A regional autonomous security cooperation spectrum sensing method, system, device and terminal

Abstract

The application belongs to the technical field of wireless communication, and discloses a regional autonomous security cooperation spectrum sensing method, system, device and terminal, determines the number of partitions k and initializes the divided regions; makes the sensing time slot n=1 and initializes the SU trust value T(n); makes regional decision and global decision by local spectrum sensing R(n), calculates Z(n) and D(n); updates the trust value T(n+1) and judges whether n≤N is true; if n≤N exists, makes n=n+1 and returns to the step of local spectrum sensing R(n); if n≤N does not exist, judges whether T i <thT exists; if T i <thT does not exist, the SU i is judged to be an honest node; if T i <thT exists, the SU i is judged to be a malicious node. The regional autonomous cooperation spectrum sensing model of the application relieves the sensing error influence caused by malicious attackers on the whole system, detects and positions to the MU, and realizes cooperation spectrum sensing with better security and accuracy. Through simulation experiments, the application has better global decision performance and good MU detection and positioning ability.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of wireless communication, and particularly relates to a regional autonomous security cooperation spectrum sensing method, system, device and terminal. BACKGROUND

[0002] At present, the intelligent mine field is a main attack and defense system for attacking tanks and ultra-low altitude flying helicopters and cruise missiles in the new combat form, and through the networking and intelligentization of the intelligent mine field, the self-detection, identification and tracking functions are realized, and the combat defense capability and the countermeasure capability are improved. Since the connection mode between the intelligent mines is an important part of realizing the intelligentization of the intelligent mine network system, it is necessary to establish the communication connection between the intelligent mine nodes and the control station.

[0003] With the mass production and use of wireless devices, the demand for spectrum resources is also increasing. Research and investigation found that even in a bustling city, the spectrum utilization rate has been at a low level for a long time. Considering the limited spectrum resources, the research based on cognitive radio proposes a dynamic spectrum allocation technology, which solves the problem of inequality between spectrum resources and user quantity.

[0004] Spectrum sensing technology is the key and premise for cognitive radio to realize dynamic spectrum allocation technology. Spectrum sensing is to detect whether there is a licensed user (primary user, PU) in the frequency band to inform the unlicensed user (secondary user, SU) whether the frequency band can be accessed. When there is no PU using the spectrum in the frequency band, the SU can temporarily use the spectrum for transmission, thereby improving the spectrum utilization rate without affecting the normal communication of the PU. When a single user performs spectrum sensing, due to the hidden terminal problem such as building shielding and multipath fading, an incorrect sensing result may be obtained, which affects the normal communication of the PU. A good solution is to use cooperative spectrum sensing. Cooperative spectrum sensing considers the results obtained by multiple users, which reduces the influence of local users due to environmental problems.

[0005] However, cooperative spectrum sensing also brings some new security problems, such as PUE (Primary User Emulation) attack, SSDF (Spectrum Sensing Data Falsification) attack, and channel blocking attack, which greatly reduces the performance of cooperative spectrum sensing.

[0006] Through the above analysis, the problems and defects of the prior art are:

[0007] (1) When a single user performs spectrum sensing, the traditional spectrum sensing will get an incorrect sensing result due to the hidden terminal problem such as building shielding and multipath fading, which affects the normal communication of the PU.

[0008] (2) The existing cooperative spectrum sensing has security problems such as mimic primary user attack, spectrum sensing data forgery attack, channel blocking attack and the like, which greatly reduce the performance of the cooperative spectrum sensing and cannot effectively identify malicious users. SUMMARY

[0009] In view of the problems in the prior art, the application provides a regional autonomous security cooperative spectrum sensing method, system, device and terminal, and particularly relates to a regional autonomous security cooperative spectrum sensing method, system, medium, device and terminal based on a trust value.

[0010] The application is implemented as follows: a regional autonomous security cooperative spectrum sensing method, the regional autonomous security cooperative spectrum sensing method comprising: dividing a sensing region into zones; initializing a trust value; zone sensing reliability weighted fusion; majority voting global sensing decision; trust value updating; and malicious node positioning.

[0011] Further, the regional autonomous security cooperative spectrum sensing method comprises the following steps:

[0012] Step one: determining the number of zones k, and equally dividing the entire sensing region into k small zones containing SUs;

[0013] Step two: initializing the trust value T(n)={T1(n),T2(n),...}={1,1,...} when the sensing time slot n=1;

[0014] Step three: the SUs perform local sensing and report the sensing result R(n) to the node with the highest trust value in the zone, and the node with a closer distance is selected when the trust values are the same;

[0015] Step four: the zone head node selects the local sensing result of the trusted SU with a trust value higher than a set threshold thZ(n) to make a zone decision; the FC collects the zone decision results Z(n) from each zone and obtains a global decision D(n) through a majority voting mechanism;

[0016] Step five: comparing the local sensing result R(n) with the global decision result D(n) to update the trust value with slow growth and fast recovery;

[0017] Step six: judging whether the sensing cycle is completed, when n≤N, then n=n+1 and returning to step three to continue sensing; otherwise, the sensing cycle is ended, and the trust value of the SU is compared with the size of the adaptive decision threshold thT, and the SU with a trust value lower than the threshold is determined as a MU, otherwise, the SU is considered as an honest SU.

[0018] Further, the equally dividing the entire sensing region into k small zones containing SUs in step one comprises:

[0019] The system sensing area is equally divided, and it is assumed that the divided areas are small enough, the sensing environment in each small area is considered to be consistent, and the SUs in the small area have the same local sensing result. When there is a malicious user MU in the small area, the sensing result is different from that of other honest users SU in the small area.

[0020] When there is one PU, one data fusion center FC and m SUs in the cooperative spectrum sensing model, there are unknown number of malicious users MU in the SUs. In the model sensing range, the whole sensing area is divided into k small areas with the same area, and each small area contains a random number of MUs. The local sensing of the SU uses the energy detection method, and the centralized cooperative spectrum sensing is used to fuse the local sensing result to make a decision. The user with the highest trust value in each area is called the regional head node of the area, and the regional head node selects the local sensing result of the trusted user (i.e. the user with a trust value higher than the threshold thZ(n)) to make a majority vote to obtain the regional decision result, and then the regional head node reports the regional decision result to the fusion center FC for global decision.

[0021] According to the purpose of spectrum sensing, the PU signal state in the spectrum is divided into two types, H1 represents that there is a PU signal in the frequency band, and H0 represents that there is no PU signal in the frequency band. The parameters of the false alarm rate P m and the false detection rate P f are used to evaluate the performance of the spectrum sensing method. The false detection rate refers to the probability that the SU detection result is H0 under H1; the false alarm rate refers to the probability that the SU detection result is H1 under H0. The sensing error rate P e =P m +P f is used to represent all the errors in the local sensing process.

[0022] Further, the threshold formula in step four is:

[0023]

[0024] In the formula, T mean (n) represents the mean value of T(n), and M k is the number of SUs in the area.

[0025] The majority voting mechanism is used to fuse the local sensing result to obtain the regional sensing decision, which specifically includes:

[0026] The local sensing result of the i-th SU in the k-th area is as follows:

[0027]

[0028] In the formula, r i(n) e {0,1}, n = 1,2,...N, there are N sensing slots in a sensing period, 0 means H0, 1 means H1 in the spectrum state; the local sensing result of all SUs in the region in the nth sensing slot is represented as The local sensing result of all SUs in the region in the whole sensing period is r = [r(1), r(2),..., r(N)].

[0029] The regional head node receives the sensing result r(n) of the nth slot and makes a majority vote decision to determine the regional decision result. Considering the reliability of the node report, the nodes with a trust value higher than thZ(n) are selected to participate in the vote, and the regional decision result Z(n) is as follows:

[0030]

[0031] Let m k be the number of SUs in the region with a trust value higher than thZ(n), when more than half of the trusted SUs report 1, the regional decision result Z(n) is determined as 1, otherwise as 0. The FC collects the regional decision reports from the regional head nodes and makes a majority vote decision to obtain the global decision result D(n), which is as follows:

[0032]

[0033] Further, in step five, the slow growth and fast recovery update of the trust value includes: adopting a "slow growth and fast recovery" trust value update strategy, the trust value based security cooperative spectrum sensing method uses the convergence value of the trust value before and after sensing to detect the abnormal behavior of the node, and then locates the malicious node through the space difference of the trust value, completes the detection and positioning of the MU, and realizes the security sensing in the cooperative spectrum sensing.

[0034] The steps of the "slow growth and fast recovery" trust value update mechanism are as follows:

[0035] (1) Before sensing starts, the initial value of the trust value T(1) = [T1(1), T2(1),...] of all SUs is 1;

[0036] (2) At the end of each slot in the sensing period, the global decision result is compared with the local sensing result of the SU, and if they are the same, the trust value is increased by 0.05, and if they are different, the trust value is reduced to 0.9 times the original value;

[0037] The trust value update mathematical formula is as follows:

[0038]

[0039] (3) After all SUs have updated their trust values, normalize the trust values:

[0040] T i T(n) = T i (n) / maxT(n);

[0041] (4) Repeat steps (2) and (3) until the sensing period ends.

[0042] Furthermore, the adaptive decision threshold thT in step six is:

[0043] thT = 0.9 * T mean (N + 1).

[0044] Another object of the present invention is to provide a regional autonomous secure cooperative spectrum sensing system applying the described regional autonomous secure cooperative spectrum sensing method. The regional autonomous secure cooperative spectrum sensing system includes:

[0045] A partition area initialization module for determining the number of partitions k and initializing the partition area;

[0046] A spectrum sensing fusion module for setting the sensing time slot n = 1 and initializing the SU trust value T(n); making reliable area decisions and global decisions on the local spectrum sensing R(n), and calculating Z(n) and D(n);

[0047] A node judgment module for updating the trust value T(n + 1) and judging whether n ≤ N is satisfied; if n ≤ N holds, then set n = n + 1 and perform local spectrum sensing for the next time slot; if n ≤ N is not satisfied, then judge whether there is T i < thT; if T i < thT does not hold, then determine that the SU i is an honest node; if T i < thT holds, then determine that the SU i is a malicious node.

[0048] Another object of the present invention is to provide a computer device. The computer device includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the steps of the described regional autonomous secure cooperative spectrum sensing method.

[0049] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by the processor, the processor executes the steps of the described regional autonomous secure cooperative spectrum sensing method.

[0050] Another object of the present invention is to provide an information data processing terminal for implementing the described regional autonomous secure cooperative spectrum sensing system.

[0051] In combination with the above technical solutions and the technical problems solved, the technical solutions to be protected by the application have the following advantages and positive effects:

[0052] First, in view of the technical problems existing in the prior art and the difficulty in solving the problems, the technical solutions to be protected by the application and the results and data in the research and development process are combined closely, and the technical problems solved by the technical solutions are analyzed in detail and profoundly, and some creative technical effects brought about after the problems are solved are described specifically as follows:

[0053] In view of the intelligent minefield networked model communication problem, it can be mapped to a cognitive radio spectrum sensing problem. Cooperative spectrum sensing can reduce the influence of shadow effect and multipath fading in local sensing on sensing performance, but the resulting malicious user attack makes the sensing effect unstable or even worse. In view of the large data processing amount of centralized spectrum sensing and the large energy consumption of distributed spectrum sensing, the application proposes a regional autonomous spectrum sensing method. First, the sensing region is divided to perform cooperative spectrum sensing in small regions, and then the regional sensing results are hard-fused to make decisions, which improves the performance of cooperative spectrum sensing and achieves the effect of detecting and positioning malicious users. Based on the trust value of cooperative spectrum sensing, the trusted nodes are selected for sensing fusion to ensure the reliability of the decision and reduce the communication loss. The convergence value of the trust value of the nodes before and after sensing with respect to the sensing time is used to detect the abnormality of the nodes. Based on the spatial difference of the regional autonomous cooperative spectrum sensing, the cumulative difference of the trust value of the nodes within the sensing period is considered to detect and position the malicious nodes. Considering that the malicious user will commit crimes multiple times, and the honest user will only make a small amount of errors, the trust value updating method of the application selects the "slow growth and fast recovery" mechanism, which makes it difficult for malicious nodes to disguise, and honest users can ensure a larger trust value. The application realizes the communication between intelligent mine nodes based on cognitive radio spectrum sensing.

[0054] The application analyzes the security problem in spectrum sensing. In order to reduce the influence of hidden terminal environment on local spectrum sensing, the spectrum sensing region is divided, and the sensing results in the region tend to be consistent under the influence of similar environment. In order to obtain accurate sensing results and solve the influence of SSDF attack in cooperative spectrum sensing on global decision results, the application implements regional management system based on trust value, and determines reliable regional decision by majority voting of trusted users, and then obtains global decision by majority voting of regional decision results. In order to detect and position MU, the trust value of SU is updated by "slow growth and fast recovery" according to the correctness of historical sensing, and MU can be detected and positioned at the end of the sensing period. Simulation experiments show that the method of the application has good global decision performance and good MU detection and positioning ability.

[0055] Second, the technical solutions as a whole or from the perspective of the product, the technical effects and advantages of the technical solutions to be protected by the present application are described as follows:

[0056] The application improves the cooperative spectrum sensing for SSDF attack behavior, and proposes a regional autonomous cooperative spectrum sensing model based on trust value, manages the sensing performance of the SU in different regions, and updates the trust value of the SU based on the "slow growth and fast recovery" mechanism, so that the trust value of the attacker (malicious user, MU) can be greatly reduced with the evolution of the sensing time, and the spatial difference of the sensing can find the MU and obtain accurate global sensing results. The regional autonomous cooperative spectrum sensing based on trust value has good performance and does not require prior information, and has stronger application. The regional autonomous cooperative spectrum sensing model based on trust value of the application can alleviate the sensing error influence of the malicious attacker on the whole system, detect and locate the MU, and realize cooperative spectrum sensing with higher security and accuracy.

[0057] Third, as the creative auxiliary evidence of the claims of the present application, it is also embodied in the following important aspects:

[0058] The technical solutions of the application fill the technical gap in the industry at home and abroad:

[0059] Based on the cooperative spectrum sensing with SSDF attack, reliable users are selected for information fusion without prior information, so that more accurate spectrum state can be obtained; and the malicious user can be accurately located according to the convergence value of the trust value and the spatial difference value. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiments of the application will be briefly introduced as follows. Obviously, the drawings described below are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0061] Figure 1 is a flow chart of the regional autonomous security cooperative spectrum sensing method provided by the embodiments of the application;

[0062] Figure 2 is a principle diagram of the regional autonomous security cooperative spectrum sensing method provided by the embodiments of the application;

[0063] Figure 3 is a system model schematic diagram provided by the embodiments of the application;

[0064] Figure 4 is a sensing report and decision schematic diagram received by the regional head node provided by the embodiments of the application;

[0065] Figure 5 is a system model node distribution schematic diagram provided by an embodiment of the present application;

[0066] Figure 6A is a global decision result error rate schematic diagram under RY attack provided by an embodiment of the present application;

[0067] Figure 6B is a global decision result error rate schematic diagram under RN attack provided by an embodiment of the present application;

[0068] Figure 6C is a global decision result error rate schematic diagram under RF attack provided by an embodiment of the present application;

[0069] Figure 7A is a MU detection rate schematic diagram under RY attack provided by an embodiment of the present application;

[0070] Figure 7B is a MU detection rate schematic diagram under RN attack provided by an embodiment of the present application;

[0071] Figure 7C is a MU detection rate schematic diagram under RF attack provided by an embodiment of the present application;

[0072] Figure 8A is a RY global decision result error rate comparison schematic diagram provided by an embodiment of the present application;

[0073] Figure 8B is a RN global decision result error rate comparison schematic diagram provided by an embodiment of the present application;

[0074] Figure 8C is a RF global decision result error rate comparison schematic diagram provided by an embodiment of the present application;

[0075] Figure 9A is a MU detection rate comparison schematic diagram under RY attack provided by an embodiment of the present application;

[0076] Figure 9B is a MU detection rate comparison schematic diagram under RN attack provided by an embodiment of the present application;

[0077] Figure 9C is a MU detection rate comparison schematic diagram under RF attack provided by an embodiment of the present application. DETAILED DESCRIPTION

[0078] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0079] In view of the problems in the prior art, the application provides a regional autonomous safe cooperative spectrum sensing method, system, device and terminal, and the application is described in detail below with reference to the drawings.

[0080] I. Explanation of embodiments. In order for those skilled in the art to fully understand how the application is specifically implemented, this part is an explanation of the embodiments of the technical scheme of the claims.

[0081] As shown in the figure, the regional autonomous safe cooperative spectrum sensing method provided by the embodiment of the application comprises the following steps: Figure 1

[0082] S101, the sensing area is divided, so as to facilitate regional cooperative spectrum sensing; each regional user performs local spectrum sensing;

[0083] S102, cooperative spectrum sensing based on trust value, selecting nodes with high trust value to perform intra-regional sensing information fusion; making global decision in a majority voting manner according to regional decision;

[0084] S103, based on the "slow growth and fast recovery" mechanism, updating the trust value through the difference between the global decision result and the local sensing; detecting the abnormality of the node by using the convergence value of the trust value of the node before and after sensing with respect to the sensing time; based on the spatial difference of the cooperative spectrum sensing trust value of the regional autonomy, analyzing the cumulative difference of the trust value of the node in the sensing period to detect and locate the malicious node.

[0085] As a preferred embodiment, as shown in the figure, the regional autonomous safe cooperative spectrum sensing method provided by the embodiment of the application specifically comprises the following steps: Figure 2

[0086] 1. System model

[0087] Although the cooperative spectrum sensing has the advantage of spatial diversity to alleviate the impact of hidden terminals, the existence of MU can still greatly reduce the sensing performance. The regional autonomous trust value updating algorithm is used in the embodiment of the application, the sensing area is first divided into small areas which are easy to manage and compare; then the reliable sensing of each area is fused based on the majority voting mechanism to obtain the global decision result; the trust value weight of each SU is updated through the location and the authenticity of the historical report, so that the influence of MU is excluded, and a more real sensing effect is obtained; finally, the MU can be detected and located, and the safe cooperative spectrum sensing is realized.

[0088] 1.1 Regional autonomous model

[0089] ​​The system sensing area is equally divided, and it is assumed that the sensing environment (hidden terminal phenomenon) in each small area is the same, so the SU in the small area should have the same local sensing result. When there are malicious users MU (the number of MU accounts for a minority of the total users) in the small area, the sensing result is different from that of other honest users SU in the small area; the local sensing result in each small area is fused to ensure the rationality of the result in the area and reduce the influence of the false sensing result on the regional sensing decision result in the case of a small number of MU attacks; and the regional decision result is fused to obtain the final global decision result, which ensures the correctness of the overall sensing result and avoids the influence of the regional sensing error caused by a large number of MU in a small area on the global decision result.

[0090] The system model provided by the embodiment of the application is as shown in Figure 3 The model assumes that there is one PU, one data fusion center (FC) and m SUs in the cooperative spectrum sensing model, and there are an unknown number of malicious users MU in the SUs. In the model sensing range, the entire sensing area is divided into k small areas of the same area, and each small area may contain a random number of MU. The local sensing of the SU uses the energy detection method, which is simple and practical; the local sensing result is fused and decided by the centralized cooperative spectrum sensing. The user with the highest trust value in each area is called the regional head node of the area, and the regional head node selects the local sensing result of the trusted user for regional decision, and then reports the regional decision result to the fusion center FC for global decision.

[0091] According to the purpose of spectrum sensing, the PU signal state in the spectrum is divided into two types, H1 represents that there is a PU signal in the frequency band at this time, and H0 represents that there is no PU signal in the frequency band at this time. In order to evaluate the performance of the spectrum sensing method, the parameters of the false alarm rate P m and the false alarm rate P f are used. The false alarm rate refers to the probability that the SU detection result is H0 under H1; the false alarm rate refers to the probability that the SU detection result is H1 under H0. The sensing error rate P e = P m + P f used in the embodiment of the application represents all errors in the local sensing process.

[0092] 1.2 SSDF attack

[0093] The SSDF attacker first obtains the true information of the spectrum state during sensing, but due to its attack, it sends the opposite sensing result when reporting to other users or the FC. The fusion of the false sensing result may obtain a false decision result, which interferes with the normal use of the PU or the selfish occupation of the spectrum by the MU. The SSDF attack can be divided into the following three forms:

[0094] • Random Yes (RY) attack: the malicious user MU always sends the local sensing result of the existence of PU signal with a certain attack probability, regardless of the sensing result, to make other users mistakenly think it is H1 state and not use the frequency band, so as to achieve the purpose of monopolizing the frequency band. A special case of RY attack is Always Yes (AY) attack, whose attack probability is 1, that is, the MU always sends the information of the existence of PU signal.

[0095] • Random No (RN) attack: the malicious user MU sends the local sensing result of the nonexistence of PU signal with a certain attack probability, regardless of the sensing result, to make other users mistakenly think it is H0 state and possibly access the frequency band, so as to cause the SU to access the frequency band and interfere with the normal work of the PU. A special case of RN attack is Always No (AN) attack, whose attack probability is 1, that is, the MU always sends the information of the nonexistence of PU signal.

[0096] • Random False (RF) attack: when the sensing state is H0, the MU forges the sensing data with a certain probability and sends the information in H1 state, or when the sensing state is H1, the MU sends the false data in H0 state with a certain probability. When the attack probability is 1, it becomes a special case of RF attack, Always False (AF) attack.

[0097] AY, AN and AF attacks have too high attack frequency and are easy to be discovered by the system.

[0098] 1.3 Majority voting mechanism

[0099] There are two ways of fusion of local sensing results in cooperative spectrum sensing, namely hard fusion and soft fusion. The soft fusion way has too much information transmission and energy consumption, while the hard fusion way has small transmission and consumption and simple calculation. The hard fusion way has "AND" rule, "OR" rule and majority voting rule. Considering the local sensing error or malicious attack of reporting false sensing information, the use of "AND" rule and "OR" rule will lead to the error of fusion decision result, so the embodiment of the application selects the majority voting mechanism for fusion.

[0100] The local sensing result of the ith SU in the kth region is shown in formula (1), r i (n)∈{0,1},n=1,2,...N, there are N sensing time slots in a sensing period, and 0 in the sensing result represents H0 and 1 represents H1. Let M k be the number of SUs in the region, and the local sensing result of all SUs in the region in the nth sensing time slot is The local sensing result of all SUs in the region in the whole sensing period is r=[r(1),r(2)...,r(N)].

[0101]

[0102] The regional head node receives the sensing result r(n) of the nth time slot and makes a majority voting decision to determine the regional decision result as shown in equation (2). Considering the reliability of the node report, the node with a trust value higher than thZ(n) is selected to participate in the voting. When more than half of the users in the result report 1, the regional decision result Z(n) is determined as 1, otherwise as 0. The obtained regional decision result Z(n) is as shown in the following equation:

[0103]

[0104] As shown in Figure 4 , assuming that the regional head node receives the sensing report of the SU above Figure 4 , the regional decision made by the majority voting mechanism is shown as Z in the figure, wherein m k is the number of SUs participating in the regional decision.

[0105] Similar to the regional decision, the FC collects the regional decision report from each regional head node and makes a majority voting decision to obtain the global decision result D(n), and the mathematical formula is as shown in equation (3).

[0106]

[0107] 1.4 Trust value updating mechanism

[0108] In order to detect and locate the malicious node MU, the trust value based cooperative spectrum sensing has good effect. Since the SSDF attack mode is various, the robustness of the corresponding defense strategy designed for a kind of attack is poor. The trust value updating strategy of "slow growth and fast recovery" is adopted in the embodiment of the application, which can greatly reduce the malicious behavior, and the trust value of the honest node which is reduced due to accidental error can also be increased slowly. The safety cooperative spectrum sensing method based on the trust value in the embodiment of the application utilizes the convergence value of the trust value of the node before and after sensing in the time difference to detect the abnormal behavior of the node, and then locates the malicious node through the spatial difference, so as to complete the detection and location of the MU, and realize the safety sensing in the cooperative spectrum sensing.

[0109] The steps of the "slow growth and fast recovery" trust value updating mechanism provided by the embodiment of the application are as follows:

[0110] 1. Before sensing, the trust value T(1) of all SUs is initialized to 1, that is, T(1)=[T1(1),T2(1)...,T m (1)].

[0111] 2. At the end of each time slot of the sensing period, compare the global decision result with the SU local sensing result, if the same, increase the trust value by 0.05, if different, decrease the trust value to 0.9 times of the original value. The trust value updating mathematical formula is shown as formula (4);

[0112]

[0113] 3. After all the SUs update the trust value, normalize the trust value:

[0114] T i (n)=T i (n) / maxT(n)(5)

[0115] 4. Repeat steps 2 and 3 until the sensing period ends.

[0116] 1.5 Trust value-based regional autonomous spectrum sensing model

[0117] Considering that the MUs are randomly hidden in the whole sensing area, in order to detect and locate the MUs, a trust value is defined for each SU. The size of the trust value determines whether the SU can make a regional decision and the trustworthiness of the SU, which to some extent alleviates the influence of the channel problem on the detection result of the SU. The local sensing result of the SU with a high trust value in the region is selected for regional decision fusion, and the global decision result of the cooperative spectrum sensing depends on the regional decision and the trust value of the SU in the region. With the progress of the sensing activity, the trust value of the SU is updated in combination with the historical trust value and the accuracy of the current sensing result; at the end of the sensing period, the SU with a trust value lower than a threshold value is determined as the MU.

[0118] The trust value-based regional autonomous cooperative sensing algorithm process provided by the embodiment of the application is as shown in Figure 2 .

[0119] The trust value-based regional autonomous cooperative sensing algorithm steps provided by the embodiment of the application are as follows:

[0120] 1. Determine the number of partitions k, and divide the whole sensing area into k small areas containing SUs;

[0121] 2. Let the sensing time slot n=1, and initialize the trust value T(n) of each SU;

[0122] 3. The SU performs local sensing, and reports the sensing result R(n) to the node with the highest trust value in the region (when the trust values are the same, the closer one is selected);

[0123] 4. The regional head node selects the local sensing result of the trusted SU with a trust value higher than a set threshold value thZ(n) for regional decision, and the formula of the threshold value is shown as formula (6), T mean(n) represents the mean value of T(n); FC collects the regional decision results Z(n) from each region, and obtains the global decision D(n) through a majority voting mechanism;

[0124]

[0125] 5. Comparing the local perception result with the global decision result, updating the trust value according to formula (4) with "slow growth and fast recovery";

[0126] 6. Judging whether the perception cycle is completed, when n<=N, n=n+1 and returning to step 3 to continue completing the perception cycle; otherwise, the perception cycle is ended, and the size of the SU trust value and the adaptive decision threshold value is compared, and the SU with a trust value lower than the threshold value is judged as the MU, otherwise, the SU is considered as the honest SU. The adaptive decision threshold value thT is:

[0127] thT=0.9*T mean (N+1) (7)

[0128] The regional autonomous security cooperation spectrum perception system provided by the embodiment of the application comprises:

[0129] The partition region initialization module is used for determining the partition number k and initializing the partition region;

[0130] The spectrum perception fusion module is used for letting the perception time slot n=1 and initializing the SU trust value T(n); performing reliable regional decision and global decision on the local spectrum perception R(n), and calculating Z(n) and D(n);

[0131] The node judgment module is used for updating the trust value T(n+1) and judging whether n<=N is satisfied; if n<=N is satisfied, n=n+1 is let and the local spectrum perception of the next time slot is performed; if n<=N is not satisfied, whether T i <thT is judged; if T i <thT is not satisfied, the SU i is judged as the honest node; if T i <thT is satisfied, the SU i is judged as the malicious node.

[0132] II. Application Embodiment. In order to prove the creativity and technical value of the technical scheme of the application, this part is the application embodiment of the technical scheme of the claim on the specific product or related technology.

[0133] The application realizes safe and reliable spectrum sensing in wireless communication, helps to reduce the interference caused by malicious users to the spectrum, and improves the spectrum utilization. For spectrum sensing between intelligent mines in an intelligent mine field, the intelligent mine field is first divided into regions, and the intelligent mine nodes in a small region perform sensing respectively; the local sensing results of the nodes with high trust values in the region are selected to participate in majority voting to obtain reliable regional sensing decisions; and the regional decisions are hard-fused to obtain a final global decision. The accuracy of the sensing result obtained in this way is improved, and the trust value of the malicious node is obviously reduced through the sensing period trust value updating mechanism, so that the malicious node is located. The communication between intelligent mines in the intelligent mine field can obtain accurate environmental information in this spectrum sensing, and find a spectrum hole that can be used for communication.

[0134] III. Evidence of the effects related to the embodiments. The embodiments of the application have achieved some positive effects in the development or use process, and indeed have great advantages compared with the prior art. The following contents are described in combination with the data, charts and the like in the test process.

[0135] In order to verify the effectiveness of the algorithm, the regional autonomous cooperative spectrum sensing algorithm is simulated. It is assumed that the sensing region is 10km*10km, the PU is located at the center position, and 20 SUs are randomly scattered around, including an unknown number of MUs, as shown in Figure 5 The system model node distribution example is shown. In the experiment, the entire region is divided into four square small regions with equal areas. It is assumed that the sensing period N=100, the PU signal existence probability P pu =0.6. The MUs respectively perform falsification on the local sensing results in three attack modes of RandomYes, RandomNo and RandomFalse, and the MU attack probability is set to a=0.5.

[0136] 1. Global decision result error rate

[0137] Under different local sensing error rates, the global decision result error rate of the regional autonomous cooperative spectrum sensing model based on trust value obtained by the Monte Carlo experiment is shown in Figures 6A-6C .

[0138] It can be observed from Figures 6A-6C that under the three attacks, the global decision result error rate will increase with the local sensing error rate P eThe local perception error is a direct cause of the global decision error, but it is inevitable; according to the experimental effect, although the global decision error rate shows an upward trend, it is effectively inhibited in the presence of the MU. The global decision result error rate increases with the increase of the MU proportion, and the more the number of the MU, the worse the global decision result, which reflects that the existence of the MU can affect the normal spectrum sensing result; with the MU proportion in the SU increasing by several times, the global decision error rate is not increased by several times, which shows that the regional autonomous cooperative sensing based on the trust value can effectively identify the MU and make a reasonable security decision on the global decision result. Meanwhile, under the joint influence of the MU attack and the local perception error, the global decision error rate is still lower than the local perception error rate. When P e is less than 0.1, the global decision error rate is less than 0.2 when the MU proportion is less than 0.5 under the three attacks, and the decision effect is good.

[0139] 2, MU detection rate

[0140] Under the same experimental conditions, the MU detection rate of the regional autonomous cooperative spectrum sensing method based on the trust value changes with the local perception error rate as shown in Figures 7A-7C .

[0141] As can be seen from Figures 7A-7C , in the three attack experiments for detecting the MU, the MU detection rate decreases with the increase of the local perception error rate; the local perception error can make the honest SU report the wrong local perception result, which leads to the decrease of the trust value and misleads the system to make a correct detection, so the MU detection rate decreases. When P e is the same, the greater the MU proportion, the more obvious the change of the trust value, and the higher the MU detection rate. When the MU proportion is small (P mu = 0.1), with the increase of the local perception result error rate, the attack of the MU on the system is not as great as the influence of the error of the system itself on the global perception, so the detection effect is poor; when the local perception result error rate is large, the MU has a malicious influence on all the SUs in the system, so the MU detection rate decreases. When P e is less than 0.05, the MU detection rate is higher than 0.7 when the MU proportion is less than 0.5 under the three attacks, and the detection performance is good.

[0142] 3, comparison between the method considering the trust value and the method not considering the trust value

[0143] In order to detect the performance of the method, the global decision result error rate and the MU detection rate of the method proposed in the application and the algorithm not considering whether the user is a trusted user when performing regional perception are compared, and the simulation experimental results are shown in Figures 8A-8C , Figures 9A-9C .

[0144] FromFigures 8A-8C , Figures 9A-9C As can be seen from Table 2, in the case of the MU ratio of 0.2, 0.3 and 0.4, the performance of the method not considering the trusted SU is slightly better than the method proposed in the present application in the comparison of the global decision result error rate in the three attacks, except for the RN attack. In the MU detection, the method proposed in the present application has obvious advantages when the P e is less than 0.3, and the higher local sensing error rate should not occur in the actual situation. Therefore, the performance of the region autonomous cooperative spectrum sensing based on the trust value proposed in the present application is better.

[0145] It should be noted that the embodiments of the present application can be realized by hardware, software or the combination of software and hardware. The hardware part can be realized by special logic; the software part can be stored in the memory and executed by the appropriate instruction execution system, such as microprocessor or special designed hardware. Those skilled in the art can understand that the above-mentioned devices and methods can be realized by computer executable instructions and / or contained in the processor control code, such as the carrier medium, such as magnetic disk, CD or DVD-ROM, programmable memory, such as read-only memory (firmware) or data carrier, such as optical or electronic signal carrier. The device of the present application and its modules can be realized by hardware circuit, such as ultra large scale integrated circuit or gate array, semiconductor, such as logic chip, transistor, or programmable hardware device, such as field programmable gate array, programmable logic device, or software executed by various types of processors, or the combination of the above-mentioned hardware circuit and software, such as firmware.

[0146] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any modification, equivalent replacement and improvement within the technical range disclosed in the present application, which is made by those skilled in the art in the spirit and principle of the present application, should be covered in the protection scope of the present application.

Claims

1. A method for regional autonomous security cooperative spectrum sensing, characterized in that, The regional autonomous security cooperation spectrum sensing method comprises the following steps: determining the number of partitions k and initializing the divided regions; setting a sensing time slot and initializing the SU trust value ; making regional decisions and global decisions from the local spectrum sensing results , calculating and ; updating the trust value T(n+1) and judging whether there is ; if there is , setting and returning to the local spectrum sensing result step; if there is not , continuing to judge whether there is T i thT; if there is not T i thT, judging that the SU i is an honest node; if there is T i thT, judging that the SU i is a malicious node; and specifically comprising the following steps: Step one, determine the number of partitions k, divide the entire sensing area into k small areas containing SU; Step two, initialize a trust value for each SU ;​ Step three, the SU performs local sensing and reports the local spectrum sensing result to the node with the highest trust value in the region, or the node with the closest distance if the trust values are the same. reports to the node with the highest trust value in the region, or the node with the closest distance if the trust values are the same. Step four, regional head node selects the trust value higher than the set threshold of the local perception results of the trusted SUs; the FC collects the regional decision results from each region , and gets the global decision result through the majority voting mechanism ; Step five, compare the local sensing results with the global decision results, and update the trust value with slow growth and fast recovery; Step six: Determine whether the perception cycle has been completed. At that time, Return to step three to continue sensing; otherwise, the sensing cycle ends. Compare the SU trust value with the adaptive judgment threshold. If the value is lower than the threshold, it is judged as MU; otherwise, it is considered as honest SU.

2. The method of claim 1, wherein the regional autonomous security cooperation spectrum sensing is characterized by, The step one of dividing the entire sensing area into k small areas containing SU includes: The system sensing area is divided by equal area, if the sensing environment in each small area is consistent, the SU in the small area has the same local sensing result; when there is a malicious user MU in the small area, the sensing result is different from the result of other honest users SU in the small area; the local sensing results in each small area are fused, and the final global decision result is obtained by fusing the decision results of each area; When there is a primary user PU, a data fusion center FC and m SU in the cooperative spectrum sensing model, there are unknown number of malicious users MU in the SU; in the model sensing range, the entire sensing area is divided into k small areas with the same area, each small area contains a random number of MU; the local sensing of SU uses energy detection method, and the local sensing results are fused and decided by centralized cooperative spectrum sensing; the user with the highest trust value in each area is called the regional head node of the area, and the regional head node selects the local sensing result of the trusted user for regional decision, and then reports the regional decision result to the fusion center FC for global decision; According to the purpose of spectrum sensing, the PU signal state in the spectrum is divided into two types, indicating the presence of a PU signal in the frequency band, indicating the absence of a PU signal in the frequency band; the parameters of the false alarm rate and the missed detection rate are used to evaluate the performance of the spectrum sensing method; the missed detection rate refers to the probability that the SU detection result is in the case of ; the false alarm rate refers to the probability that the SU detection result is in the case of ; the sensing error rate is used to represent all errors in the local sensing process.

3. The method of claim 2, wherein the regional autonomous security collaboration spectrum sensing is characterized by, The threshold formula in step four is: ； wherein represents the mean value of The local sensing results are fused by majority voting mechanism to obtain regional sensing decision, which specifically includes: The local sensing result of the ith SU in the kth area is as follows: ； In the formula, , there are N sensing slots in a sensing period, 0 in the sensing result represents , and 1 represents ; let be the number of SUs in the region, and the local sensing result of all SUs in the region in the nth sensing slot is , and the local sensing result of all SUs in the region in the entire sensing period is ; The regional head node receives the sensing result of the nth time slot The regional decision result is determined by majority voting decision. Considering the reliability of the node report, the nodes with trust value higher than are selected to participate in voting, and the regional decision result obtained is as shown in the following formula: ； Record For the area, the trust value is higher than The number of SU, when a half and above number of users in the trusted SU report 1, the area decision result Is determined as 1, otherwise 0; FC collects the area decision reports from each area head node and makes a majority vote decision to get the global decision result , the mathematical formula is as follows: 。 4. The method of claim 1, wherein the regional autonomous security cooperation spectrum sensing is characterized by, In step five, the slow growth and fast recovery of the trust value includes: adopting the "slow growth and fast recovery" trust value updating strategy, the security cooperative spectrum sensing method based on trust value detects the abnormal behavior of the node by using the convergence value of the trust value of the node before and after sensing, and then locates the malicious node through the space difference of the trust value, completes the detection and positioning of the MU, and realizes the security sensing in the cooperative spectrum sensing; The steps of the "slow growth and fast recovery" trust value updating mechanism are as follows: (1) The trust value of all SUs before the sensing starts The initial value is 1; (2) At the end of each time slot in the sensing period, the global decision result is compared with the local sensing result of the SU, if they are the same, the trust value is increased by 0.05, and if they are different, the trust value is reduced to 0.9 times of the original value; The mathematical formula of trust value updating is as follows: ； (3) After all SUs update the trust value, the trust value is normalized: ； (4) Repeat steps (2) and (3) until the end of the sensing period.

5. The method of claim 1, wherein the regional autonomous security cooperative spectrum sensing is characterized by, Adaptive decision threshold in step six is: 。 6. A regional autonomous security cooperative spectrum sensing system applying the regional autonomous security cooperative spectrum sensing method according to any one of claims 1-5, the regional autonomous security cooperative spectrum sensing system comprising: A partitioning area initialization module for determining the number of partitions k and initializing the partitioning area; a spectrum sensing fusion module for ordering the sensing time slots and initializing the SU trust value ; local spectrum sensing reliable region decision and global decision, computing and ; The node judgment module is used to update the trust value T(n + 1) and judge whether it satisfies ; if holds, then let and perform local spectrum sensing for the next time slot; if it does not satisfy , then judge whether there exists T i < thT; if T i < thT does not hold, then determine that SU i is an honest node; if T i < thT holds, then determine that SU i is a malicious node.

7. A computer device comprising a memory and a processor, the memory storing a computer program, and the computer program being executed by the processor to make the processor execute the steps of the regional autonomous security cooperative spectrum sensing method according to any one of claims 1-5.

8. A computer readable storage medium storing a computer program, the computer program, when executed by a processor, causing the processor to perform the steps of the method for regional autonomous security cooperative spectrum sensing according to any one of claims 1-5.

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