Cooperative attack resistant spectrum sensing method based on trust value estimation, medium and equipment

Abstract

The invention relates to the technical field of cognitive radio networks and internet security services, in particular to an anti-cooperative attack spectrum sensing method, medium and equipment based on trust value estimation, and the method comprises the following steps: dividing a time axis into a plurality of sliding windows, each window comprising a fixed time slot, node trust values are calculated according to the number of matching times of detection results in the windows and fusion judgment, then accumulated trust values are generated through weighted accumulation of the node trust values of all the sliding windows, and a data comparison algorithm utilizes the characteristic that malicious nodes can uniformly send error data during cooperative attack; according to the method, a normal time slot and a suspicious time slot are divided according to the difference degree of data received by each time slot, then trust values of other nodes are updated according to the credibility of node data in the suspicious time slot and the number of the same data nodes, and finally, the two trust values are synthesized to carry out weighted fusion on the node data. According to the invention, the accuracy and reliability of spectrum sensing are effectively improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of cognitive wireless networks and Internet security services, in particular to an anti-collusion attack spectrum sensing method based on trust value estimation, a medium and equipment. BACKGROUND

[0002] With the rapid development of wireless communication technology, the demand for spectrum resources is growing. However, the traditional fixed spectrum allocation mode leads to low utilization of spectrum resources, and many frequency bands are not fully utilized at specific times and places. Cognitive radio networks allow secondary users to utilize the time-space gaps in licensed frequency bands for communication, which is expected to alleviate the problem of spectrum resource shortage. One of the key tasks of implementing cognitive radio networks is spectrum sensing. Due to the limited spectrum sensing capability of a single cognitive user, through the integration of sensing information of multiple users, i.e. cooperative spectrum sensing, the idle spectrum can be more accurately detected. In distributed sensing technology, the SU collects data sent by surrounding SUs, and fuses the data with its own sensing results to obtain the final decision result. When the number of nodes around the SU is small, the data collected by the SU is limited, and if the wireless communication environment around the SU is poor at this time, it is likely to cause misjudgment of the SU. Therefore, some scholars have proposed an information transmission algorithm based on a topological network.

[0003] Malicious users often use collusion attacks to reduce the probability of being identified by the system. In a distributed sensing network based on a topological structure, malicious nodes communicate before the information transmission phase to determine whether to attack in the time slot. If it is decided not to attack in the time slot, the malicious nodes will send their real sensing data in the information transmission phase, hide their identities as malicious nodes, and increase their trust values in the traditional trust value estimation algorithm to prevent being identified by honest nodes. If it is decided to attack in the time slot, the malicious node group will first use their respective information to make a local decision, and after determining the sensing result, they will send data opposite to the sensing result in the information transmission phase to attack the sensing network. This collusion attack method can better hide itself and cause greater harm to the sensing network by sending consistent fake data during the attack. This collusion attack method is a difficult problem that needs to be solved for existing Internet security technology. SUMMARY

[0004] The application provides an anti-collusion attack spectrum sensing method based on trust value estimation, which effectively solves the problem of collusion attack detection and improves the robustness of the system, and can at least solve one of the above technical problems.

[0005] To solve the above technical problems, the application adopts the following technical solutions: The anti-collusion attack spectrum sensing method based on trust value estimation comprises the following steps: S1, dividing the time axis into sliding windows with length of T past k sliding windows, recording the number of times that the sensing result of each secondary user node SU m is consistent with the fusion decision result; S2, calculating the cumulative trust value m of each SU m,1 ; S3, counting the number of sensing results of spectrum occupation kT and the number of sensing results of spectrum vacancy n 0 received in each of the past n sliding windows, and marking a time slot as suspicious if the difference between the two is greater than a preset threshold m ; S4, for each honest node, comparing the sensing results of other nodes received in the suspicious time slot with the sensing result of the node itself, and screening out suspicious nodes; S5, initializing the data comparison trust value of all other nodes as 1, and reducing the data comparison trust value of suspicious nodes; S6, calculating the comprehensive trust value based on the sliding window criterion trust value R m,1 and the data comparison criterion trust value R m,2 ; R m performing weighted fusion on the sensing data to obtain the final spectrum sensing result.

[0006] Further, in the S2, the calculation method of the cumulative trust value is as follows: S2.1, defining the weight of the l th sliding window λ l ; S2.2, assuming that the matching times of SU m in the l th window is , and the trust value of SU m in the l th window is ; ; S2.3, the cumulative trust value is represented as , wherein l = 1 is the latest window.

[0007] Further, in the S2.1, considering the timeliness of historical data, the weight of the sliding window adopts a linear decay strategy, and the weightλ l is represented as .

[0008] Further, in the S3, the threshold value m is set to satisfy: m = β × N , wherein, N is the total number of nodes participating in fusion, β is an adjustable coefficient, 0.2≤ β ≤0.5.

[0009] Further, in the S4, the node whose self-similarity rate is lower than 0.5 is determined as a suspicious node.

[0010] Further, in the S5, the trust value reduction rule is that if j suspected nodes send the same data in the suspicious time slot, then the trust value reduction amount ΔR = j × γ , wherein, γ is a cooperative punishment coefficient, 0.05≤ γ ≤0.2, and the lower limit of the trust value after single reduction is 0.1.

[0011] Further, in the S6, the arithmetic weighted is used for the comprehensive trust value calculation: , wherein, θ is an adjustable weight factor, 0.3≤ θ ≤0.7.

[0012] Further, in the S6, the weighted fusion detection value formula is: , wherein, R m is the comprehensive trust value of the node SU m , and v m is the perception data of the node SU m , which is 0 or 1, and when the detection value V is greater than the decision threshold, it is considered that the honest node spectrum is occupied, otherwise it is considered that the honest node spectrum is in an idle state.

[0013] A computer readable storage medium, which stores a computer program, when the computer program is executed by a processor, causes the processor to execute the steps of the anti-cooperative attack spectrum sensing method based on trust value estimation.

[0014] A computer device, which comprises a memory and a processor, the memory stores a computer program, when the computer program is executed by the processor, causes the processor to execute the steps of the anti-cooperative attack spectrum sensing method based on trust value estimation.

[0015] The beneficial effects of the present application are embodied in: 1、The present application reduces the weight of historical data by using sliding window mechanism, adapts to the dynamic changes of collaborative attack, and accurately identifies the collaborative malicious node group through suspicious time slot screening and data consistency analysis.

[0016] 2、The present application fuses double trust evaluation, reduces the influence of malicious nodes on fusion decision, and has better system performance than traditional algorithms, especially when facing large-scale malicious node collaborative attack, the system shows good robustness. BRIEF DESCRIPTION OF DRAWINGS

[0017] The drawings described herein are used to provide further understanding of the present application, constitute a part of the present application, the illustrative embodiments of the present application and the description thereof are used to explain the present application, and do not constitute improper limitation on the present application.

[0018] Figure 1 It is the overall flowchart of the method of the embodiment of the present application.

[0019] Figure 2 It is the detailed flowchart of the method of the embodiment of the present application.

[0020] Figure 3 It is the system model schematic diagram of the embodiment of the present application.

[0021] Figure 4 It is the sliding window algorithm model schematic diagram of the embodiment of the present application.

[0022] Figure 5 It is the suspicious time slot determination schematic diagram of the embodiment of the present application.

[0023] Figure 6 It is the reliability calculation schematic diagram of the embodiment of the present application.

[0024] Figure 7 It is the suspicious data comparison schematic diagram of the embodiment of the present application.

[0025] Figure 8 It is the structure block diagram of the computer equipment of the embodiment of the present application. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. The embodiments in the present application and the features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0027] It should be noted that the meaning of "and / or" appearing throughout the text includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or solutions in which both A and B are satisfied. In addition, "multiple" refers to more than two. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0028] See also Figures 1-2 The embodiment of the present invention provides a spectrum sensing method for resisting coordinated attacks based on trust value estimation, comprising the following steps: S1, each T The time slots are divided into a sliding window, recording the past k The number of times that the perception result of each secondary user SU in a sliding window is consistent with the fusion decision result; S2, l The weight of the sliding window is recorded as λ l , secondary user SU m In the l The number of times the perception result in the sliding window is consistent with the fusion decision result is recorded as , then SU m In the l Trust value in a sliding window Expressed as , SU m Cumulative trust value Expressed as ; S3. Statistics of the past kT In each time slot, the number of occupied spectrum perception results n1 and idle spectrum perception results n0 received in each time slot are greater than the preset threshold. m , then mark the time slot as a suspicious time slot; S4. For each honest node, it compares the perception results of other nodes received during the suspicious time slot with its own perception results, selects nodes with a similarity rate lower than 0.5, and marks them as suspicious nodes; S5. Set the initial trust value of all nodes to 1, and reduce the trust value of all suspicious nodes. j If a suspicious node sends the same data in a suspicious time slot, then the j The trust value of a suspicious node is reduced by j *0.1; S6. For the trust value R with sliding window criterion m,1and data comparison criterion trust value R m,2 of the nodes, the integrated trust value of which is The received sensing data is weightedly fused based on the integrated trust value of the nodes to obtain a final spectrum sensing result.

[0029] In the above S1-S2, the following model is established: The system model is shown in Figure 3 The topology network is composed of one primary user PU and secondary users SU, each SU is randomly distributed around the PU, and it is assumed that the network topology structure and the state of the PU transmitter do not change in a sensing period, i.e. from the beginning of sensing to the end of fusion decision. In the information transmission phase, each SU collects data information from other SUs manufacturing or forwarding, and after storing these information in the database, the information is forwarded to other SUs. Most of the SUs in the sensing network are HUs that transmit real data and maintain honest sensing behavior, and some are malicious users MU that transmit false data and destroy the sensing rules. The MU will attack the sensing network in different ways to achieve the purpose of occupying the frequency band alone or causing network communication conflict.

[0030] It is assumed that regardless of whether the local decision report of each SU is forged, the remaining nodes SU do not have prior information that the local decision report of the SU is consistent with the integrated decision result , and it is assumed that the probability that the local decision report of the node SU is matched with the integrated decision result is subject to uniform distribution between 0 and 1, i.e. the probability density function is:

[0031] The detection result of the th node and the matching times between the integrated decision result and the detection result of the th node after sensing time slots are subject to a binomial distribution, so under this probability premise, the relationship between the matching times between the detection result of the th node and the integrated decision result and the probability

[0032]

[0033] In each time slot , if the detection result of the SU is consistent with the integrated decision result, then The value is 1, otherwise it is 0. time slots The accumulation of values ​​can be used to calculate this The number of times the SU’s perception result matches the comprehensive decision result in a time slot. According to the Bayesian conditional probability formula, There are a total of time slots Under the premise of the number of matches, the The probability that the detection result of SU in a time slot is consistent with the comprehensive judgment result can be expressed as:

[0034] in, Indicates that the SU is in The value "1" indicates that the results are consistent, and the value "0" indicates that the results are inconsistent.

[0035] Combined with probability density function , can be further expressed as:

[0036] Similarly, it can be expressed as:

[0037] Therefore, we can get:

[0038]

[0039]

[0040] From this we can see that in the previous Within the sensing time slot, the detection result of SU matches the comprehensive judgment result Under the premise of the second The probability and number of matches of the time slot results And observe the time slot length related.

[0041] Because collaborative attack is a long-term and dynamically changing attack method, in the trust value estimation stage, the influence of previous historical observation results on the comprehensive judgment of this stage should be reduced and their weight should be reduced. To achieve this goal, the present invention proposes a sliding window trust value update algorithm.

[0042] by Figure 4 For example, taking time as the coordinate axis, there are Sliding windows, and each sliding window contains Each sliding window has different fusion weight in the fusion decision process, in The weight value of the th sliding window is:

[0043] Wherein, , with the value increases, the weight value of the sliding window in the fusion decision is also more and more big, for the th node , its trust value in the th sliding window can be expressed as:

[0044] Wherein, is the number of time slots contained in each sliding window, represents In the th sliding window, the number of times that the perception result value delivered in the th time slot matches the last fusion decision result; The sliding window is sliding backward all the time, The trust value of the th node is also constantly updated, so in the current th sliding window, The cumulative trust value of the th node can be expressed as:

[0045] The above S3-S5, specifically includes the following content: After the end of each cycle of message passing phase, the HU node carries out proportional statistics on the collected information, if the data amount between the data "1" representing the spectrum occupied state and the data "0" representing the spectrum idle state is large, it is defaulted that the data of the perception cycle is safe, and the malicious user node group does not initiate a coordinated attack in the perception cycle; on the contrary, if the data amount between the data "1" and "0" representing the two states of the spectrum is not large, and is very close, it is very likely that a coordinated attack initiated by the malicious user node group occurs in the perception cycle to mislead the HU node to make a wrong decision, for example, in Figure 5 , assuming that the threshold is 3, the suspicious time slots in the statistical time slots are the 4th, 6th and 8th time slots.

[0046] After each round of message transmission, the HU node performs proportional statistics on the collected information. When the difference between the amount of "1" and "0" data is small and lower than the threshold, it is assumed that a coordinated attack by a malicious user group has occurred within the perception cycle. At this time, the HU node will record the transmission data of each SU node in the time slot and compare it with its own detection data.

[0047] by Figure 6 For example, assuming that node 3 is an honest node, node In the topological network, data from other nodes is collected. data information, and in In the perception time slots, we screened out The time slots of are the time slots where coordinated malicious user attacks may occur. In these time slots, the nodes The perception data of itself is , and in these time slots from nodes The data is , in these 5 suspicious time slots, the node With node There are 4 identical data, then for the node In terms of nodes The data credibility is , similarly, the nodes can be calculated separately The data credibility is .

[0048] The HU node updates the credibility of the nodes sending data based on the data collected from other SU nodes and the similarity between the collected data and its own detection data. SU nodes that send data with high similarity to the HU node are assigned higher credibility, while SU nodes that send data with low similarity to the HU node are assigned lower credibility. This allows for preliminary identification of suspicious nodes. Malicious nodes are further screened and identified among data nodes with a credibility below 0.5.

[0049] Because malicious nodes transmit consistent forged data during the message passing phase when selecting an attack, node HU performs a secondary identification of malicious nodes by observing the consistency of data between nodes with a credibility lower than 0.5 during suspicious time slots. The initial trust value of all nodes is set to 1. For nodes with a credibility lower than 0.5 that send consistent data during suspicious time slots, their trust value is lowered based on the number of nodes sending consistent data, reducing their influence on the fusion decision.

[0050] exist Figure 7 In the node After calculating the credibility of other nodes, the node Because the credibility is higher than 0.5, it is considered as an honest node and keeps its trust value as 1, while Because the credibility is lower than 0.5, it is considered as a suspicious node by the node , and the trust value of the node and the node is reduced by one order of magnitude, to 0.9, while for the suspicious node , because the data sent are consistent, the trust value of them is reduced by two orders of magnitude, to 0.8, and if there are suspicious nodes sending consistent data, the trust value of them is reduced by orders of magnitude.

[0051] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to make the processor execute the steps of the anti-collusion attack spectrum sensing method based on trust value estimation.

[0052] Referring to Figure 8 , the embodiment of the present application further provides a computer device, which comprises a memory and a processor, and the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the anti-collusion attack spectrum sensing method based on trust value estimation.

[0053] The embodiment of the present application further provides a computer program product comprising instructions which, when executed on a computer, cause the computer to carry out the steps of the anti-collusion attack spectrum sensing method based on trust value estimation.

[0054] It can be understood that the system, device and storage medium provided by the embodiment of the present application correspond to the method provided by the embodiment of the present application, and the explanation, examples and beneficial effects of the related content can refer to the corresponding part in the anti-collusion attack spectrum sensing method based on trust value estimation.

[0055] It should be understood by those skilled in the art that all or part of the steps in the embodiments of the present application can be implemented by software, hardware, firmware or any combination thereof. When implemented by hardware, all or part of the steps can be implemented in the form of a purchased standard component or a modified component. When implemented by software, all or part of the steps can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be magnetic media (for example, floppy disk, hard disk, magnetic tape), optical media (for example, DVD), or semiconductor media (for example, solid state disk (SSD)) and the like.

[0056] In summary, in order to cope with the problem of cooperative spectrum sensing facing malicious node cooperative attack, the present application proposes an anti-cooperative attack spectrum sensing method based on trust value estimation. Specifically, the time axis is divided into multiple sliding windows, each window contains a fixed time slot, and the node trust value is calculated according to the matching times of detection results and fusion decision in the window, and then the cumulative trust value is generated by weighted accumulation of all sliding window node trust values. The data comparison algorithm uses the feature that malicious nodes will send false data uniformly during cooperative attack, first divides normal time slots and suspicious time slots according to the difference degree of data received in each time slot, then updates the trust value of other nodes according to the credibility of node data in suspicious time slots and the number of same data nodes, and finally weights and fuses the node data by integrating the two trust values. In this way, through the trust value estimation method based on sliding window and data comparison, the cooperative attack problem of malicious nodes can be effectively solved, and the accuracy and reliability of spectrum sensing can be improved.

[0057] It should be understood that the examples and embodiments described herein are merely for the purposes of illustration and are not intended to limit the present application, which is defined by the claims. Various modifications or changes in addition to those discussed above can be resorted to by those skilled in the art without departing from the spirit and principle of the application, and it is intended to fall within the protection of the claims.

Claims

1. A spectrum sensing method against coordinated attacks based on trust value estimation, characterized in that: The following steps are involved: S1. Divide the time axis into sections of length T Sliding window of time slots, recording the past k Each secondary user node SU in the sliding window m The number of times the perception result is consistent with the fusion judgment result; S2. Calculate each SU m Cumulative trust value ; S3. Statistics of the past kT The number of spectrum occupied perception results received in each time slot n 1 and the number of spectrum idle perception results n 0, if the difference between the two in a certain time slot is greater than the preset threshold m , then mark the time slot as a suspicious time slot; S4. For each honest node, compare the perception results of other nodes except itself received during the suspicious time slot with its own perception results to screen out suspicious nodes; S5. Initialize the data comparison trust value of all other nodes to 1 and reduce the data comparison trust value of the suspicious node; S6. Node-based sliding window criterion trust value R m,1 and data comparison criteria trust value R m,2 Calculate the comprehensive trust value R m , perform weighted fusion on the perception data to obtain the final spectrum perception result.

2. The spectrum sensing method for resisting coordinated attacks based on trust value estimation according to claim 1, wherein: In S2, the accumulated trust value The calculation method is: S2.

1. Definition l The weight of the sliding window λ l ; S2.2, set SU m In the l The number of matches within a window is , SU m In the l Trust value in the window Expressed as ; S2.

3. Accumulated Trust Value Expressed as ,in, l = 1 is the latest window.

3. The spectrum sensing method for resisting coordinated attacks based on trust value estimation according to claim 2, wherein: In S2.1, considering the timeliness of historical data, the weight of the sliding window adopts a linear decay strategy. λ l Expressed as .

4. The spectrum sensing method for resisting coordinated attacks based on trust value estimation according to claim 1, wherein: In S3, the threshold m The settings satisfy: m = β × N ,in, N is the total number of nodes participating in the fusion, β is an adjustable coefficient, 0.2≤ β ≤0.

5.

5. The spectrum sensing method for resisting coordinated attacks based on trust value estimation according to claim 1, wherein: In S4, a node whose suspicious time slot data has a similarity with itself lower than 0.5 is determined to be a suspicious node.

6. The spectrum sensing method for resisting coordinated attacks based on trust value estimation according to claim 1, characterized in that: In S5, the trust value reduction rule is: if j If a suspicious node sends the same data in a suspicious time slot, its trust value decreases by ΔR= j × γ ,in, γ is the collaborative penalty coefficient, 0.05≤ γ ≤0.2, and the lower limit of the trust value after a single reduction is 0.

1.

7. The spectrum sensing method for resisting coordinated attacks based on trust value estimation according to claim 1, wherein: In S6, the comprehensive trust value is calculated using arithmetic weighting: ,in, θ is an adjustable weight factor, 0.3≤ θ ≤0.

7.

8. The spectrum sensing method for resisting coordinated attacks based on trust value estimation according to claim 1, characterized in that: In S6, the weighted fusion detection value formula is: ,in, R m SU m The comprehensive trust value of v m SU m The perception data is 0 or 1. V When it is greater than the decision threshold, the honest node spectrum is considered to be occupied, otherwise it is considered to be idle.

9. A computer-readable storage medium, characterized in that A computer program is stored, and when the computer program is executed by a processor, the processor is caused to perform the steps of the spectrum sensing method for resisting coordinated attacks based on trust value estimation according to any one of claims 1 to 8.

10. Computer device, characterized in that The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the spectrum sensing method for resisting coordinated attacks based on trust value estimation according to any one of claims 1 to 8.

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