control method, apparatus, system, equipment, and medium for unmanned aerial vehicle (UAV) communication networks. Background Technology

[0002] With the rapid development of Unmanned Aerial Vehicle (UAV) technology, autonomous inspection of power distribution lines using UAVs equipped with sensors such as visible light cameras, infrared thermal imagers, and lidar has become a mainstream technology in the power industry. In power distribution inspection missions, multiple UAVs form a formation, communicate with each other through a wireless ad hoc network, and forward the collected inspection data (such as information on abnormal line temperatures, insulator damage, and obstructions in the transmission line) to a remote analysis center for processing via a base station.

[0003] However, drone communication networks are susceptible to various cyberattacks, which can lead to the failure of power distribution inspection tasks and potentially cause safety hazards to the power system. Therefore, security detection and control are necessary. One common approach is to rely on dedicated intrusion detection systems. For example, using real-world drone network intrusion detection datasets such as UAV-NIDD, and targeting the vulnerability characteristics of drone-specific communication protocols like MAVLink, a customized intrusion detection system can be built to cover the identification needs of various drone-specific network attacks, such as black hole attacks and DNS attacks. Security control can then be implemented based on the identification results.

[0004] However, the detection method based on the standard intrusion detection system has the following technical problems: Although the intrusion detection system can identify and process different scenarios, the signature detection is too broad in its rules, which can easily misjudge "normal communication anomalies" as attacks, resulting in low detection accuracy. This makes it difficult for subsequent control to adapt to the actual communication status, and the control may be biased. Summary of the Invention

[0005] This invention provides a secure routing control method, apparatus, system, equipment, and medium for unmanned aerial vehicle (UAV) communication networks, which can solve one or more technical problems existing in the prior art.

[0006] A first aspect of this invention provides a secure routing control method for an unmanned aerial vehicle (UAV) communication network, the method comprising: The drone communication network is used to acquire real-time detection data of the drone, including inspection data of the drone sensor inspection task, the drone's coordinate parameters, and forwarding reports from the drone to the base station. Based on the inspection data, attack detection processing is performed to obtain suspected nodes of the UAV communication network. The attack detection processing includes data integrity attack detection, GPS spoofing attack detection, and network availability attack detection. The global threat level of the suspected node is calculated using a belief function, and a corresponding routing security decision scheme is determined based on the global threat level to perform security regulation and control on the UAV communication network.

[0007] This invention can acquire drone data in real time and perform multiple different tests on the data uploaded by the drone. It can not only identify and process data for different scenarios, but also reduce detection errors, avoid misjudging "normal communication anomalies" as attacks, and improve detection accuracy. Subsequently, it can be adjusted according to the attack type to match the real-time communication status and improve the accuracy of the adjustment.

[0008] In conjunction with the first aspect, in one implementation, the detection operation for data integrity attack detection includes: Based on the inspection data, the mean vector and covariance matrix are determined respectively; The mean vector and the covariance matrix are used to calculate the data deviation value, which is the Mahalanobis distance between the inspection data and the normal state data uploaded by the UAV. If the deviation value of the data is greater than the preset deviation threshold, the communication node of the UAV corresponding to the inspection data is determined to be a suspected node. The preset deviation threshold is an adaptive threshold updated based on the historical data of the UAV using machine learning.

[0009] In conjunction with the first aspect, in one implementation, the coordinate parameters include: the UAV's GPS coordinates, the signal strength when transmitting the GPS coordinates, and the round-trip time; the GPS spoofing attack detection operation includes: The round-trip delay is used to calculate the round-trip distance between the base station and the UAV, and the logarithmic distance path loss model is called to calculate the inverse distance value based on the signal strength; The round-trip distance value and the inverted distance value are weighted and fused based on the preset distance error variance to obtain the physical layer estimated distance value; If the physical layer estimated distance value and the Euclidean distance value corresponding to the GPS coordinates do not meet the preset distance value, then the communication node of the UAV corresponding to the coordinate parameters is determined to be a suspect node.

[0010] In conjunction with the first aspect, in one implementation, the detection operation of the network availability attack detection includes: The forwarding behavior of the forwarding reports is cross-compared to obtain the forwarding scenario; The node packet loss rate is obtained based on the forwarding scenario, wherein the node packet loss rate is the packet loss rate of nodes that are unavailable within a preset buffer time. If the packet loss rate of a node is greater than the adjacent packet loss rate, then the communication node corresponding to the packet loss rate of the node is determined to be a suspect node, wherein the adjacent packet loss rate is the average packet loss rate of the adjacent nodes of the communication node corresponding to the packet loss rate of the node.

[0011] In conjunction with the first aspect, in one implementation, the step of calculating the global threat level of the suspected node using a belief function and determining a corresponding routing security decision scheme based on the global threat level to perform security control processing on the UAV communication network includes: The real-time observation data of the suspected node is obtained, and the attack and defense utility matrix is ​​determined and the preset belief function is updated using the real-time observation data. The local threat level is determined using the updated belief function and the attack-defense utility matrix, and the global threat level is calculated using the local threat level. The node type of the suspected node is determined based on the comparison result between the global threat level and the preset level threshold. The corresponding routing security decision scheme is determined based on the node type, and the UAV communication network is subjected to security control processing according to the routing security decision scheme.

[0012] In conjunction with the first aspect, in one implementation, acquiring real-time detection data of the UAV through the UAV communication network includes: A drone communication network is constructed based on the routing architecture of the delay-tolerant and disconnection-tolerant network, and the number of maintenance and activation time slots of the drone communication network is obtained. If the number of maintenance activation time slots is less than the preset maximum number of activation time slots, an activation command is sent to the drone based on the drone communication network, so that the drone can collect and feed back real-time detection data based on the activation command. The preset maximum number of activation time slots is adaptively adjusted according to the number of drones deployed in the drone communication network.

[0013] A second aspect of the present invention provides a secure routing control device for an unmanned aerial vehicle (UAV) communication network, the device comprising: The acquisition module is used to acquire real-time detection data of the UAV through the UAV communication network. The real-time data includes inspection data of the UAV sensor inspection task, coordinate parameters of the UAV, and forwarding reports forwarded by the UAV to the base station. The detection module is used to perform attack detection processing based on the inspection data to obtain suspected nodes of the UAV communication network. The attack detection processing includes data integrity attack detection, GPS spoofing attack detection, and network availability attack detection. The control module is used to calculate the global threat level of the suspected node using a belief function, and determine the corresponding routing security decision scheme based on the global threat level to perform security control processing on the UAV communication network.

[0014] A third aspect of this invention provides a secure routing and control system for an unmanned aerial vehicle (UAV) communication network, the system comprising: a base station, a plurality of relay nodes, and multiple UAVs; The base station is connected to several relay nodes and multiple drones to form a drone communication network. The base station executes the secure routing control method for the UAV communication network as described above.

[0015] Compared to existing technologies, the present invention provides a secure routing control method, apparatus, system, device, and medium for unmanned aerial vehicle (UAV) communication networks. Its advantages include: acquiring real-time detection data of UAVs through the UAV communication network; performing data integrity attack detection, GPS spoofing attack detection, and network availability attack detection based on the inspection data to identify suspected nodes in the UAV communication network; calculating the global threat level of the suspected nodes using a belief function; and determining the corresponding routing security decision scheme based on the global threat level to perform security control processing on the UAV communication network. The present invention can acquire UAV data in real time and perform multiple different detections on the data uploaded by the UAVs. This not only allows for identification and processing for different scenarios but also reduces detection errors, avoids misjudging "normal communication anomalies" as attacks, improves detection accuracy, and allows for subsequent control based on attack types to better match the real-time communication status and improve control precision. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a secure routing control method for an unmanned aerial vehicle (UAV) communication network according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the communication network architecture and deployment of a UAV power distribution inspection system according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a threat level assessment process based on a belief function provided in an embodiment of the present invention; Figure 4 This is an operation flowchart of a secure routing control method for an unmanned aerial vehicle (UAV) communication network provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a secure routing control device for an unmanned aerial vehicle (UAV) communication network according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of a secure routing and control system for an unmanned aerial vehicle (UAV) communication network provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] With the rapid development of Unmanned Aerial Vehicle (UAV) technology, autonomous inspection of power distribution lines using UAVs equipped with sensors such as visible light cameras, infrared thermal imagers, and lidar has become a mainstream technology in the power industry. In power distribution inspection missions, multiple UAVs form a formation, communicate with each other through a wireless ad hoc network, and forward the collected inspection data (such as information on abnormal line temperatures, insulator damage, and obstructions in the transmission line) to a remote analysis center for processing via a base station.

[0019] However, drone communication networks are susceptible to various cyberattacks, which can lead to the failure of power distribution inspection tasks and potentially cause safety hazards to the power system. Therefore, security detection and control are necessary. One common approach is to rely on dedicated intrusion detection systems. For example, using real-world drone network intrusion detection datasets such as UAV-NIDD, and targeting the vulnerability characteristics of drone-specific communication protocols like MAVLink, a customized intrusion detection system can be built to cover the identification needs of various drone-specific network attacks, such as black hole attacks and DNS attacks. Security control can then be implemented based on the identification results.

[0020] However, the detection method based on the standard intrusion detection system has the following technical problems: Although the intrusion detection system can identify and process different scenarios, the signature detection is too broad in its rules, which can easily misjudge "normal communication anomalies" as attacks, resulting in low detection accuracy. This makes it difficult for subsequent control to adapt to the actual communication status, and the control may be biased.

[0021] To address the aforementioned issues, the following detailed description and explanation will be provided through specific embodiments of a secure routing control method, apparatus, system, device, and medium for an unmanned aerial vehicle (UAV) communication network provided in this application.

[0022] To address the technical problem that existing technologies have omissions and biases in attack identification, leading to errors in subsequent control and low control accuracy, this paper refers to... Figure 1 The diagram shows a flowchart of a secure routing control method for an unmanned aerial vehicle (UAV) communication network according to an embodiment of the present invention.

[0023] In one embodiment, the secure routing control method for the UAV communication network can be applied to base stations communicating with UAVs.

[0024] As an example, the secure routing control method for the UAV communication network may include: S11. Obtain real-time detection data of the UAV through the UAV communication network, wherein the real-time data includes inspection data of the UAV sensor inspection task, coordinate parameters of the UAV, and forwarding reports forwarded by the UAV to the base station.

[0025] In one embodiment, real-time detection data fed back by the UAV can be obtained through the UAV communication network. The real-time data includes inspection data from the UAV sensor inspection task, the coordinate parameters of the UAV, and forwarding reports from the UAV to the base station.

[0026] In a specific operation, the inspection data can be multi-dimensional data such as line temperature, insulator damage, and channel obstacles collected by the drone's sensors, and the drone can periodically send it to the base station.

[0027] In a specific operation, the coordinate parameters of the UAV may include the GPS coordinates fed back by the UAV and related coordinate parameters.

[0028] In a specific operation, the forwarding report can be a forwarding log reported by the drone after it enters the communication range of the base station.

[0029] As an example, the step of acquiring real-time detection data of the UAV through the UAV communication network may include the following sub-steps: S111. Construct a UAV communication network based on the routing architecture of the delay-tolerant and disconnection-tolerant network, and obtain the number of maintenance and activation time slots of the UAV communication network.

[0030] S112. If the number of maintenance activation time slots is less than the preset maximum number of activation time slots, an activation command is sent to the drone based on the drone communication network so that the drone can collect and feed back real-time detection data based on the activation command. The preset maximum number of activation time slots is adaptively adjusted according to the number of drones deployed in the drone communication network.

[0031] Reference Figure 2 The diagram illustrates the architecture and deployment of a UAV power distribution inspection communication network according to an embodiment of the present invention.

[0032] In one embodiment, a drone communication network can be constructed, which is a drone power distribution inspection communication network.

[0033] Specifically, such as Figure 2As shown, the UAV power distribution inspection communication network in this embodiment consists of the following components: multiple UAV nodes equipped with inspection sensors, one or more base stations, and several mobile ground relay nodes. Each UAV node performs autonomous flight missions within the power distribution line inspection area, collecting line status data through onboard sensors such as visible light cameras, infrared thermal imagers, or lidar.

[0034] The drone nodes communicate with each other via a wireless ad hoc network, with communication links including links between drones and between drones and base stations. Secure communication between the base station and the remote analysis center is achieved via a wired link and the Transport Layer Security (TLS) protocol.

[0035] Because nodes in a drone communication network are constantly moving and the network topology changes frequently, persistent connections between nodes are difficult to guarantee, and communication delays and link drops are common. Therefore, this embodiment adopts a Delay-Tolerant Network (DTN) routing architecture, using a "store-carry-forward" mechanism for data transmission. When a drone detects an inspection event, it forwards the data to the next-hop node (drone or base station) or mobile ground relay node using a greedy forwarding mechanism. If no next-hop node is currently available, the data is temporarily stored in a buffer, waiting for a forwarding opportunity within a preset time T, and then removed from the buffer after the timeout.

[0036] Mobile ground relay nodes are dynamically deployed to specific locations to assume a relay role when the next-hop node is unavailable, thereby improving packet delivery rate and reducing packet loss rate.

[0037] Next, you can deploy a distributed intrusion detection system (IDS) agent and set up monitoring slots.

[0038] Specifically, an IDS agent is deployed on each drone node, enabling it to activate promiscuous mode to monitor the communication behavior of neighboring nodes within its wireless communication range. The IDS agent observes the behavioral patterns of neighboring nodes by listening to all data packets passing through its radio range.

[0039] To conserve the energy resources of drones, this embodiment divides the monitoring time into several equal-length time slots. Within each time slot, the IDS agent is in either active mode (performing monitoring) or waiting mode (energy-saving hibernation). Let K be the maximum number of active time slots for the IDS agent, and the value of K is adaptively adjusted based on the number of drones in the network.

[0040] In one specific implementation, K is set to 12 when there are 100 drones; K is set to 18 when there are 200 drones; and K is set to 25 when there are 300 drones. Increasing the value of K can improve detection accuracy (reduce false alarm rate and false negative rate), but it will also increase energy consumption. Therefore, a trade-off needs to be made between detection accuracy and energy consumption.

[0041] When an IDS proxy node is detected as a suspicious node (GTL belongs to [0.3, 0.6)) or an attacker node (GTL belongs to [0.6, 1]), its monitoring privileges are immediately revoked, and it is no longer allowed to act as a monitoring node.

[0042] Furthermore, the deployment of a distributed IDS proxy includes the following steps: The first step is to deploy an IDS agent on each drone node, enabling it to activate promiscuous mode and monitor the communication behavior of neighboring nodes.

[0043] The second step is to divide the monitoring time into multiple equal-length time slots, with the IDS agent in either active or waiting mode within each time slot, in order to achieve a balance between detection accuracy and energy consumption.

[0044] This invention cancels the monitoring permissions of IDS proxies detected as suspicious nodes, avoiding the problem of compromised monitoring nodes providing false detection results and improving the reliability of the security system itself.

[0045] It should be noted that the switching between the IDS agent's active and waiting modes is achieved through a time slot counting and threshold comparison mechanism, as follows: The monitoring time is divided into a series of equal-length time slots. The IDS agent locally maintains an active time slot counter c (initially 0) to obtain the number of active time slots for the UAV communication network. At the beginning of each new time slot, the agent compares the current counter c with the maximum number of active time slots K set in step S23: if c is less than K, the agent enters (or remains in) active mode, opens the promiscuous mode listening interface, collects the communication behavior of neighboring nodes and accumulates observation samples, and increments the counter c by 1; if c is greater than or equal to K, the agent switches to waiting mode, closes the listening interface to reduce energy consumption, and no new observation samples are collected in this mode, only the existing belief function state is maintained. When a monitoring cycle ends (i.e., the counter reaches the total number of time slots in this cycle), the counter c is cleared to zero, and the agent re-enters active mode to start the next cycle of cyclic listening.

[0046] Furthermore, the mode switching also supports early response to event triggers: when the base station detects an abnormally high message drop rate for a drone (including the node where the IDS agent itself is located) based on subsequent steps, or determines that its global threat level has entered a suspicious range based on subsequent steps, the base station can issue a wake-up command to the neighboring IDS agents, causing the agents in waiting mode to switch back to active mode in advance, temporarily increasing the monitoring density of the suspected node (i.e., combined with the subsequent step of "increasing the number of monitoring time slots K"). This hybrid (time slot counting-driven as the main method, and abnormal event triggering as the auxiliary method) switching mechanism ensures detection accuracy while taking into account the limited energy resources of drones.

[0047] The third step is to set the maximum number of active time slots K for the IDS agent to obtain the preset maximum number of active time slots. The value of K is adaptively adjusted according to the number of drones deployed in the network to ensure that a low false alarm rate and false negative rate can be obtained in networks of different sizes.

[0048] In one embodiment, the adaptive adjustment of the preset maximum number of active time slots K is as follows: 1) During the network deployment or operation phase, the base station counts the number of drones currently actually on the network (n) through node registration / heartbeat messages; 2) The base station determines the K value that matches the current network scale based on the "number of UAVs - maximum number of active time slots" mapping relationship calibrated in advance through offline simulation experiments. This mapping relationship comprehensively evaluates the trade-off between the false alarm rate, false negative rate and IDS energy consumption under different network scales. Table 2 of the manual gives representative calibration points: K=12 when n=100, K=18 when n=200, and K=25 when n=300. That is, the K value increases with the increase of network scale to ensure that sufficient observation samples can still be obtained in large-scale networks, thereby maintaining a low false alarm rate and false negative rate. 3) For intermediate-sized networks not directly given in the table (such as n=150), use linear interpolation to calculate the corresponding K value between adjacent calibration points to ensure that the K value increases monotonically with n; 4) The base station will send the determined K value to the IDS agent of each drone node in the network through a broadcast message. Each agent will update the local active time slot count limit accordingly, which will be used for the mode switching judgment in step S22. 5) During network operation, if the number of drones changes due to the addition of new nodes or the exit of existing nodes (due to power depletion, removal, or removal from the network by nodes identified as attackers, etc.), the base station will recount the current number of drones n′ according to a preset period (such as at the end of each monitoring period) and repeat steps 2)–4) to achieve dynamic adaptive update of the K value. 6) Based on this, a closed-loop feedback fine-tuning is introduced: The base station continuously tracks the actual false alarm rate, false negative rate and other detection performance indicators obtained from subsequent steps. If the observed false alarm rate / false negative rate is higher than the preset acceptable threshold, the K value is appropriately increased to increase the observation sample and improve detection accuracy. If the detection performance is already significantly sufficient but the energy consumption exceeds the budget, the K value is appropriately decreased to save energy, thereby maintaining a dynamic balance between detection accuracy and energy consumption.

[0049] This invention employs a distributed IDS architecture and an adaptive time-slot monitoring mechanism, which can achieve a good balance between detection accuracy and UAV energy consumption, and is suitable for resource-constrained UAV power distribution inspection communication network environments.

[0050] In an optional embodiment, when an IDS agent is detected as a suspicious node, its monitoring privileges are revoked, and it is no longer allowed to act as a monitoring node, in order to prevent compromised nodes from providing false detection results.

[0051] S12. Based on the inspection data, perform attack detection processing to obtain suspected nodes of the UAV communication network, wherein the attack detection processing includes data integrity attack detection, GPS spoofing attack detection and network availability attack detection.

[0052] In one embodiment, data integrity attack detection can be performed based on inspection data, GPS spoofing attack detection can be performed based on the coordinate parameters of the UAV, and network availability attack detection can be performed based on the forwarding reports forwarded by the UAV to the base station.

[0053] By conducting joint detection of multiple attacks, all typical threats to the network can be covered, thereby reducing the probability of false alarms and errors, and thus improving the accuracy of subsequent control.

[0054] In an optional embodiment, the data integrity attack detection operation may include the following sub-steps: S21. Determine the mean vector and covariance matrix based on the inspection data.

[0055] S22. Calculate the data deviation value using the mean vector and the covariance matrix. The data deviation value is the Mahalanobis distance between the inspection data and the normal state data uploaded by the UAV.

[0056] S23. If the deviation value of the data is greater than the preset deviation threshold, the communication node of the UAV corresponding to the inspection data is determined to be a suspected node. The preset deviation threshold is an adaptive threshold updated based on the historical data of the UAV using machine learning.

[0057] In one embodiment, the goal of a data integrity attack is to maliciously tamper with inspection data collected by drone sensors, causing the compromised drone to transmit false information to the base station, thereby compromising the authenticity of the power distribution inspection data.

[0058] In one operational embodiment, the data integrity attack detection process is performed at the base station level, and the specific steps are as follows: Each drone node During the inspection, sensors collect inspection data on the status of power distribution lines and periodically forward the inspection data to the base station.

[0059] Set up drones The reported data sequence is ={ , , ..., }, where n is the dimension of the data reported by the drone. The data set of all drones in the same area is} ={ , ,..., }, where k is the number of drones in the area.

[0060] Based on the prior knowledge that drones within the same inspection area should observe the same or similar physical phenomena, the base station calculates the mean vector of all data reported by drones within that area. Sum of variance-covariance matrix The calculation formula is as follows: ; ; Next, the Mahalanobis distance can be used to measure the deviation between the data reported by each UAV and the normal data distribution, thus obtaining the data deviation value. For UAVs... Reported data The Mahalanobis distance is calculated as follows: ; in, This represents the degree of data deviation.

[0061] When the data deviates from the value Preset deviation threshold At that time, the judgment The reported data was abnormal, and the drone was marked as a suspected node for data integrity attack detection. A preset deviation threshold is set, which can be an adaptive threshold using machine learning or a threshold updated based on the drone's historical data.

[0062] Specifically, a preset deviation threshold is set. The value of is affected by factors such as wireless communication noise, sensor measurement error and environmental interference, and its optimal value changes dynamically with time and environmental conditions.

[0063] In one implementation, a machine learning-based adaptive threshold determination method can be used to train a classifier using features of historical data (such as data dimension, data type, and acquisition environment) to dynamically update a preset deviation threshold. .

[0064] This invention employs a data integrity attack detection strategy based on Mahalanobis distance. It identifies malicious nodes that inject false data by measuring the statistical deviation between the data reported by each UAV and the normal data distribution in the same area. Compared with detection methods based on fixed rules, it can adaptively cope with different types of data tampering attacks.

[0065] In one embodiment, the coordinate parameters include: the UAV's GPS coordinates, the signal strength when transmitting the GPS coordinates, and the round-trip delay; As an example, the detection operation for GPS spoofing attacks may include the following sub-steps: S31. Calculate the round-trip distance between the base station and the UAV using the round-trip delay, and calculate the inversion distance value based on the signal strength using the logarithmic distance path loss model.

[0066] S32. The round-trip distance value and the inverted distance value are weighted and fused based on the preset distance error variance to obtain the physical layer estimated distance value.

[0067] S33. If the physical layer estimated distance value and the Euclidean distance value corresponding to the GPS coordinates do not meet the preset distance value, then the communication node of the UAV corresponding to the coordinate parameters is determined to be a suspected node.

[0068] In one embodiment, the GPS coordinates of each drone node can be verified. Malicious nodes may broadcast false GPS coordinates to cause drones to collide or deviate from the inspection route.

[0069] This embodiment employs a location verification method based on signal strength and round-trip time (RTT) to identify GPS spoofing attacks by comparing the consistency between the location claimed by the UAV node and the location estimated based on physical layer information.

[0070] Specifically, GPS spoofing attack detection is achieved by comparing the consistency between the "claimed location" and the "physical layer estimated location." The physical layer estimated location is jointly calculated by the received signal strength (RSS) and round-trip time (RTT). The specific identification steps are as follows: 1) Location claim collection: When each UAV node sends inspection data or forwards reports to the base station or neighboring nodes, it also attaches its claimed coordinates and timestamps calculated by the onboard GPS. 2) Physical layer parameter measurement: When receiving the signal from the node, the verifier (base station or several trusted neighbor IDS agents) simultaneously measures two types of physical layer parameters: received signal strength RSS and round-trip time RTT measured by sending probe frames and receiving their response frames. 3) Distance estimation based on RTT: based on the propagation speed of electromagnetic waves By comparing the measured round-trip time (RTT) with the actual geometric distance between the verification party and the tested node (denoted as the RTT estimated distance), the round-trip distance value is obtained. Specifically, the formula for calculating the round-trip distance value is as follows: ; in, The node response processing delay is pre-calibrated and compensated. 4) RSS-based distance estimation and cross-validation: A logarithmic distance path loss model is used to characterize the attenuation of received signal strength with distance, i.e. ,in For reference distance The pre-calibrated known receiving power, The path loss index for this inspection environment is used; the estimated distance of RSS is obtained by inverting the measured received signal strength, and the inverted distance value is obtained.

[0071] Specifically, the formula for calculating the inversion distance value is as follows: .

[0072] Then, it is cross-validated with the RTT estimated distance obtained in step 3), and the relative deviation is calculated. :like Less than the set consistency threshold If this indicates that the two independent ranging methods corroborate each other, then the measurements are accepted and the process proceeds to step 6) weighted fusion; if If the value is too large, it indicates that a certain path may be affected by multipath, blockage, or interference. In this case, the measurement is deemed unreliable, triggering a remeasurement or temporarily reducing the weight of that path in the fusion, thereby suppressing random errors and outliers in a single measurement. 5) Claimed distance calculation: The Euclidean distance between the claimed coordinates of the tested node and the known coordinates of the verifying party (the base station coordinates are known, and the coordinates of trusted neighbors are mutually verified) is calculated and denoted as the claimed distance. ; 6) Weighted fusion and consistency residual determination: The two ranging results are weighted and fused according to the inverse variance (inverse variance) based on the measurement accuracy to obtain the estimated distance value of the physical layer.

[0073] Specifically, the formula for calculating the estimated distance value at the physical layer is as follows: ; in, , The error variances of RTT ranging and RSS ranging are respectively (the more accurate the ranging and the smaller the variance, the greater its weight in the fusion; if step 4 determines that a certain method is unreliable, its variance is increased accordingly to reduce its weight).

[0074] Next, if the physical layer estimated distance value and the Euclidean distance value corresponding to the GPS coordinates do not meet the preset distance value, the communication node of the UAV corresponding to the coordinate parameters is determined to be a suspect node.

[0075] Specifically, the residual value can be calculated using the physical layer estimated distance value and the Euclidean distance value corresponding to the GPS coordinates. The formula for calculating the residual value is as follows: ; When the residual Exceeding the tolerance threshold determined by ranging noise and channel fluctuations If the node's claimed location is inconsistent with its actual physical location, it will be marked as a suspected GPS spoofing attack node.

[0076] It should be noted that multi-verifier cross-localization (enhancement) can also be performed: when there are three or more trusted verifiers at the same time, multi-lateration is performed on the measured node based on the distances measured by each verifier to calculate its physical estimated coordinates, and then compared with the claimed coordinates to further reduce misjudgment and improve the reliability of localization. The aforementioned consistency determination result serves as a form of observational evidence. It is then incorporated into the corresponding IDS agent's belief function update and local threat level (TL) calculation for that node in subsequent steps. This result, along with data integrity detection and message drop rate detection results, participates in the converged evaluation of the global threat level (GTL), enabling collaborative determination of GPS spoofing attacks and other attack types.

[0077] In one embodiment, the network availability attack detection operation may include the following sub-steps: S41. Cross-compare the forwarding behavior of the forwarding report to obtain the forwarding scenario.

[0078] S42. Obtain the node packet loss rate based on the forwarding scenario, wherein the node packet loss rate is the packet loss rate of nodes that are unavailable within a preset buffer time.

[0079] S43. If the packet loss rate of the node is greater than the adjacent packet loss rate, then the communication node corresponding to the packet loss rate of the node is determined to be a suspect node, wherein the adjacent packet loss rate is the average packet loss rate of the adjacent nodes of the communication node corresponding to the packet loss rate of the node.

[0080] In one embodiment, the goal of a network availability attack is to disrupt the normal operation of the DTN routing protocol, causing packet loss. The most typical form of attack is a wormhole attack, in which two malicious drone nodes establish a virtual tunnel, guide legitimate drone packets into the tunnel, and then discard them.

[0081] Specifically, the network availability attack detection process in this embodiment is executed at the base station level, and the specific steps are as follows: When each drone node enters the base station's communication range, it sends a forwarding report to the base station. The forwarding report includes node type information (source node, relay node, or destination node) as well as the identifiers of its claimed next-hop node and previous-hop node.

[0082] After collecting forwarding reports from each drone node, the base station verifies the forwarding behavior through cross-comparison to obtain the forwarding scenario, which can be an anomaly detection scenario. Specific anomaly detection scenarios include: Scenario 1: Relay node B claims to have forwarded the data packet to destination node C, but C reports that it has not received the data packet from B. In this case, the message drop rate (MDR) of both B and C is increased. Scenario 2: Attacking node B claims to be the source node, but the real source node A reports that it has forwarded the data packet to B. In this case, B's MDR is increased. Scenario 3: Relay node B claims that it has not received data packets from source node A, but A reports that it has sent data packets to B. In this case, B's MDR is increased.

[0083] Next, the message drop rate (MDR) of each relay node can be calculated based on the verification results, specifically including the following abnormal scenarios: when relay node B claims to have forwarded a data packet to destination node C but C did not receive it, the MDR of B and C are increased; when attacking node B claims to be the source node but the real source node A claims to have forwarded it to B, the MDR of B is increased; when relay node B claims not to have received the data packet from source node A but A claims to have sent it to B, the MDR of B is increased. In a DTN routing environment, since the next-hop node is not always available, normal nodes may also drop packets due to buffer timeout. However, the normal packet loss rate is significantly lower than the packet loss rate caused by wormhole attacks. Therefore, drone nodes with MDR significantly higher than the average level of neighboring nodes are marked as suspected nodes of wormhole attacks.

[0084] Specifically, in a DTN routing environment, due to the characteristics of the store-and-forward mechanism, when the cache time T expires and the next-hop node is still unavailable, normal nodes will also drop data packets. However, the packet loss rate of normal nodes is significantly lower than that caused by wormhole attacks. Therefore, drones with a MDR significantly higher than the average level of their neighboring nodes are marked as suspected nodes of wormhole attacks.

[0085] This invention employs a network availability attack detection strategy based on message packet loss rate. By cross-referencing forwarding reports, it identifies malicious behaviors such as wormhole attacks targeting the DTN routing protocol, effectively compensating for the shortcomings of existing intrusion detection systems in detecting routing layer attacks.

[0086] In an optional embodiment, after a drone node is marked as a suspected node (whether triggered by data integrity anomaly detection or message drop rate (MDR) anomaly detection), the following processes will be performed in sequence: 1) This anomaly marker is used as observational evidence and fed back to the threat level assessment model: it is included as an anomalous behavior observation sample in the belief function update of the corresponding IDS agent regarding the target UAV, and is used to calculate the local threat level TL of the agent to the target node; 2) The base station continuously aggregates the local threat level (TL) reported by each IDS agent of the target node and updates its global threat level (GTL); 3) Based on the classification rules of subsequent steps, the node's category is re-determined based on the latest GTL: If the GTL falls within the range of [0.3, 0.6), the node is officially added to the "suspicious list," triggering the processing defined in subsequent steps (reducing its routing forwarding priority weight and increasing the monitoring frequency of the node; see the subsequent replies on priority adjustment and relay selection for specific steps); if the GTL further increases to the range of [0.6, 1], the node is upgraded to an attacker node, triggering the processing of subsequent steps (completely removed from the routing path, prohibited from participating in any data forwarding, and a security alert is sent to the remote analysis center, the alert content including node identifier, attack type, GTL value, and detection timestamp). 4) In terms of increasing the monitoring frequency, the base station can trigger the neighboring IDS agent to temporarily switch from the waiting mode back to the active mode in advance, or temporarily increase the number of monitoring time slots K corresponding to the suspected node (see the corresponding answer in the above steps for the specific mechanism) to obtain more observation samples for further confirmation or elimination of the node's suspicion in subsequent time slots. 5) If the suspected node itself plays the role of IDS proxy monitoring, its monitoring privileges will be revoked and its reported observation data will no longer be accepted, in order to prevent compromised nodes from providing false detection results and interfering with the accuracy of the overall threat assessment. 6) The base station records the abnormal feature data corresponding to the suspected event (such as MDR anomalies, Mahalanobis distance anomalies, occurrence timestamps, etc.) into its local log, which serves as the basis for subsequent continuous updates and retrospective analysis of the belief function. Since GTL can dynamically increase or decrease with subsequent observations, if the node's subsequent behavior returns to normal and the relevant abnormal indicators fall back, its GTL will decrease accordingly and can fall back to the [0, 0.3) interval, thereby removing the suspected label and restoring it to a normal node. This reflects the dynamic reversibility of the threat level determination mechanism in this application and avoids "killing" a node after misjudging it.

[0087] S13. Calculate the global threat level of the suspected node using a belief function, and determine the corresponding routing security decision scheme based on the global threat level to perform security regulation and control on the UAV communication network.

[0088] In one embodiment, threat level quantification assessment can be performed based on a belief function to calculate the global threat level of a suspected node.

[0089] Next, based on the global threat level, a corresponding routing security decision scheme can be determined to perform security control and management of the UAV communication network. Specifically, differentiated processing can be carried out according to the global threat level, coupled with a dynamic scheduling mechanism for ground mobile relays, to perform security control and management of the UAV communication network.

[0090] This invention deeply integrates intrusion detection results with routing decisions, dynamically adjusting routing priorities and path selection based on the global threat level of nodes. This achieves security-aware-driven intelligent routing optimization, ensuring the security and reliability of power distribution inspection data transmission.

[0091] In one embodiment, the step of calculating the global threat level of the suspected node using a belief function and determining the corresponding routing security decision scheme based on the global threat level to perform security regulation processing on the UAV communication network may include the following sub-steps: S131. Obtain real-time observation data of the suspected node, and use the real-time observation data to determine the attack and defense utility matrix and update the preset belief function.

[0092] S132. Determine the local threat level using the updated belief function and the attack-defense utility matrix, and calculate the global threat level using the local threat level.

[0093] S133. Determine the node type of the suspected node based on the comparison result between the global threat level and the preset level threshold.

[0094] S134. Determine the corresponding routing security decision scheme according to the node type, and perform security control processing on the UAV communication network according to the routing security decision scheme.

[0095] Reference Figure 3 The diagram illustrates a threat level assessment process based on a belief function, according to an embodiment of the present invention.

[0096] like Figure 3 As shown, due to the uncertainty in the IDS agent's determination of the target drone (i.e., it cannot be completely determined whether the target node is an attacker or a normal node), this embodiment uses a threat level assessment method based on a belief function to accurately quantify the threat level of the node. The specific steps are as follows: IDS agent observes target drones The behavior is observed, and the belief function is updated based on the observations. The formula for updating the belief function is: ; in, >0 represents the belief value for the Nth time slot. For a given target drone type The observation probability under (normal mode or malicious mode) conditions, N={1, ..., K}, where K is the maximum number of activation slots of the IDS agent.

[0097] Construct a belief matrix (as shown in Table 1). The belief matrix includes two dimensions: the target drone's behavioral pattern (malicious, normal) and the IDS agent's judgment action (judging it as an attacker, judging it as normal). Let x be the probability that the target drone is a malicious node, and y be the probability that the IDS agent judges it as an attacker. The utility value is defined as follows: (The attacker's utility when malicious nodes are correctly detected) (The utility of IDS when malicious nodes are correctly detected) (The attacker's utility when malicious nodes are not detected) (The effectiveness of IDS when malicious nodes are not detected, i.e., penalty for missed detection) (The attacker's utility when a normal node is mistakenly identified as an attacker) (The utility of IDS when a normal node is misjudged, i.e., the penalty for false positives) (The attacker's utility when a normal node is correctly identified as normal) (The utility of IDS when normal nodes are correctly identified as normal) in, and The gains for the attacker and the IDS, respectively. Penalty for incorrect IDS judgments and These represent the costs of attack and detection, respectively, and the values ​​of these parameters all range from [0, 1].

[0098] Table 1 below shows the belief matrix: utility values. Calculate the expected utility function of the IDS agent based on the belief matrix. : ; The goal of an IDS agent is to maximize its utility function by selecting an appropriate detection strategy, i.e., to find the optimal solution that satisfies: .

[0099] right about Find the first derivative and set it to zero to obtain the optimal detection probability. Specifically, it can be shown in the following formula: ; in .

[0100] IDS agent targets drone Local threat level That is, the optimal detection probability The local threat level can be obtained, as shown in the following formula: ; TL is composed of posterior belief functions The weighting depends primarily on the IDS agent and the target drone. The results of the interaction between them.

[0101] Finally, each IDS agent will calculate the... The data is reported to the base station, which then monitors all target drones. The threat levels are aggregated from the threat levels reported by the IDS agents to calculate the global threat level. Specifically, it can be shown in the following formula: ; Where s is the surveillance drone The number of IDS agents.

[0102] In one embodiment, the base station can target the drone based on the range of the Global Threat Level (GTL). The node type is determined by classifying it into the following three states, as shown below: when When ∈[0, 0.3), determine This is a normal node; when When ∈ [0.3, 0.6), determine This node is considered suspicious. when When ∈[0.6, 1], determine This is the attacker's node.

[0103] Table 2 below shows the optimal number of time slots K and detection performance for different numbers of drones. After determining the classification results of the above node types, a secure routing decision can be made based on the classification results.

[0104] Specifically, this embodiment deeply integrates intrusion detection results with DTN routing decisions, dynamically adjusting routing strategies based on the global threat level of nodes: If the node type is a normal node (GTL∈[0, 0.3)): it is allowed to participate normally in the greedy forwarding mechanism of the DTN routing protocol, and forward inspection data as a relay node. Its priority weight in routing selection remains at the default value.

[0105] If the node type is a suspicious node (GTL∈[0.3, 0.6)): reduce its forwarding priority weight in routing selection, so that the routing protocol will prioritize other normal nodes or mobile ground relay nodes as relays. At the same time, increase the IDS monitoring frequency of this node (i.e., increase the number of monitoring time slots K) to obtain more observation samples to further confirm its behavior pattern.

[0106] It should be noted that the steps for adjusting priority and selecting other normal nodes or mobile ground relay nodes as relays can be divided into "steps for adjusting priority" and "steps for selecting other normal nodes or mobile ground relay nodes as relays".

[0107] Specifically, the steps for adjusting priorities are as follows: 1) Base station / related drone nodes obtain the continuously updated Global Threat Level (GTL) of each node in the current candidate relay node set; 2) For suspicious nodes with GTL∈[0.3, 0.6), their routing priority weight is reduced according to a penalty coefficient positively correlated with the GTL value. The specific calculation method is as follows: Adjusted priority weight = Baseline priority weight × (1 - GTL), where the baseline priority weight is determined by the node under normal conditions based on factors such as its geographical proximity to the destination node / base station, remaining energy, and link quality; the higher the GTL value, the lower the reduced priority weight. 3) The updated priority weight is written to the corresponding field in the node's local routing table / neighbor forwarding candidate list; 4) When DTN greedy forwarding is performed to select the next hop, the available candidate relay nodes are sorted according to the updated priority weights. The node with higher weight (i.e. lower threat level) is selected as the next hop first. This makes the suspicious node "marginalized" in relay selection but not completely excluded. It retains its candidate status when there are no other available relays, and takes into account the robustness requirement of the delay-tolerant and interrupt-tolerant network that "having a relay is better than having no relay". 5) This priority weight is dynamically refreshed with the periodic updates of GTL, forming a closed-loop adjustment mechanism.

[0108] Specifically, the selection process for choosing other normal nodes or mobile ground relay nodes as relays is as follows: 1) The node currently holding the data packet (source node or relay node) scans all candidate next-hop nodes within its communication range, including nearby UAV nodes and mobile ground relay nodes; 2) Remove candidate nodes that have been identified as attacker nodes (GTL∈[0.6, 1]) so that they no longer participate in this relay selection; 3) The remaining candidate nodes (normal nodes, suspicious nodes, and mobile ground relay nodes) are sorted according to a comprehensive score. The scoring factors include: a) the node's current priority weight (as described in (I), which is negatively correlated with GTL); b) the node's geographical proximity to the destination node / base station (distance metric for greedy forwarding); c) the node's remaining energy and link quality. 4) Prioritize selecting the normal node with the highest score (GTL∈[0, 0.3)) as the next hop relay, since its priority weight is not reduced; 5) If there are no available normal UAV nodes within the current communication range (e.g., sparse formation, normal nodes not being in the data transmission forward direction, or normal nodes being temporarily in the mission obscuration area), then further determine whether there are mobile ground relay nodes within the communication coverage area: if so, include them in the candidates and select them as the next-hop relay after evaluation according to the same greedy forwarding distance criterion (corresponding to the mechanism described in the subsequent steps); since mobile ground relay nodes are deployed on ground infrastructure, relatively isolated from UAV-level intrusion behavior, and not easily compromised by similar attacks, their default priority weight is set to the highest level, and they are regarded as the most trustworthy relay source; 6) If both normal drone nodes and mobile ground relay nodes are unavailable, the suspicious node with the highest current score will be selected as a temporary relay to maintain data reachability under the "store-carry-forward" strategy of the delay-tolerant and interruptible network. At the same time, the forwarding event will be marked and included in the message drop rate verification closed loop as the observation basis for subsequent threat level assessment. 7) If no candidate nodes are available in steps 1)–6), the data packet is cached in the current node according to the DTN store-carry-forward mechanism, waiting for the next available relay window to appear, or it is discarded / rescheduled according to the rules after the cache time T expires.

[0109] If the node type is an attacker node (GTL∈[0.6, 1]): completely remove it from the routing path and prohibit it from participating in any packet forwarding. At the same time, send a security alert to the remote analysis center, including the attacker node identifier, attack type (data integrity attack or network availability attack), GTL value, and detection timestamp.

[0110] When the removal of an attacker node causes partial routing path disruption, mobile ground relay nodes are used as alternative relays. These mobile ground relay nodes are dynamically adjusted to areas with the highest communication coverage demand to ensure end-to-end reachability of inspection data. In extreme cases (when no relay nodes are available), the drone can initiate a store-and-carry mechanism, temporarily storing data in a local buffer and forwarding it only after entering the communication range of a base station or other normal nodes.

[0111] In one embodiment, the dynamic adjustment of mobile ground relay nodes to the "area with the greatest communication coverage demand" is centrally decided and executed by the base station. Its core is to construct the coverage demand distribution based on the real-time network status and solve for the optimal deployment location. The specific dynamic adjustment steps are as follows: 1) Network status collection: The base station collects the network operation status according to the monitoring cycle, including the current location and movement trend of each drone node, link connectivity status, the amount of inspection data to be forwarded back in the buffer, the data packet delivery rate, and the location of the route path that is broken due to the removal of the attacker node; 2) Coverage Demand Quantification: The inspection area is divided into several grid cells, and the communication coverage demand weight w is calculated for each grid cell. The value of w is positively correlated with the following factors: a) the number of routing paths interrupted in the area due to the removal of attacker nodes; b) the amount of inspection data backed up in the node buffers in the area and facing cache timeout; c) the generation rate of inspection service data in the area; and negatively correlated with the density of existing available normal relay nodes in the area, thereby highlighting the weak areas of "high demand, low coverage". 3) Demand heatmap construction: The demand weights of each grid cell are aggregated to form the communication coverage demand distribution (heatmap) of the entire area, where the grid cell with the highest demand weight is the target area with the greatest communication coverage demand; 4) Target location solution: Under the constraint that mobile ground relay nodes must maintain connectivity with base stations (or existing trusted relays), the optimal deployment coordinates of each mobile ground relay node are solved with the goal of maximizing coverage gain in high-demand areas and taking into account the repair of broken path connectivity. (A weighted centroid / greedy selection strategy can be adopted to place the relay at the weighted centroid of the demand heatmap). 5) Issuance of dispatch instructions: The base station issues the target coordinates obtained from the solution as a mobile dispatch instruction to the corresponding mobile ground relay node; 6) Relay migration and link reconstruction: The mobile ground relay node moves to the target location according to the instructions, re-establishes the communication link with the surrounding UAV nodes and base stations, takes over the relay forwarding task of the broken path, and restores the end-to-end reachability of the inspection data. 7) Closed-loop reassessment: After the migration is completed, the base station will re-collect the network status in the next monitoring cycle and repeat steps 1)–6). The location of the mobile ground relay node will be continuously and dynamically adjusted according to the changes in the attack situation, node distribution and service requirements, forming a closed loop of "requirement assessment - location optimization - migration execution - effect feedback".

[0112] In an optional embodiment, after the adjustment is completed, the network topology information and routing table can be updated to enter the next monitoring cycle.

[0113] Specifically, after completing one round of intrusion detection and secure routing decisions, the system updates network topology information (removing attacker nodes, marking suspicious nodes, and recording the current location of mobile ground relay nodes), updates the routing tables of each node (adjusting route priority weights and removing path entries leading to attacker nodes), and enters the next monitoring time slot. In the next monitoring time slot, the IDS agent continues to observe the behavior of neighboring nodes, updates belief functions and threat levels, and performs continuous intrusion detection and secure routing optimization.

[0114] By continuously repeating the above steps, this embodiment can provide dynamic security protection throughout the entire operation cycle of the UAV power distribution inspection communication network, promptly detect and isolate malicious nodes, and ensure the integrity of inspection data and network availability.

[0115] This invention employs a threat level assessment model based on belief functions. By collaboratively observing the behavior of target nodes through multiple IDS agents and converging to calculate the global threat level, it can effectively reduce the false alarm rate and false negative rate caused by a single detection node, thereby improving the accuracy and robustness of intrusion detection.

[0116] In one specific implementation, the system simulation verification of this embodiment adopts the following parameter configuration: Table 3 below shows the simulation parameter configurations. Simulation results show that, under conditions where the number of drones increases from 50 to 250 and the proportion of attackers increases from 10% to 30%, the method in this embodiment can maintain a detection rate of over 93% and a false alarm rate of less than 3%, demonstrating higher detection accuracy and better scalability compared to existing specification-based intrusion detection methods.

[0117] The improved detection performance is mainly attributed to the following two aspects: First, the collaborative detection mechanism in this embodiment can simultaneously detect data integrity attacks and network availability attacks, covering a wider range of attack types; Second, the threat level assessment model based on belief functions effectively reduces the judgment bias of a single detection node and significantly improves the accuracy of threat assessment through collaborative observation and belief convergence of multiple IDS agents.

[0118] Reference Figure 4 The diagram illustrates an operation flowchart of a secure routing control method for a drone communication network according to an embodiment of the present invention.

[0119] Specifically, the operational procedure for the secure routing control method of the UAV communication network is as follows: The first step is to construct a communication network topology model for UAV power distribution inspection, set up UAV nodes, base stations and mobile ground relay nodes, and adopt a delay-tolerant and disconnection-tolerant network (DTN) routing architecture to realize the storage, carrying and forwarding transmission of power distribution inspection data; The second step is to deploy a distributed intrusion detection system (IDS) agent on the drone node, set up monitoring time slots, and activate promiscuous mode to listen to the communication behavior of neighboring nodes. The third step is to perform data integrity attack detection. The Mahalanobis distance is used to measure the deviation between the inspection data reported by each UAV and the normal data distribution in the same area, to identify malicious nodes that inject false data, and to perform GPS coordinate verification to detect GPS spoofing attacks. The fourth step is to perform network availability attack detection, which involves analyzing the message drop rate (MDR) of each relay node to detect malicious behaviors such as wormhole attacks against the DTN routing protocol. The fifth step is to build a threat level assessment model based on the belief function. Each IDS agent calculates the belief function and local threat level (TL) by observing the behavior of the target drone. The base station aggregates the local threat levels reported by each agent to calculate the global threat level (GTL). Based on the GTL threshold, the drone nodes are classified as normal nodes, suspicious nodes, or attacker nodes. The sixth step is to make a secure routing decision based on the classification results of the drone nodes, allowing normal nodes to participate in routing forwarding, reducing the routing priority of suspicious nodes and strengthening monitoring, and isolating and removing attacker nodes from the routing path. Step 7: Update the network topology information and routing table, enter the next monitoring time slot, and repeat steps 2 to 6 to achieve continuous security protection for the communication network of the UAV power distribution inspection.

[0120] In this embodiment, the present invention provides a secure routing control method for a drone communication network. Its advantages are as follows: The present invention can acquire real-time detection data of drones through the drone communication network; based on the inspection data, it performs data integrity attack detection, GPS spoofing attack detection, and network availability attack detection to obtain suspected nodes in the drone communication network; it uses a belief function to calculate the global threat level of the suspected nodes, and determines the corresponding routing security decision scheme based on the global threat level to perform security control processing on the drone communication network. The present invention can acquire drone data in real time and perform multiple different detections on the data uploaded by the drone. This not only allows for identification and processing for different scenarios but also reduces detection errors, avoids misjudging "normal communication anomalies" as attacks, improves detection accuracy, and allows for subsequent control based on attack types to better match the real-time communication status and improve control accuracy.

[0121] This invention also provides a secure routing control device for unmanned aerial vehicle (UAV) communication networks, see [link / reference]. Figure 5 The diagram shows a structural schematic of a secure routing control device for an unmanned aerial vehicle (UAV) communication network according to an embodiment of the present invention.

[0122] As an example, the secure routing control device for the UAV communication network may include: The acquisition module 201 is used to acquire real-time detection data of the UAV through the UAV communication network. The real-time data includes inspection data of the UAV sensor inspection task, coordinate parameters of the UAV, and forwarding reports forwarded by the UAV to the base station. The detection module 202 is used to perform attack detection processing based on the inspection data to obtain suspected nodes of the UAV communication network. The attack detection processing includes data integrity attack detection, GPS spoofing attack detection and network availability attack detection. The control module 203 is used to calculate the global threat level of the suspected node using a belief function, and determine the corresponding routing security decision scheme based on the global threat level to perform security control processing on the UAV communication network.

[0123] Optionally, the detection operation for the data integrity attack detection includes: Based on the inspection data, the mean vector and covariance matrix are determined respectively; The mean vector and the covariance matrix are used to calculate the data deviation value, which is the Mahalanobis distance between the inspection data and the normal state data uploaded by the UAV. If the deviation value of the data is greater than the preset deviation threshold, the communication node of the UAV corresponding to the inspection data is determined to be a suspected node. The preset deviation threshold is an adaptive threshold updated based on the historical data of the UAV using machine learning.

[0124] Optionally, the coordinate parameters include: the UAV's GPS coordinates, the signal strength when transmitting the GPS coordinates, and the round-trip time; the GPS spoofing attack detection operation includes: The round-trip delay is used to calculate the round-trip distance between the base station and the UAV, and the logarithmic distance path loss model is called to calculate the inverse distance value based on the signal strength; The round-trip distance value and the inverted distance value are weighted and fused based on the preset distance error variance to obtain the physical layer estimated distance value; If the physical layer estimated distance value and the Euclidean distance value corresponding to the GPS coordinates do not meet the preset distance value, then the communication node of the UAV corresponding to the coordinate parameters is determined to be a suspect node.

[0125] Optionally, the detection operation for network availability attack detection includes: The forwarding behavior of the forwarding reports is cross-compared to obtain the forwarding scenario; The node packet loss rate is obtained based on the forwarding scenario, wherein the node packet loss rate is the packet loss rate of nodes that are unavailable within a preset buffer time. If the packet loss rate of a node is greater than the adjacent packet loss rate, then the communication node corresponding to the packet loss rate of the node is determined to be a suspect node, wherein the adjacent packet loss rate is the average packet loss rate of the adjacent nodes of the communication node corresponding to the packet loss rate of the node.

[0126] Optionally, the step of calculating the global threat level of the suspected node using a belief function and determining the corresponding routing security decision scheme based on the global threat level to perform security regulation processing on the UAV communication network includes: The real-time observation data of the suspected node is obtained, and the attack and defense utility matrix is ​​determined and the preset belief function is updated using the real-time observation data. The local threat level is determined using the updated belief function and the attack-defense utility matrix, and the global threat level is calculated using the local threat level. The node type of the suspected node is determined based on the comparison result between the global threat level and the preset level threshold. The corresponding routing security decision scheme is determined based on the node type, and the UAV communication network is subjected to security control processing according to the routing security decision scheme.

[0127] Optionally, acquiring real-time detection data of the UAV through the UAV communication network includes: A drone communication network is constructed based on the routing architecture of the delay-tolerant and disconnection-tolerant network, and the number of maintenance and activation time slots of the drone communication network is obtained. If the number of maintenance activation time slots is less than the preset maximum number of activation time slots, an activation command is sent to the drone based on the drone communication network, so that the drone can collect and feed back real-time detection data based on the activation command. The preset maximum number of activation time slots is adaptively adjusted according to the number of drones deployed in the drone communication network.

[0128] This invention also provides a secure routing and control system for unmanned aerial vehicle (UAV) communication networks, see [link / reference]. Figure 6 The diagram shows a structural schematic of a secure routing control system for an unmanned aerial vehicle (UAV) communication network according to an embodiment of the present invention.

[0129] As an example, the secure routing control system of the UAV communication network may include: a base station, several relay nodes, and multiple UAVs; The base station is connected to several relay nodes and multiple drones to form a drone communication network. The base station executes the secure routing control method for the unmanned aerial vehicle (UAV) communication network as described in the above embodiments.

[0130] Those skilled in the art will understand that, for ease of description and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0131] Furthermore, this application also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the secure routing control method for the UAV communication network as described in the above embodiments.

[0132] Furthermore, embodiments of this application also provide a computer-readable storage medium storing a computer-executable program, which is used to cause a computer to execute the secure routing control method for a drone communication network as described in the above embodiments.

[0133] In the description of the embodiments of the present invention, it should be noted that the terms "above," "below," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. When an element such as a layer, region, or substrate is referred to as being "above" or "on top of" another element, it may be directly on the other element, or there may be an intermediate element. Conversely, when an element is referred to as being "directly on" or "above" another element, there is no intermediate element. It should also be understood that when an element is referred to as being "below" or "under" another element, it may be directly below or under the other element, or there may be an intermediate element. Conversely, when an element is referred to as being "directly below" or "under" another element, there is no intermediate element. Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0134] Those skilled in the art will understand that embodiments of this application may also include computer program products. Therefore, this application may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), devices, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A secure routing control method for an unmanned aerial vehicle (UAV) communication network, characterized in that, The method includes: The drone communication network is used to acquire real-time detection data of the drone, including inspection data of the drone sensor inspection task, the drone's coordinate parameters, and forwarding reports from the drone to the base station. Based on the inspection data, attack detection processing is performed to obtain suspected nodes of the UAV communication network. The attack detection processing includes data integrity attack detection, GPS spoofing attack detection, and network availability attack detection. The global threat level of the suspected node is calculated using a belief function, and a corresponding routing security decision scheme is determined based on the global threat level to perform security regulation and control on the UAV communication network.

2. The secure routing control method for unmanned aerial vehicle (UAV) communication networks according to claim 1, characterized in that, The detection operations for the data integrity attack detection include: Based on the inspection data, the mean vector and covariance matrix are determined respectively; The mean vector and the covariance matrix are used to calculate the data deviation value, which is the Mahalanobis distance between the inspection data and the normal state data uploaded by the UAV. If the deviation value of the data is greater than the preset deviation threshold, the communication node of the UAV corresponding to the inspection data is determined to be a suspected node. The preset deviation threshold is an adaptive threshold updated based on the historical data of the UAV using machine learning.

3. The secure routing control method for unmanned aerial vehicle (UAV) communication networks according to claim 1, characterized in that, The coordinate parameters include: the UAV's GPS coordinates, the signal strength when transmitting the GPS coordinates, and the round-trip delay; the GPS spoofing attack detection operation includes: The round-trip delay is used to calculate the round-trip distance between the base station and the UAV, and the logarithmic distance path loss model is called to calculate the inverse distance value based on the signal strength; The round-trip distance value and the inverted distance value are weighted and fused based on the preset distance error variance to obtain the physical layer estimated distance value; If the physical layer estimated distance value and the Euclidean distance value corresponding to the GPS coordinates do not meet the preset distance value, then the communication node of the UAV corresponding to the coordinate parameters is determined to be a suspect node.

4. The secure routing control method for unmanned aerial vehicle (UAV) communication networks according to claim 1, characterized in that, The network availability attack detection operation includes: The forwarding behavior of the forwarding reports is cross-compared to obtain the forwarding scenario; The node packet loss rate is obtained based on the forwarding scenario, wherein the node packet loss rate is the packet loss rate of nodes that are unavailable within a preset buffer time. If the packet loss rate of a node is greater than the adjacent packet loss rate, then the communication node corresponding to the packet loss rate of the node is determined to be a suspect node, wherein the adjacent packet loss rate is the average packet loss rate of the adjacent nodes of the communication node corresponding to the packet loss rate of the node.

5. The secure routing control method for unmanned aerial vehicle (UAV) communication networks according to claim 1, characterized in that, The process of calculating the global threat level of the suspected node using a belief function and determining the corresponding routing security decision scheme based on the global threat level to perform security regulation and control on the UAV communication network includes: The real-time observation data of the suspected node is obtained, and the attack and defense utility matrix is ​​determined and the preset belief function is updated using the real-time observation data. The local threat level is determined using the updated belief function and the attack-defense utility matrix, and the global threat level is calculated using the local threat level. The node type of the suspected node is determined based on the comparison result between the global threat level and the preset level threshold. The corresponding routing security decision scheme is determined based on the node type, and the UAV communication network is subjected to security control processing according to the routing security decision scheme.

6. The secure routing control method for an unmanned aerial vehicle (UAV) communication network according to any one of claims 1-5, characterized in that, The acquisition of real-time detection data of the UAV through the UAV communication network includes: A drone communication network is constructed based on the routing architecture of the delay-tolerant and disconnection-tolerant network, and the number of maintenance and activation time slots of the drone communication network is obtained. If the number of maintenance activation time slots is less than the preset maximum number of activation time slots, an activation command is sent to the drone based on the drone communication network, so that the drone can collect and feed back real-time detection data based on the activation command. The preset maximum number of activation time slots is adaptively adjusted according to the number of drones deployed in the drone communication network.

7. A secure routing control device for an unmanned aerial vehicle (UAV) communication network, characterized in that, The device includes: The acquisition module is used to acquire real-time detection data of the UAV through the UAV communication network. The real-time data includes inspection data of the UAV sensor inspection task, coordinate parameters of the UAV, and forwarding reports forwarded by the UAV to the base station. The detection module is used to perform attack detection processing based on the inspection data to obtain suspected nodes of the UAV communication network. The attack detection processing includes data integrity attack detection, GPS spoofing attack detection, and network availability attack detection. The control module is used to calculate the global threat level of the suspected node using a belief function, and determine the corresponding routing security decision scheme based on the global threat level to perform security control processing on the UAV communication network.

8. A secure routing and control system for an unmanned aerial vehicle (UAV) communication network, characterized in that, The system includes: a base station, several relay nodes, and multiple drones; The base station is connected to several relay nodes and multiple drones to form a drone communication network. The base station performs the secure routing control method for the UAV communication network as described in any one of claims 1-6.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the secure routing control method for a drone communication network as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which are used to cause a computer to execute the secure routing control method for a drone communication network as described in any one of claims 1-6.

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