Anti-attack performance evaluation method for aircraft fleet network

Abstract

The invention discloses an anti-attack performance evaluation method for an aircraft fleet network, and the method comprises the steps: obtaining a two-dimensional space model which comprises a plurality of aircrafts; positioning information of each aircraft in the plurality of aircrafts in the two-dimensional space model is acquired, and the positioning information includes speed information of the aircrafts in the two-dimensional space model at each moment as position information; generating an aircraft fleet model based on the positioning information of the plurality of aircrafts; the aircraft fleet model is configured to determine the probability of successful communication connection establishment between any two aircrafts in the plurality of aircrafts; determining a target aircraft as an attack object based on the aircraft fleet model; performing attack simulation on the target aircraft by adopting a preset attack method to obtain an attacked aircraft group network; performing network recovery on the attacked cluster network based on a preset recovery method; performance evaluation is carried out on the cluster network after network recovery, and evaluation efficiency and accuracy can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft swarm networks, and in particular to a method for evaluating the anti-attack performance of an aircraft swarm network. Background Art

[0002] A swarm of aircraft, also known as a drone swarm, consists of a group of coordinated drones that work together to complete tasks that are difficult or impossible for a single drone to accomplish. Drone swarms have multiple advantages over single drones, including greater coverage, redundancy, and resilience. The successful deployment and operation of drone swarms depends on a strong and efficient communication network. Due to the high variability of drone swarm networks, drone swarms use self-organizing networks, and drone nodes can autonomously establish and maintain connections without the need for overall planning by the network center. Once some drone nodes in the drone swarm network fail due to hardware failure, environmental interference, or deliberate attacks, the drone swarm network will become "fragmented."

[0003] In order to improve the security of networks and systems, methods to enhance communication networks from attacks have become a recent research hotspot. At the same time, the evaluation of network performance (such as resilience assessment methods) is also an important part of the methods to strengthen the security of communication networks. However, most of these methods focus on non-UAV fields, or propose defense strategies only for deception attacks, and many resilience assessment methods often only focus on a specific dimension, such as the system's recovery time, performance degradation or vulnerability, etc., ignoring the comprehensive consideration of multiple dimensions; this may lead to an underestimation or overestimation of the system's true resilience.

[0004] Therefore, providing an anti-attack performance evaluation method for an aircraft swarm network can effectively improve the evaluation efficiency and accuracy. Summary of the invention

[0005] One or more embodiments of the present specification provide a method for evaluating the anti-attack performance of an aircraft cluster network, comprising: obtaining a two-dimensional space model, wherein the two-dimensional space model includes a plurality of aircraft; obtaining positioning information of each aircraft among the plurality of aircraft in the two-dimensional space model, wherein the positioning information includes speed information of the aircraft in the two-dimensional space model at each moment as position information; generating an aircraft cluster model based on the positioning information of the plurality of aircraft; the aircraft cluster model is configured to determine the probability of successfully establishing a communication connection between any two aircraft among the plurality of aircraft; determining a target aircraft as an attack object based on the aircraft cluster model; performing an attack simulation on the target aircraft using a preset attack method to obtain a cluster network after the attack; performing network recovery on the cluster network after the attack based on a preset recovery method; and performing performance evaluation on the cluster network after the network recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0007] Figure 1 is an exemplary module diagram of an anti-attack performance evaluation system for an aircraft fleet network according to some embodiments of this specification;

[0008] Figure 2 is an exemplary flow chart of a method for evaluating the anti-attack performance of an aircraft fleet network according to some embodiments of this specification;

[0009] Figure 3a is a schematic diagram of a cluster network before an attack according to some embodiments of this specification;

[0010] Figure 3b is a schematic diagram of a cluster network after an attack according to some embodiments of this specification;

[0011] Figure 4 is an exemplary schematic diagram of a required power determination model according to some embodiments of this specification;

[0012] Figure 5 It is a schematic diagram of a curve showing a change in a network connectivity index of a drone swarm after a single attack according to some embodiments of this specification;

[0013] Figure 6a-6c It is a schematic diagram showing the connectivity network of a drone swarm after being attacked by a corresponding attack mode according to some embodiments of the present specification;

[0014] Figure 7 is a schematic diagram of energy consumption of drone cluster communication in corresponding attack modes according to some embodiments of this specification;

[0015] Figure 8a-8c It is a schematic diagram comparing the resilience of a cluster network after an attack of a corresponding attack mode according to some embodiments of the present specification. DETAILED DESCRIPTION

[0016] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0017] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0018] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0019] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0020] As an important carrier of low-altitude flight activities, drones are the leading industry of the low-altitude economy. With the support of technologies such as automation, intelligence, and networking, drones will play an increasingly important role in various industrial applications, logistics and transportation, urban air traffic, and other fields. However, the utility of a single drone is often limited by its operating range, battery life, and susceptibility to interference or damage. These limitations can seriously affect the completion of missions, especially in complex or large-scale operations. To address these limitations, researchers are paying more and more attention to drone swarms. A drone swarm is a group of coordinated drones that work together to complete tasks that are difficult or impossible for a single drone to complete. Compared with a single drone, a drone swarm has multiple advantages, including greater coverage, redundancy, and resilience. For example, when performing surveillance missions, a drone swarm can cover a larger area faster and more thoroughly than a single drone. If one drone fails, other drones can continue to perform the mission, thereby increasing the overall success rate. The successful deployment and operation of a drone swarm depends on a strong and efficient communication network. Due to the high variability of drone swarm networks, drone swarms use self-organizing networks, and nodes can autonomously establish and maintain connections without the need for overall planning by the network center. Once some drone nodes in a drone network fail due to hardware failure, environmental interference or deliberate attack, the drone swarm network will become "fragmented".

[0021] In order to improve the security of the cluster network, evaluating it is an indispensable step so that targeted performance improvement can be carried out based on the evaluation results. Some embodiments of the present invention provide an aircraft cluster network anti-attack performance evaluation method, system, and device, which can realize effective evaluation of the cluster network, effectively improve the speed and efficiency of the evaluation, and at the same time, can also conduct multi-dimensional evaluation of the cluster network in combination with actual needs, so as to further improve the accuracy of the evaluation, and thereby obtain a more accurate and effective security improvement strategy.

[0022] Figure 1 It is an exemplary module diagram of an anti-attack performance evaluation system for an aircraft cluster network (hereinafter referred to as the anti-attack performance evaluation system 100) according to some embodiments of the present specification.

[0023] like Figure 1 As shown, the anti-attack performance evaluation system 100 may include a first acquisition module 110, a second acquisition module 120, a model generation module 130, a target determination module 140, an attack simulation module 150, a network recovery module 160, and a performance evaluation module 170. In some embodiments, all or part of the modules of the anti-attack performance evaluation system 100 may be implemented by a processor.

[0024] In some embodiments, the first acquisition module 110 is configured to: acquire a two-dimensional space model.

[0025] In some embodiments, the second acquisition module 120 is configured to: acquire positioning information of each of the multiple aircraft in the two-dimensional space model.

[0026] In some embodiments, the model generation module 130 is configured to generate an aircraft fleet model based on the positioning information of the plurality of aircraft.

[0027] In some embodiments, the target determination module 140 is configured to: determine the target aircraft as the attack target based on the aircraft fleet model. The target aircraft includes at least one of a first target aircraft, a second target aircraft, and a third target aircraft; the first target aircraft is at least one aircraft randomly selected from the plurality of aircraft; the second target aircraft is an aircraft among the plurality of aircraft whose degree of freedom meets a preset degree of freedom condition; the third target aircraft is an aircraft among the plurality of aircraft whose centrality meets a preset centrality condition, and the centrality is determined based on the aircraft fleet model.

[0028] In some embodiments, the attack simulation module 150 is configured to: use a preset attack method to simulate an attack on the target aircraft to obtain a post-attack fleet network.

[0029] In some embodiments, the attack simulation module 150 is further configured to: in response to the target aircraft being the first target aircraft, interfere with the network of the first target aircraft by means of random hardware failure or simulated environmental interference; in response to the target aircraft being the second target aircraft and / or the third target aircraft, interfere with the network of the second target aircraft and / or the third target aircraft by means of simulated human attack.

[0030] In some embodiments, the network recovery module 160 is configured to perform network recovery on the cluster network after the attack based on a preset recovery method.

[0031] In some embodiments, the network recovery module 160 is further configured to: obtain a number of connected cluster slices in the cluster network after the attack, the connected cluster slices including at least one aircraft; perform degree of freedom preprocessing on each aircraft in each of the connected cluster slices, and determine the communication capability level of each aircraft; establish a communication connection between aircraft whose communication capability level difference is within a preset difference range.

[0032] In some embodiments, the performance evaluation module 170 is configured to: perform performance evaluation on the cluster network after the network is restored.

[0033] In some embodiments, the performance evaluation module 170 is further configured to: perform connectivity level analysis on the cluster network after the attack and the cluster network after the network is restored, respectively, to obtain network connectivity before recovery and network connectivity after recovery; and obtain a network connectivity evaluation result based on the network connectivity before recovery and network connectivity after recovery.

[0034] In some embodiments, the performance evaluation module 170 is further configured to: respectively analyze the communication power demand of the cluster network after the attack and the cluster network after the network is restored, and obtain the power demand before restoration and the power demand after restoration; based on the power demand before restoration and the power demand after restoration, obtain a power demand evaluation result

[0035] For more information about the first acquisition module 110, the second acquisition module 120, the model generation module 130, the target determination module 140, the attack simulation module 150, the network recovery module 160, and the performance evaluation module 170, please refer to the relevant description below.

[0036] It should be noted that the above description of the anti-attack performance evaluation system 100 and its modules is only for convenience of description and does not limit this specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, Figure 1 The first acquisition module 110, the second acquisition module 120, the model generation module 130, the target determination module 140, the attack simulation module 150, the network recovery module 160, and the performance evaluation module 170 disclosed in the specification can be different modules in a system, or one module can realize the functions of two or more of the above modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the scope of protection of this specification.

[0037] The present specification provides a method for evaluating the anti-attack performance of an aircraft cluster network, the method comprising: obtaining a two-dimensional space model; obtaining the positioning information of each aircraft among the multiple aircraft in the two-dimensional space model; generating an aircraft cluster model based on the positioning information of the multiple aircraft; determining a target aircraft as an attack object based on the aircraft cluster model; using a preset attack method to perform an attack simulation on the target aircraft to obtain a cluster network after the attack; performing network recovery on the cluster network after the attack based on a preset recovery method; and performing performance evaluation on the cluster network after the network recovery.

[0038] Figure 2 FIG. 1 is an exemplary flow chart of a method for evaluating the anti-attack performance of an aircraft fleet network according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps. In some embodiments, the process 200 may be executed by a processor.

[0039] It should be noted that the processor is a processor configured in the control system and integrated with one or more modules of the anti-attack performance evaluation system. Among them, the control system is a system with computing capabilities, such as a computer, an industrial computer, a computing cloud platform, etc. The processor can execute program instructions to implement the anti-attack performance evaluation method of the aircraft cluster network described in some embodiments of this specification. The processor may include various common general-purpose central processing units (CPUs), graphics processing units (GPUs), microprocessors, application-specific integrated circuits (ASICs), or other types of integrated circuits.

[0040] In some embodiments, the control system may include a storage medium, which may store instructions or data. The storage medium may include a mass storage device, a removable storage device, a volatile read-write memory, a read-only memory (ROM), etc., or any combination thereof.

[0041] In some embodiments, the control system can establish a data connection with the aircraft through a wired network or a wireless network. The following takes the aircraft as a drone as an example to illustrate this solution. In practice, the aircraft can also be an unmanned helicopter, an unmanned airship, an unmanned submarine or other intelligent flight devices.

[0042] Step 210, obtaining a two-dimensional space model.

[0043] In some embodiments, the processor may obtain the two-dimensional space model based on a variety of methods. For example, the processor may simplify the drone swarm wandering model into an m*m two-dimensional space to obtain a two-dimensional space model. The drone swarm wandering model may be read based on the drone control system, or determined based on obtaining user input, etc.

[0044] In some embodiments, the two-dimensional space model includes multiple aircraft. For example, n drones are distributed in an m*m two-dimensional space, where each drone uses a unique identifier ID i .

[0045] Step 220: Obtain positioning information of each of the multiple aircraft in the two-dimensional space model.

[0046] In some embodiments, the positioning information includes the speed information of the aircraft in the two-dimensional space model at each time as position information. For example, the speed of the aircraft at time t can be described as (v xi ,v yi ), the position is (x i ,y i ).

[0047] In some embodiments, the processor may obtain the positioning information of each aircraft in the two-dimensional space model based on the GPS positioning system of the drone.

[0048] Step 230: Generate an aircraft fleet model based on the positioning information of the multiple aircraft.

[0049] In some embodiments, the processor may construct an aircraft fleet model based on the acquired information and the communication examples between the aircraft.

[0050] In some embodiments, the processor may construct the following aircraft fleet model:

[0051]

[0052] Among them, F(d j→i ) represents the probability of successfully establishing a communication connection between the jth aircraft and the ith aircraft; α represents the item weight; N t is the number of aircraft in the fleet network at time t; ε represents the adjustment coefficient; k i represents the degree of freedom of the i-th aircraft, k l represents the degree of freedom of the lth aircraft; d j→i represents the communication distance between the jth aircraft and the ith aircraft; P j→i It represents the probability of successfully establishing a communication connection between the j-th aircraft and all aircraft in the fleet where the i-th aircraft is located.

[0053] In some embodiments, the degrees of freedom of an aircraft include a communication upper limit of the aircraft, that is, the maximum number of aircraft that the aircraft can connect to communicate with under the current circumstances (such as within the maximum communication range), which can be obtained based on the communication control system of the aircraft.

[0054] In some embodiments, the probability of successfully establishing a communication connection between the j-th aircraft and the i-th aircraft can be represented based on the following formula:

[0055]

[0056] Among them, r c represents the communication range, and η represents the distance influence factor.

[0057] In some embodiments, when When , it means that the probability of the j-th aircraft successfully establishing a communication connection with other aircraft is 0, that is, there is no communicable aircraft within the maximum communication range of the j-th aircraft.

[0058] Step 240: determining a target aircraft to be attacked based on the aircraft group model.

[0059] In some embodiments, the target aircraft includes at least one of a first target aircraft, a second target aircraft, and a third target aircraft.

[0060] Among them, the first target aircraft is at least one aircraft randomly selected from the multiple aircraft; for example, the processor can randomly select m nodes from n nodes (i.e., n aircraft, and subsequent nodes are the aliases of aircraft in the swarm network) to make them invalid.

[0061] The second target aircraft is an aircraft among the multiple aircraft whose degree of freedom meets the preset degree of freedom condition. For example, the second target aircraft is a node with the highest degree of freedom among the n nodes, or a node in the first m positions in the order of the degree of freedom from the most to the least.

[0062] The third target aircraft is an aircraft among the multiple aircraft whose center degree meets a preset center degree condition. The preset center degree condition may be the highest center degree or the top m center degree ranking.

[0063] Centrality refers to the centrality of the node in the communication network. Nodes with high centrality play an important bridge role in the network. Attacking these nodes will seriously affect the data transmission efficiency and robustness of the network, and achieve a better attack simulation effect.

[0064] The centrality can be determined based on the aircraft group model. For example, the processor can perform clustering based on the positioning information, degree of freedom, and probability mean of the probability of each aircraft successfully establishing a communication connection with other aircraft corresponding to each node, to obtain a number of clusters, and use the cluster center of each cluster as the third target aircraft. The probability of each aircraft successfully establishing a communication connection with other aircraft can be determined based on the aircraft group model.

[0065] As an example only, the clustering algorithm may include but is not limited to K-Means clustering and / or density-based clustering method (DBSCAN), etc.

[0066] Step 250, using a preset attack method to simulate an attack on the target aircraft to obtain a fleet network after the attack.

[0067] In some embodiments, different attack methods are preset for different types of target aircraft.

[0068] In some embodiments, in response to the target aircraft being the first target aircraft, the processor may interfere with the network of the first target aircraft by simulating random hardware failure or simulating environmental interference.

[0069] For example, random hardware failure simulation may include signal receiving antenna failure, power supply failure, etc. Simulated environmental interference may include simulated stormy weather, simulated high or low temperature weather, etc.

[0070] In some embodiments, in response to the target aircraft being the second target aircraft and / or the third target aircraft, a network of the second target aircraft and / or the third target aircraft is interfered with in a manner of simulating a human attack.

[0071] For example, methods of simulating human attacks may include deception attacks, network intrusions, denial of service attacks, data theft, and the like.

[0072] Step 260: Perform network recovery on the cluster network after the attack based on a preset recovery method.

[0073] After an attack occurs, the aircraft in the swarm network will be divided into several connected slices, each of which includes at least one aircraft. The conventional recovery method is to directly perform self-detection and self-recovery on each connected slice, but this method has the problems of poor recovery effect and long time.

[0074] In some embodiments, the preset recovery method includes:

[0075] S262: Obtain a number of connected cluster slices in the cluster network after the attack.

[0076] For example, nodes that are connected to each other can be considered as the same connected fleet slice. Each connected fleet slice includes at least one aircraft. The processor can obtain each connected fleet slice separately.

[0077] like Figure 3a and Figure 3b It is a schematic diagram of a cluster network before and after an attack according to some embodiments of this specification; as can be seen from the figure, after the cluster network is attacked, multiple connected cluster slices will be formed.

[0078] S264: Perform degree of freedom preprocessing for each aircraft in each of the connected cluster slices, and determine the communication capability level of each of the aircraft.

[0079] In some embodiments, the processor may perform degree of freedom preprocessing on each aircraft in the following manner:

[0080] In response to the degree of freedom of the aircraft being less than a minimum threshold, the communication capability level of the aircraft is increased by one level; in response to the degree of freedom of the aircraft being greater than a maximum threshold, the communication capability level of the aircraft is decreased by one level.

[0081] The minimum threshold and maximum threshold can be set based on historical experience, such as the minimum threshold is 2 and the maximum threshold is 50.

[0082] In some embodiments, the communication capability level can be determined based on the degree of freedom of the aircraft. For example, the processor can determine the communication capability level of the aircraft by querying a level comparison table based on the degree of freedom of the aircraft. The level comparison table can be a comparison table pre-set based on historical data and historical experience, which includes the communication capability level corresponding to each degree of freedom, such as the greater the degree of freedom, the greater the corresponding communication capability level.

[0083] In some embodiments, each aircraft also includes a signal transmitter with multiple random links, which is communicatively connected to the processor. The signal transmitter can be used to transmit power gears. When the aircraft is in daily use, the gear is smaller. When the aircraft is attacked and a larger connecting hole appears, the gear gradually increases until the gear threshold is met.

[0084] In some embodiments, the communication capability level of an aircraft is also related to the positioning information of the aircraft and the communication distance between the aircraft and other aircraft. For example, the level comparison table also includes the corresponding relationship between the positioning information of different aircraft, the communication distance between other aircraft, the degree of freedom and the communication capability level. The processor can further obtain the positioning information of the aircraft and the communication distance between the aircraft and other aircraft, and determine the communication capability level of the aircraft based on the level comparison table.

[0085] S266: Establishing a communication connection between the aircrafts whose communication capability level differences are within a preset difference range.

[0086] In some embodiments, the preset difference range can be preset based on demand, for example, the preset difference range can be 0 or 1, etc., and as an example only, when the preset difference range is 0, the processor can control the establishment of a communication connection between two aircraft with equal communication capability levels. In some embodiments, when the number of such communication connections increases to a certain connection threshold, the connected cluster slices of the cluster network begin to combine until a huge connected component appears, and these huge connected components include most of the peer networks in the attacked cluster network, thereby gradually restoring the cluster network.

[0087] Step 270: Perform performance evaluation on the cluster network after the network is restored.

[0088] In some embodiments, the performance evaluation may include a network connectivity performance evaluation, and the network connectivity performance evaluation includes performing a connectivity level analysis on the cluster network after the attack and the cluster network after the network is restored.

[0089] In some embodiments, when performing a network connectivity performance evaluation, the processor may respectively perform a connectivity level analysis on the cluster network after the attack and the cluster network after the network is recovered, to obtain the network connectivity before recovery and the network connectivity after recovery, and obtain a network connectivity evaluation result based on the network connectivity before recovery and the network connectivity after recovery.

[0090] In some embodiments, the connectivity level analysis includes:

[0091] S101: Control each of the plurality of aircrafts except the target aircraft to generate a probability μ according to the information at the corresponding time t based on a preset period. t , generate test information and send it to the target aircraft.

[0092] in, μ0 represents the probability of generating initial information, which can be obtained based on a preset. N represents the total number of aircraft in the two-dimensional space model.

[0093] S102: Obtain the total amount of information y(t) obtained by the target aircraft at time t:

[0094] Among them, R i (t) represents the number of messages received by the i-th aircraft, is the shortest path length between the jth aircraft and the ith aircraft obtained by the shortest path algorithm, N t represents the number of aircraft at time t, Δ represents the time sensitivity coefficient, 0<Δ≤1;

[0095] S103: Determine the network connectivity before restoration or the network connectivity after restoration based on the total number of the information acquired at multiple moments.

[0096] In some embodiments, since the shortest path length of each piece of information may affect the real-time performance of information exchange within the aircraft fleet, the processor may determine the performance recovery status of the system at time t based on the total amount of information acquired by the target aircraft at time t.

[0097] In some embodiments, the data sent by each aircraft are random numbers, and the processor can perform multiple simulations and take the average of multiple total information obtained from the multiple simulations as the final total information.

[0098] In some embodiments, the processor may obtain the total amount of information determined based on data at multiple times before recovery as network connectivity before recovery, and the total amount of information determined based on data at multiple times after recovery as network connectivity after recovery, in the above manner.

[0099] In some embodiments, the processor may obtain a network connectivity evaluation result based on the network connectivity before recovery and the network connectivity after recovery. For example, the processor may determine the network connectivity evaluation result through a first result comparison table based on the difference between the total number of information before and after recovery. The first result comparison table may be preset based on historical experience, and the first result comparison table records the network connectivity evaluation results corresponding to the difference between the total number of different information. For example, the network connectivity after recovery is greater than the network connectivity before recovery, and the greater the difference between the two, the better the network connectivity evaluation result.

[0100] In some embodiments, the performance evaluation may further include a power demand evaluation, and the power demand evaluation includes performing communication power demand analysis on the cluster network after the attack and the cluster network after the network is restored.

[0101] In some embodiments, when performing a power requirement assessment, the processor may respectively analyze the communication power requirements of the cluster network after the attack and the cluster network after the network is restored, obtain the power requirement before recovery and the power requirement after recovery, and obtain a power requirement assessment result based on the power requirement before recovery and the power requirement after recovery.

[0102] In some embodiments, the processor performs communication power demand analysis including: for each aircraft, based on the communication distance between the aircraft and other aircraft, determining the power demand before recovery and the power demand after recovery through a power demand determination model.

[0103] In some embodiments, the required power determination model may be a free space path loss (FSPL) communication model. The free space path loss communication model may be used to determine the increase in energy consumption due to an increase in communication distance.

[0104] Free space path loss (FSPL) is an indicator in wireless communication that can be used to describe the attenuation of signal strength when radio waves propagate in free space. The free space path loss communication model can be used to characterize the decrease in signal power caused by the increase in propagation distance when radio waves are not affected by any obstacles, reflections or scattering. Since drones usually fly at an altitude of 100 to 1000 meters, there are few obstacles blocking communication. Therefore, the relationship between communication distance and communication power can be characterized by constructing a free space path loss communication model.

[0105] In some embodiments, the free space path loss communication model is characterized as follows:

[0106] Wherein, d represents the communication distance in meters, c is the speed of light in meters per second, and f is the signal frequency in Hz.

[0107] In some embodiments, in order to more intuitively characterize the relationship between the communication distance and the communication power, the processor may further convert the above-mentioned free space path loss communication model and obtain the following converted model:

[0108] Among them, G t is the transmitting antenna gain, in decibels (dB); G ris the receiving antenna gain, in decibels (dB); P sens is the receiving sensitivity in decibel milliwatts (dBm).

[0109] In some embodiments, for ease of calculation, the processor may convert the transmit power from decibel milliwatts dBm to milliwatts (mW), and the specific conversion method is as follows:

[0110] From the above content, it can be concluded that as the communication distance increases, the increase in communication power becomes more and more obvious. Therefore, the change in communication distance can be determined based on the communication power, and then the change in its communication capability can be obtained.

[0111] In some embodiments, the required power determination model may be a machine learning model, such as a convolutional neural network model. Figure 4 The corresponding content.

[0112] In some embodiments, the processor may obtain a power demand evaluation result based on the power demand before recovery and the power demand after recovery. For example, the processor may determine the difference in communication distance based on the difference in power demand before and after recovery, and then determine the difference in network performance before and after recovery. For example, if the power demand after recovery is higher than before recovery, it means that the communication distance after recovery is higher than before recovery.

[0113] As an example only, the processor may determine the power demand evaluation result through the second result comparison table based on the difference in power demand. The second result comparison table may be preset based on historical experience, and the second result comparison table records the power demand evaluation results corresponding to the difference in different communication distances. For example, the power demand after recovery is greater than the power demand before recovery, and the greater the difference between the two, the better the power demand evaluation result.

[0114] In some embodiments, the processor may perform a performance evaluation on the cluster network after the network is restored based on the network connectivity evaluation results and the power demand evaluation results. For example, the processor may use the weighted values ​​of the network connectivity evaluation results and the power demand evaluation results as the final evaluation results, wherein the weights of the network connectivity evaluation results and the power demand evaluation results may be set based on actual needs. If more emphasis is placed on network connectivity, the weight of the network connectivity evaluation result will be greater; if more emphasis is placed on the recovery or improvement of the communication distance, the weight of the power demand evaluation result will be greater.

[0115] It should be noted that the above description of the process 200 is only for example and illustration, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process 200 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0116] Figure 4 is an exemplary schematic diagram of a required power determination model according to some embodiments of this specification. Figure 4 As shown, in some embodiments, for each aircraft, the processor can determine the power requirement before recovery and the power requirement after recovery 460 based on the communication characteristics 410, weather characteristics 420, communication environment characteristics 440 and equipment characteristics 440 of the aircraft and other aircraft through a power demand determination model 450. For example, the power requirement before recovery can be determined based on the communication characteristics, weather characteristics, communication environment characteristics and equipment characteristics before recovery, and the power requirement after recovery can be determined in the same way.

[0117] In some embodiments, communication characteristics may include the quality of the communication network, weather characteristics may include temperature, humidity, air pressure, etc., communication environment characteristics may include whether there is any obstruction between aircraft, the amount of interference, etc., and equipment characteristics may include aircraft model, age, etc.

[0118] In some embodiments, the inputs of the power requirement determination model 450 include communication characteristics 410 between the aircraft and other aircraft, weather characteristics 420, communication environment characteristics 440, and equipment characteristics 440, and the output includes the power requirement 460 of the aircraft in the corresponding scenario.

[0119] In some embodiments, the processor may train a demand power determination model based on a plurality of labeled training samples. For example, the processor may input the training samples into the initial demand power determination model, construct a loss function based on the output and labels of the initial demand power determination model, iteratively update the parameters of the initial demand power determination model based on the loss function, and terminate the iteration when the iteration completion condition is met to obtain a trained demand power determination model. The iterative update method includes but is not limited to the gradient descent method, and the iteration completion condition may be that the loss function converges or the number of iterations reaches a threshold.

[0120] The training samples include sample communication characteristics of the sample aircraft in the sample scenario, sample weather characteristics of the sample scenario, sample communication environment characteristics of the sample scenario, and sample equipment characteristics of the sample aircraft, and the output includes the actual power demand of the sample aircraft in the sample scenario.

[0121] The following is an example of a specific evaluation test based on the scheme of the present invention when a drone performs forest monitoring to illustrate the scheme. The simulation is based on Matlab 2017a. The details are as follows:

[0122] When drone swarms are used for tasks such as forest surveillance, drones may become ineffective due to certain attacks or energy exhaustion. The goal of this embodiment is to prevent the emergence of isolated drones without changing the planned path of the drones. In this embodiment, it is assumed that there are N drones distributed in a fixed range, the action path of the drone adopts the RandomWaypoint action mode, and the initial communication radius of the drone is r c ,like Figure 5 As shown, assuming that the time point of launching the attack is t attack The time point when the drone adopts recovery mode after detecting the attack is t recovery , the simulation stop time is t final , the original value of the connectivity index is y D , the connectivity index reaches the minimum value y after the attack min , the connectivity index after restoration is y r , the specific parameter configuration description is shown in Table 1:

[0123] After simulation, the connectivity of the drone network can be experimentally analyzed. The prediction results of the swarm network under three attack modes can be expressed based on the following formula:

[0124]

[0125] Among them, the number of drone nodes is 200, and the drone network connectivity index is expressed as y(t).

[0126] like Figure 6a-6c is a schematic diagram showing the connectivity of a drone swarm network after being attacked by a corresponding attack mode according to some embodiments of the present specification, wherein Figure 6a-6c The performance of the connectivity network of the drone network when 40, 60 and 80 drones are attacked respectively. Among them, when t is 0-20s, it is the normal operation stage of the drone group, and the fluctuation of y(t) is mainly caused by the internal movement of the drone nodes; when t is 21-30s, it is the stage when the drone group is attacked, and some drone nodes lose communication capabilities, resulting in a sharp drop in the connectivity index y(t). During t from 31 to 50s, the drone group uses the preset recovery method to restore connectivity. The results show that when the number of nodes under attack is small, the preset recovery method can effectively restore the connectivity of the drone network.

[0127] Due to the structural constraints of the UAV, energy is both important and limited, so it is necessary to evaluate the amount by which the preset recovery method will increase the energy consumption of the UAV system. In some embodiments, the processor can convert the communication radius of the UAV node into the corresponding required communication energy. The experimental results are shown in Figure 7 As shown, Figure 7 The types of node attacks and the number of nodes attacked are indicated by a cross combination of three colors and three different line types: red indicates attack mode I, green indicates attack mode II, and blue indicates attack mode III. Attack mode I, attack mode II, and attack mode III can be used to indicate different attack methods, such as attack mode I, attack mode II, and attack mode III can be selected from random hardware failure simulation, simulated environmental interference, and simulated human attack. As an example only, attack mode I can use random hardware failure simulation, attack mode II can use simulated human attack, and attack mode III can use simulated environmental interference. The solid line indicates that 40 nodes are attacked, the dotted line indicates that 60 nodes are attacked, and the denser dotted line indicates that 80 nodes are attacked.

[0128] Among them, the initial smooth part represents the total communication energy consumption of the drone swarm before the attack. After the attack, some drone nodes lost their communication capabilities, reducing the overall communication energy consumption. The decline that can be observed in the figure reflects that the more drone nodes there are, the more obvious the decline is. When the same number of nodes are attacked, the green line is always higher than the other two colors, indicating that attack mode II requires more energy to recover compared to attack modes I and III. This may be because attack mode II directly destroys network connectivity, but is also easier to recover and can be recovered by increasing energy input. In contrast, it is more difficult for the network to fully recover from attack mode III.

[0129] In this embodiment, the network resilience of drone clusters under different attack types was tested. The test results are as follows: Figure 8a-8c As shown, Figure 8a-8c The numbers of drones attacked in the three experimental results were 40, 60 and 80 respectively.

[0130] The blue part indicates the resilience value of the drone swarm network when the preset recovery method (i.e., the original method) is not adopted after being attacked, and the orange part indicates the resilience value when the preset recovery method (i.e., the present invention) is adopted. After recovery using the preset recovery method, the robustness of the drone swarm network is significantly improved, among which the resilience under attack mode III is most significantly improved, with the resilience values ​​increased by 5.3612 times, 7.4177 times, and 8.9435 times, respectively. The preset recovery method also shows obvious effectiveness for attack mode I and attack mode II, with the resilience increased by 2.5857 times, 3.0313 times, and 3.6188 times, and 2.6322 times, 4.1868 times, and 6.3443 times, respectively.

[0131] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0132] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0133] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0134] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0135] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0136] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0137] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for evaluating the anti-attack performance of an aircraft swarm network, characterized in that: include: Acquire a two-dimensional space model, wherein the two-dimensional space model includes a plurality of aircraft; Acquire positioning information of each of the multiple aircraft in the two-dimensional space model, wherein the positioning information includes speed information of the aircraft in the two-dimensional space model at each time as the positioning information; Based on the positioning information of the plurality of aircraft, an aircraft swarm model is generated; the aircraft swarm model is configured to determine a probability of successfully establishing a communication connection between any two aircraft among the plurality of aircraft; Based on the aircraft fleet model, determining a target aircraft to be attacked; Using a preset attack method to simulate an attack on the target aircraft to obtain a fleet network after the attack; Performing network recovery on the cluster network after the attack based on a preset recovery method; A performance evaluation is performed on the cluster network after the network is restored.

2. The method according to claim 1, characterized in that The aircraft fleet model is characterized as follows: Among them, F(d j→i ) represents the probability of successfully establishing a communication connection between the jth aircraft and the ith aircraft; α represents the item weight; N t is the number of aircraft in the fleet network at time t; ε represents the adjustment coefficient; k i represents the degree of freedom of the i-th aircraft, k l represents the degree of freedom of the lth aircraft; d j→i represents the communication distance between the jth aircraft and the ith aircraft; P j→i It represents the probability of successfully establishing a communication connection between the j-th aircraft and all aircraft in the fleet where the i-th aircraft is located.

3. The method according to claim 2, characterized in that The degrees of freedom of the aircraft include the communication upper limit of the aircraft. The probability of successfully establishing a communication connection between the j-th aircraft and the i-th aircraft is characterized by the following formula: Among them, r c represents the communication range, and η represents the distance influence factor.

4. The method according to claim 2, characterized in that: The target aircraft includes at least one of a first target aircraft, a second target aircraft, and a third target aircraft; The first target aircraft is at least one aircraft randomly selected from the plurality of aircraft; The second target aircraft is an aircraft among the multiple aircraft, the degree of freedom of which satisfies a preset degree of freedom condition; The third target aircraft is an aircraft among the multiple aircraft, the centrality of which satisfies a preset centrality condition, and the centrality is determined based on the aircraft fleet model.

5. The method according to claim 4, characterized in that The attack simulation of the target aircraft using a preset attack method includes: In response to the target aircraft being the first target aircraft, interfering with the network of the first target aircraft by means of random hardware failure or simulated environmental interference; In response to the target aircraft being the second target aircraft and / or the third target aircraft, a network of the second target aircraft and / or the third target aircraft is interfered with in a manner of simulating a human attack.

6. The method according to claim 1, characterized in that The preset recovery method includes: Acquire a plurality of connected cluster slices in the cluster network after the attack, wherein the connected cluster slices include at least one aircraft; A degree of freedom preprocessing is performed on each aircraft in each of the connected cluster slices, and a communication capability level of each aircraft is determined; the communication capability level is determined based on the degree of freedom of the aircraft, and the degree of freedom preprocessing includes: In response to a degree of freedom of the aircraft being less than a minimum threshold, increasing a communication capability level of the aircraft by one level; In response to the degree of freedom of the aircraft being greater than a maximum threshold, reducing the communication capability level of the aircraft by one level; A communication connection is established between the aircrafts whose communication capability level differences are within a preset difference range.

7. The method according to claim 1, characterized in that The performing performance evaluation on the cluster network after the network is restored includes: Performing connectivity level analysis on the cluster network after the attack and the cluster network after the network is restored, respectively, to obtain network connectivity before restoration and network connectivity after restoration; Based on the network connectivity before restoration and the network connectivity after restoration, a network connectivity evaluation result is obtained.

8. The method according to claim 7, characterized in that The connectivity level analysis includes: Control each of the plurality of aircrafts except the target aircraft to generate a probability μ according to the information at the corresponding time t based on a preset period. t , generating test information and sending it to the target aircraft; wherein, μ0 represents the probability of generating initial information, and N represents the total number of aircraft in the two-dimensional space model; Get the total amount of information y(t) acquired by the target aircraft at time t: Among them, R i (t) represents the number of messages received by the i-th aircraft, is the shortest path length between the jth aircraft and the ith aircraft obtained by the shortest path algorithm, N t represents the number of aircraft at time t, Δ represents the time sensitivity coefficient (0<Δ≤1); The network connectivity before restoration or the network connectivity after restoration is determined based on the total number of the information acquired at multiple moments.

9. The method according to claim 1 or 7, characterized in that: The performing performance evaluation on the cluster network after the network is restored includes: Performing communication power demand analysis on the cluster network after the attack and the cluster network after the network is restored, respectively, to obtain power demand before restoration and power demand after restoration; Based on the power demand before recovery and the power demand after recovery, a power demand evaluation result is obtained.

10. The method according to claim 9, characterized in that The communication power demand analysis includes: For each aircraft, based on the communication distance between the aircraft and other aircraft, a power requirement before restoration and a power requirement after restoration are determined by a power requirement determination model.

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