A credit mechanism-based anti-dynamic SSDF attack method and system

Abstract

The application discloses a credit mechanism-based anti-dynamic SSDF attack method and system, and the method comprises the following steps: allocating an initial reputation value to each user in a network; repeatedly executing the following steps until a preset termination condition is met; each user performs local spectrum sensing to determine whether a frequency band is occupied by observing the surrounding environment; calculating a comprehensive reputation value by using the current reputation value, the historical reputation value, a gain factor and a suppression factor; allocating a fusion weight to each node according to the calculated comprehensive reputation value; weighting and fusing the local spectrum sensing results of the nodes by using the allocated fusion weight to obtain a decision result; and comparing the global decision value with the local spectrum sensing value to update each reputation value. According to the historical and current sensing behaviors of secondary users in the users, the Beta reputation system is used to allocate reputation values to the secondary users, and the reputation values are combined into a comprehensive reputation value to measure the reliability of the secondary users in the centralized cooperative spectrum sensing.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication, and particularly relates to a credit mechanism-based anti-dynamic SSDF attack method and system. BACKGROUND

[0002] In cognitive radio, although cooperative spectrum sensing (CSS) has advantages in sensing performance, it is vulnerable to attacks due to its low security performance, among which spectrum sensing data falsification (SSDF) attack is the most common attack. These malicious users (MU) tamper with the initial values received and send false messages to the fusion center, thereby making the fusion decision performance decline and the system easily make wrong judgments. In order to solve this problem, a more reliable CSS method is needed to minimize the impact of attackers on the system. In order to encourage correct sensing data sharing among secondary users (SU), people use trust schemes to identify MUs in CSS. Most of the research based on trust schemes is based on the fact that MUs always report false sensing data. In order to avoid detection of trust schemes, MUs can behave reasonably by providing correct sensing data and sometimes partially hide them by launching SSDF attacks in an intermittent manner. That is, they can maintain high credibility in the alternating process of reporting true or false sensing data. Many research works mainly focus on attack detection based on detection probability, but rarely consider the punishment of attacks, ignoring how to implement an effective punishment scheme for attackers. In addition, in dynamic SSDF attacks, MUs alternate submitting true and false sensing data, and general reputation mechanisms cannot effectively identify such attacks, MUs may always be in a trusted state, and a good reputation update mechanism should be sensitive to changes in vehicle behavior and be able to punish false sensing reports submitted by them.

[0003] Based on the above analysis and research, dynamic attacks in spectrum sensing are a problem that needs to be solved urgently. SUMMARY

[0004] To address the problems existing in the prior art, this invention provides a dynamic SSDF attack security method based on a credit mechanism. This method integrates the current and historical spectrum perception performance of secondary users, employs a Beta reputation system to assign corresponding reputation values, and then uses this comprehensive reputation value to evaluate the credibility of secondary users in centralized collaborative spectrum perception in the vehicle network. Simultaneously, a reputation-correcting penalty scheme is proposed, introducing gain and inhibition factors to encourage secondary users to engage in positive and honest perception activities. The entire strategy can effectively mitigate the impact of malicious vehicle behavior on decision-making, ensure the correctness of global decisions, and greatly improve the fairness and robustness of the system. Considering the dynamic attack methods, intermittent dynamic attack behavior patterns are introduced to further verify the effectiveness of our proposed defense strategy.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a method for resisting dynamic SSDF attacks based on a credit mechanism, comprising the following steps:

[0006] Assign an initial reputation value to each user in the network;

[0007] Repeat the following steps until the preset termination condition is met;

[0008] Each user performs local spectrum awareness, determining whether a frequency band is occupied by observing the surrounding environment;

[0009] Calculate the overall reputation value using the current reputation value, historical reputation value, gain factor, and inhibition factor;

[0010] Based on the calculated overall reputation value, a fusion weight is assigned to each node;

[0011] Using the assigned fusion weights, the local spectrum sensing results of each node are weighted and fused to obtain the decision result D. t );

[0012] Each reputation value is updated by comparing the global decision value and the local spectrum awareness value.

[0013] Furthermore, when initializing reputation values, the initial reputation value is set based on the user's basic attributes.

[0014] Furthermore, the weight of a user in the collaborative spectrum perception process is dynamically adjusted based on the user's reputation value; for users whose reputation value is lower than a preset threshold, penalties such as reducing their weight or removing them from the network are imposed.

[0015] Furthermore, each SU receives spectrum sensing data via its antenna and can independently make binary decisions; each SU transmits data to the FC via a reporting channel, achieving data aggregation; the FC uses multi-data fusion technology to comprehensively analyze the collected data and make global decisions.

[0016]

[0017] in, For signals transmitted by the PU, Indicates from PU to the number Channel gain per unit (SU) For the first Noise at each SU This indicates that the PU is not using licensed spectrum. This indicates that the PU is using licensed spectrum. For the first The signal received by each SU Indicates the first The energy of the signal received by each SU This indicates the energy threshold.

[0018] Furthermore, at the t-th sensing time slot, the th... The fusion weight value of each SU can be calculated as follows:

[0019]

[0020] in, , This is the trust threshold value.

[0021] Furthermore, a final decision is made based on the majority fusion rule. as follows:

[0022]

[0023] in, Indicates that the class is always occupied. Indicates a class that is always idle. This indicates a consistently error class.

[0024] Based on the concept of the method, this invention provides a credit mechanism-based anti-dynamic SSDF attack system, including an initialization module, an iteration module, a perception module, a calculation module, a fusion module, and a reputation update module.

[0025] The initialization module is used to assign an initial reputation value to each user in the network;

[0026] The iteration module is used to repeatedly execute the following steps until a preset termination condition is met;

[0027] The perception module is used for acquiring each user to perform local spectrum sensing, and judging whether a certain frequency band is occupied by observing the surrounding environment;

[0028] The calculation module utilizes the current reputation value, the historical reputation value, the gain factor and the suppression factor to calculate a comprehensive reputation value;

[0029] The fusion module allocates a fusion weight for each node according to the calculated comprehensive reputation value; and utilizes the allocated fusion weight to perform weighted fusion on the local spectrum sensing results of the nodes to obtain a decision result;

[0030] The reputation updating module is used for comparing the global decision value and the local spectrum sensing value to update each reputation value.

[0031] The application further provides a computer device comprising a processor and a memory, the memory is used for storing a computer executable program, the processor reads the computer executable program from the memory and executes, and the processor can realize the anti-dynamic SSDF attack method based on a credit mechanism when executing the computer executable program.

[0032] Meanwhile, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program can realize the anti-dynamic SSDF attack method based on the credit mechanism when being executed by a processor.

[0033] Compared with the prior art, the application has at least the following beneficial effects: in view of the problem that there are a large number of dynamic SSDF attacks in the system, the application is applied to a local energy measurement hard fusion centralized cooperative spectrum sensing, in the scheme, the FC allocates a reputation value for the secondary user in the user according to the historical and current perception behaviors of the secondary user, and combines the reputation value into a comprehensive reputation value to measure the reliability of the secondary user in the centralized cooperative spectrum sensing. Meanwhile, an incentive mechanism strategy for correcting the reputation is provided, wherein a gain factor and a suppression factor are introduced, the honest secondary user is positively fed back to enhance the power and persistence of submitting a true decision, and the malicious user is punished to promote the malicious user to adjust the malicious behavior and avoid making mistakes again. The whole scheme can effectively alleviate the influence of the malicious behavior of the secondary user on the decision, ensure the correctness of the global decision, and greatly improve the security and robustness of the cognitive radio network. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 The flowchart for the application is shown;

[0035] Figure 2 The graph of the relationship between the SNR and the required minimum sample size is shown;

[0036] Figure 3 a graph of the relationship between the number of secondary users and the probability of correct perception;

[0037] Figure 4 a graph of the relationship between the proportion of malicious users and the probability of false perception;

[0038] Figure 5 a graph of the relationship between the number of secondary users and the probability of correct perception for the proposed method;

[0039] Figure 6 a graph of the relationship between the number of secondary users and the probability of false perception for the proposed method;

[0040] Figure 7 a simulation test result of the perception performance against malicious users in different scenarios based on the method of the present application. DETAILED DESCRIPTION

[0041] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] Reference Figure 1 The present application provides an anti-dynamic SSDF attack method based on a credit mechanism, including the following steps:

[0043] S1. Before starting, assign an initial reputation value to each node in the network. This initial value can be a fixed value, or can be set based on some basic attributes of the user, to provide an initial value for the spectrum sensing and reputation value updating process. The initial reputation value can be initialized as follows: fixed value initialization, assign a same fixed reputation value to all nodes, which is simple and easy to implement, but may not be flexible enough in actual application, without considering the differences between nodes; initialization based on node attributes, set the initial reputation value based on the hardware configuration and geographic location of the node, which can better reflect the actual ability and value of the node; initialization based on historical behavior, if a part of the nodes in the network have historical behavior records, the initial reputation value can be calculated based on these historical behavior records, which requires historical data support, but is more reasonable in actual application; random initialization, randomly assign an initial reputation value to each node, which is simple and has a certain flexibility, but may need additional adjustment and optimization in actual application. Adaptive initialization, dynamically adjust the initial reputation value by observing the preliminary performance of the node during the cooperative spectrum sensing process, this method is more flexible, but requires a monitoring and feedback mechanism to be deployed in the network. In order to improve the robustness and security of the system, the initialization method based on node attributes or historical behavior is adopted.

[0044] S2. Each user performs local spectrum sensing, i.e. judges whether a certain frequency band is occupied by observing the surrounding environment, which can be done by setting the detection time and sampling rate using energy detection method;

[0045] S3. Calculate the comprehensive reputation value using the current reputation value, historical reputation value, gain factor and suppression factor:

[0046] According to the current reputation value and historical reputation value of the node, combined with the gain factor (used to reward good behavior) and the suppression factor (used to punish bad behavior), the comprehensive reputation value of the user is calculated; a simple weighted average method can be used, or a machine learning algorithm can be used;

[0047] S4. Calculate the fusion center to allocate fusion weight:

[0048] According to the calculated comprehensive reputation value, a fusion weight is allocated to each node, which reflects the importance and credibility of the node in the cooperative spectrum sensing process.

[0049] S5. The fusion center decision fusion gets D(t):

[0050] Using the allocated fusion weight, the local spectrum sensing results of each node are weighted and fused to obtain the final decision result D(t). This process can be completed in the fusion center (Fusion Center, FC), or it can be realized through a distributed algorithm.

[0051] S6. Compare the global decision value with the local spectrum sensing value and update each reputation value:

[0052] Compare the global decision value D(t) with the local spectrum sensing value of each user. If the local sensing result of a user is consistent with the global decision, it is considered that the user shows good behavior, and the reputation value of the user can be appropriately increased; if it is not consistent, it is considered that the user may have bad behavior, and the reputation value of the user needs to be reduced. In this way, the reputation value of the node can be continuously updated and adjusted, thereby improving the reliability and security of the entire system.

[0053] The dynamic SSDF attack security method based on credit mechanism provided in this embodiment includes the following steps:

[0054] Step 1, initialization, that is, initializing the reputation value, assuming that each user is honest;

[0055] Step 2, obtain local spectrum sensing. The local spectrum sensing uses multi-user based cooperative spectrum sensing to take advantage of spatial diversity. Each SU receives spectrum sensing data through an antenna and can independently sense and make a binary decision; each SU transmits data to the FC through a reporting channel to realize data collection; the FC uses multi-data fusion technology to comprehensively analyze the collected data to make a global decision.

[0056]

[0057] in, For signals transmitted by the PU, Indicates from PU to the number Channel gain per unit (SU) For the first Noise at each SU This indicates that the PU is not using licensed spectrum. This indicates that the PU is using licensed spectrum. For the first The signal received by each SU Indicates the first The energy of the signal received by each SU This indicates the energy threshold.

[0058] Local spectrum sensing allows unlicensed users (secondary or cognitive users) to opportunistically access idle frequency bands allocated to licensed users (or primary users) but currently rarely or never used. Local spectrum sensing primarily involves the physical and link layers. The physical layer focuses on various specific local detection algorithms, while the link layer focuses on user cooperation and the optimization of local sensing, cooperative sensing, and sensing mechanisms. Energy detection algorithms, matched filter detection methods, or cyclostationary feature detection methods can be employed.

[0059] Step 3: The reputation system can help identify and isolate malicious users, thereby reducing their impact on the system. This involves integrating the reputation system into the collaborative spectrum sensing process. in, The probability of perceived behavior. , >0, >0. Furthermore, the current reputation value, historical reputation value, gain factor, and suppression factor are used to calculate the overall reputation value.

[0060] The t-th sensing time slot The current reliability values ​​for each SU are as follows:

[0061]

[0062] in, and Representing the first The results of local perception of each SU and the global decision of FC.

[0063] The t-th sensing time slot The historical reputation of SU It was evaluated according to the following rules:

[0064]

[0065] The gain factor is calculated as follows:

[0066]

[0067] where, is the reputation value of the tth SU at the tth sensing time, is the time of the continuous honest sensing event.

[0068] The suppression factor of malicious attack behavior is represented as:

[0069]

[0070] where, is the time of the continuous false sensing event.

[0071] In summary, the comprehensive reputation value of the tth SU at time t can be represented as:

[0072] .

[0073] Step 4, the data fusion part realizes the allocation of different fusion weights to the comprehensive reputation value of the SU. The SU with higher reputation has greater influence on the final decision, so the sensing accuracy of the CSS can be improved.

[0074] The fusion weight value of the tth SU at the tth sensing time slot can be calculated as:

[0075] where,

[0076] , , is the trust threshold value.

[0077] Step 5, global decision stage.

[0078] Suppose SUs are qualified to participate in cooperation, mathematically, the final decision is made according to the majority fusion rule as follows:

[0079]

[0080] where, represents the always-occupied class (AY), represents the always-idle class (AN), represents the always-false class (AF).

[0081] ​​Step 6: Compare the global decision value with the local spectrum sensing value, update each reputation value, and then return to Step 2 for the next stage of sensing.

[0082] The effects of the present application can be further illustrated by the following simulation examples.

[0083] The present application is simulated based on the above scheme and examples, and the simulation is performed in a periodic manner. All simulations are performed in the MATLAB R2019a environment, and the numerical results are obtained by Monte Carlo simulation over 10000 runs; Figure 2 A graph showing the relationship between the required minimum sample size and SNR is described, with the horizontal axis representing SNR in decibels (dB) ranging from -10 dB to 0 dB. The vertical axis represents the required minimum sample size, i.e. the minimum number of observations necessary for spectrum sensing at a given false alarm probability. As can be seen from the graph, the required minimum sample size gradually decreases as the SNR increases. This is because a higher SNR means that the signal strength is stronger relative to the noise level, so it is easier to distinguish between signal and noise, thereby reducing the number of samples required for correct perception. At an SNR of -10 dB, the required minimum sample size is 724, and at an SNR of 0 dB, the required minimum sample size is significantly reduced to 15. This indicates that under better SNR conditions, fewer observations can be used to achieve the same perception performance. Figure 2 An important benchmark is provided for subsequent simulation experiment design. When performing simulations, the sample size settings under different SNR conditions are determined according to the information in Figure 2 to ensure the effectiveness and accuracy of the simulation results.

[0084] To verify the perception performance of the method proposed in the present application, it is compared and analyzed with the reputation method and the BSPRT method. Figure 3 The relationship between the correct perception probability of the three methods and the number of secondary users is given when the malicious user is set to 50%, with the number of secondary users increasing from 10 to 60. As the number of secondary users increases, the perception performance of the three methods improves. The reputation method has a low correct perception probability when the initial number of secondary users is small, the BSPRT method has a higher correct perception probability than the reputation method under the same number of secondary users, and the performance is better than the reputation method. The performance of the proposed method remains stable throughout the process of increasing the number of secondary users from small to large, and when the number of secondary users is greater than 35, the performance of the proposed method gradually approaches 1.

[0085] Figure 4The error detection probability of the three methods is given when the number of secondary users is set to 60. When the proportion of malicious users is small, the impact on the system is small, so the error detection probability of the three methods is close to 0. The error detection probability increases with the increase of the proportion of malicious users, and when the proportion of malicious users exceeds 40%, the error detection probability of the three methods rises to varying degrees, among which the reputation method rises the fastest, the performance is poor, BSPRT is second, and the proposed method shows good sensing performance. When the malicious users account for half of the total number of secondary users, the error detection probability does not exceed 0.05.

[0086] Figure 5 The relationship between the correct detection probability of the proposed method and the number of secondary users under different proportions of malicious users is given. The proportion of malicious users is set to 40%, 50%, and 60%, and the number of secondary users is set to 10 to 60. It can be seen that the less the proportion of malicious users, the better the effect. When the proportion of malicious users is 40%, the correct detection probability is close to 1, and the sensing performance remains stable. When the proportion of malicious users is 50%, the overall performance deteriorates not very quickly; when the proportion of malicious users is 60%, the system can maintain good sensing performance by increasing the number of secondary users. This is because in spectrum sensing, the increase in the number of secondary users usually means that more entities in the network can participate in spectrum sensing and data analysis, which helps to improve the overall performance of the network. When there are malicious users in the network, more secondary users mean more data points available for analysis and comparison. This makes it easier for the network to identify and exclude malicious data, improving the robustness and anti-interference ability of the system.

[0087] Figure 6 The relationship between the error detection probability of the proposed method and the number of secondary users is given, where the proportion of malicious users is set to 50%, 60%, and 70%, and the number of secondary users is set to 10 to 60. With the increase of the number of secondary users, the error detection probability tends to zero regardless of the proportion of malicious users. This shows that with the increase of the number of secondary users participating in sensing in the network, the sensing performance of the system has been significantly improved. The sensing performance of malicious users=50% is the best, followed by malicious users=60%, and malicious users=70% is the worst. This reflects that as the proportion of malicious users increases, the challenge faced by the system also increases. However, even in the case of a malicious user proportion of up to 70%, the error detection probability of the system can still decrease with the increase of the number of secondary users, which proves the strong robustness and effectiveness of the proposed method. Large-scale malicious attacks have a great impact on the system, but by increasing the number of secondary users, the method can achieve good sensing effect.

[0088] In order to better evaluate the perceptual performance of the method in resisting malicious users, different scenarios are set to test the performance of the method. Figure 7 As shown in the figure, under scenario 1, the malicious user is set as a static attack, and the number of AN attackers and AF attackers is half, respectively, and the PU does not exist and the false perceptual decision is reported in each perception; under scenario 2, the malicious user is set as a static AY attacker and AN attacker, and the number of each is half, and the PU exists and does not exist in each perception; in scenario 3, the malicious user is set as a dynamic AF attacker, and the true and false perceptual decision is alternately reported in each perception. As can be seen from the figure, the perceptual performance of scenario 3 is optimal, which shows that the method is more applicable to the scenario of dynamic attack. Because the historical perceptual result is considered, and the gain and inhibition factors are introduced, once the malicious user performs intermittent false perception, the trust value thereof will be sharply reduced under the action of the comprehensive trust value. When the proportion of malicious users is greater than 35%, the performance of scenario 2 is better than that of scenario 1, because the damage intensity of the AF attacker to the system is greater than that of the AY and AN.

[0089] Based on the concept of the method, the application also provides an anti-dynamic SSDF attack system based on a credit mechanism, which comprises an initialization module, an iteration module, a perception module, a calculation module, a fusion module and a reputation updating module.

[0090] The initialization module is used for allocating an initial reputation value to each user in the network.

[0091] The iteration module is used for repeatedly executing the following steps until a preset termination condition is met.

[0092] The perception module is used for acquiring the local spectrum sensing performed by each user, and judging whether a frequency band is occupied through observation of the surrounding environment.

[0093] The calculation module calculates a comprehensive reputation value by using the current reputation value, the historical reputation value, the gain factor and the inhibition factor.

[0094] The fusion module allocates a fusion weight to each node according to the calculated comprehensive reputation value, and performs weighted fusion on the local spectrum sensing results of the nodes by using the allocated fusion weight to obtain a decision result.

[0095] The reputation updating module is used for comparing the global decision value and the local spectrum sensing value, and updating each reputation value.

[0096] On the other hand, the application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program can realize the anti-dynamic SSDF attack method based on the credit mechanism when executed by a processor.

[0097] The application can also provide a computer device, comprising a processor and a memory, the memory being used to store a computer executable program, the processor reading the computer executable program from the memory and executing, and the processor implementing the credit mechanism based anti-dynamic SSDF attack method according to the application when executing the computer executable program.

[0098] The computer device can be a notebook computer, a desktop computer or a workstation.

[0099] The processor can be a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0100] The memory according to the application can be an internal storage unit of a notebook computer, a desktop computer or a workstation, such as a memory, a hard disk, and can also be an external storage unit, such as a mobile hard disk or a flash card.

[0101] The computer readable storage medium can include a computer storage medium and a communication medium. The computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer readable instructions, data structures, program modules or other data. The computer readable storage medium can include a read only memory (ROM), a random access memory (RAM), a solid state disk (SSD) or an optical disk. Among them, the random access memory can include a resistance random access memory (ReRAM) and a dynamic random access memory (DRAM).

[0102] The above is only to illustrate the technical idea of the application, and cannot limit the protection scope of the application. Any modification made according to the technical idea of the application on the basis of the technical scheme falls within the protection scope of the claims of the application.

Claims

1. A method for anti-dynamic SSDF attack based on credit mechanism, characterized in that, The method comprises the following steps: allocating an initial reputation value to each user in the network; repeating the following steps until a preset termination condition is met; each user performs local spectrum sensing to determine whether a frequency band is occupied by observing the surrounding environment; calculating a comprehensive reputation value using the current reputation value, the historical reputation value, the gain factor and the suppression factor; allocating a fusion weight to each node according to the calculated comprehensive reputation value; The local spectrum sensing results of each node are fused by using the assigned fusion weight to obtain a decision result D( t ). The reputation value of each user is updated according to the comparison between the global decision value and the local spectrum sensing value, and the weight of each user in the cooperative spectrum sensing process is dynamically adjusted according to the reputation value of the user; for the user whose reputation value is lower than a preset threshold, a punishment measure of reducing the weight or eliminating from the network is taken; the fusion weight value of the tth user at the tth sensing time slot can be calculated as: ​ wherein , is a trust threshold value.

2. The credit mechanism based anti-dynamic SSDF attack method according to claim 1, characterized in that, when initializing the reputation value, setting the initial reputation value according to the basic attributes of the user.

3. The credit mechanism based method of countering dynamic SSDF attacks as claimed in claim 1, wherein, Each SU receives spectrum sensing data through an antenna and can independently sense and make a binary decision; each SU transmits data to the FC through a reporting channel to realize data collection; the FC comprehensively analyzes the collected data by using a multi-data fusion technology to make a global decision, wherein, is a signal transmitted by the PU, is a channel gain from the PU to the first SU, is noise at the first SU, indicates that the PU is not occupying the licensed spectrum, indicates that the PU is using the licensed spectrum, is a signal received by the first SU, is an energy of the signal received by the first SU, is an energy threshold.

4. The credit mechanism based anti-dynamic SSDF attack method of claim 1, wherein, According to the majority fusion rule to make the final decision As follows: wherein, represents the always-occupied class, represents the always-idle class, represents the always-error class.

5. A system for anti-dynamic SSDF attack based on credit mechanism, characterized in that, The method comprises an initialization module, an iteration module, a sensing module, a calculation module, a fusion module and a reputation updating module; The initialization module is used for allocating an initial reputation value to each user in the network; The iteration module is used for repeating the following steps until a preset termination condition is met; The sensing module is used for obtaining local spectrum sensing performed by each user to determine whether a frequency band is occupied by observing the surrounding environment; The calculation module calculates a comprehensive reputation value using the current reputation value, the historical reputation value, the gain factor and the suppression factor; The fusion module allocates a fusion weight to each node according to the calculated comprehensive reputation value; Using pre-assigned fusion weights, the local spectrum sensing results of each node are weighted and fused to obtain the decision result; the weight of a user in the collaborative spectrum sensing process is dynamically adjusted according to the user's reputation value; for users with reputation values ​​below a preset threshold, penalties such as reducing their weight or removing them from the network are applied; the t-th sensing slot at the th... The fusion weight value of each SU can be calculated as follows: wherein , is a trust threshold value The reputation updating module is used for comparing the global decision value and the local spectrum sensing value to update each reputation value.

6. A computer device, comprising: A computer program product comprising a processor and a memory storing computer executable programs, the processor reading and executing some or all of the computer executable programs from the memory, the processor executing some or all of the computer executable programs to implement the method of claim 1 4. The credit mechanism based method of claim 1-3.

7. A computer-readable storage medium, characterized in that, The computer program is stored in the computer readable storage medium and is executed by the processor to realize the method in claim 1 4. The method of claim 1-3, wherein the credit-based mechanism is used to resist dynamic SSDF attacks.

8. A communication system, characterized by The method is used for communication by using the anti-dynamic SSDF attack method according to any one of claims 1-4.

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