A quasi-state defense semi-persistent scheduling method based on fuzzy mathematics

Abstract

The application discloses a quasi-state defense semi-persistent scheduling method based on fuzzy mathematics, which comprises safety performance evaluation index calculation, safety fuzzy score and fuzzy grade evaluation based on fuzzy mathematics, and execution body dynamic scheduling based on semi-persistent scheduling and fuzzy mathematics feedback. The application improves feedback index calculation and semi-persistent cycle setting in the semi-persistent scheduling method based on fuzzy mathematics, and improves the execution body scheduling method suitable for quasi-state defense technology. The safety condition of the quasi-state defense system and the change and fluctuation size of the condition are calculated by using fuzzy mathematics, the number of execution bodies is increased or decreased by using the semi-persistent scheduling method according to the calculated change and fluctuation, and finally, the execution body set is obtained by simple scheduling according to the number of execution bodies.

Description

Technical Field

[0001] The present invention relates to network security technology, and in particular to a mimicry defense semi-continuous scheduling method based on fuzzy mathematics. Background Art

[0002] With the advancement of network technology and the increasing complexity of network structures, the requirements for cyberspace security technologies are gradually increasing. Therefore, Academician Wu Jiangxing proposed mimetic defense, a new cyberspace security technology that can defend against unknown threats. The core of mimetic defense is a dynamic heterogeneous redundant structure. Numerous studies have demonstrated the security benefits of this structure, making attacks more costly and difficult for attackers. However, during mimetic defense experiments and applications, it was generally found that the dynamic heterogeneous redundant structure had varying degrees of detrimental effects on user experience performance, such as system latency, throughput, and system load factor.

[0003] Fuzzy mathematics is a mathematical theory and method for studying and processing fuzzy phenomena. The process of using concepts for judgment, evaluation, reasoning, decision-making, and control can also be described using fuzzy mathematics. In mimetic defense systems, fuzzy mathematics can be used to quantify fuzzy concepts such as "safe," "relatively safe," and "unsafe," allowing for fuzzy scoring and evaluation of the system's security status.

[0004] Semi-persistent scheduling is a semi-static resource scheduling scheme introduced by 3GPP for LTE. Semi-persistent scheduling means that after a terminal requests resources, the current scheduling scheme remains in place for a period of time under certain conditions. If this period expires, rescheduling is possible; if a threshold is reached during this period, rescheduling is mandatory. Semi-persistent scheduling reduces scheduling overhead and improves resource utilization. Summary of the Invention

[0005] In order to overcome the above-mentioned shortcomings and deficiencies of the prior art, the object of the present invention is to provide a semi-continuous scheduling method for mimicry defense based on fuzzy mathematics.

[0006] The purpose of the present invention is achieved through the following technical solutions:

[0007] A semi-continuous scheduling method for mimicry defense based on fuzzy mathematics, including:

[0008] The calculation of safety performance evaluation indicators is as follows: based on the operation information of the mimic defense system, the average heterogeneity and historical confidence of the current set of executors of the mimic defense system are obtained; based on the operation information of the executor set of the last task scheduling, the relative heterogeneity and historical confidence of each executor are calculated;

[0009] Based on fuzzy mathematics security fuzzy scoring and fuzzy grade assessment, specifically: the fuzzy score and fuzzy grade of the mimetic defense system's security status are calculated based on the average heterogeneity and historical confidence of the current set of executors of the mimetic defense system; the fuzzy score of the security performance of each executor is calculated based on the relative heterogeneity and historical confidence of each executor;

[0010] The number of executors is updated based on semi-continuous scheduling. Specifically, the length of the semi-continuous scheduling cycle is defined according to the fuzzy level of the mimetic defense system's security status, and the increase or decrease of the number of executors is determined according to the change in the fuzzy score of the mimetic defense system's security status.

[0011] Dynamic scheduling of executors based on fuzzy mathematical feedback is specifically: the set of executors that execute the current task of the system is obtained by sorting the fuzzy scores of the executor's safety performance and the weighted results of random factors in descending order, maximizing the safety performance of the executor scheduling set and adding scheduling dynamics.

[0012] Furthermore, the safety performance index calculation is specifically as follows:

[0013] Divide the functional equivalent into multiple layer_num independent functional implementation structures {layer1, layer2, ..., layer layer_num};

[0014] The heterogeneity is calculated as a heterogeneity score, and the kth independent functional layer layer of any two executors is calculated. k The heterogeneity of Laida Criteria Determine μ and σ;

[0015] Calculate the normalized heterogeneity value of any two executives to obtain the heterogeneity matrix. Each element in the matrix represents the executive E i and executive E j Heterogeneity And he i,j =he j,i ,he i＝j =0;

[0016] Calculate the average heterogeneity of a set of n executors

[0017] Compute the executor E in the heterogeneous pool i The average degree of conformation relative to other executors in the heterogeneous pool Where num_total is the number of executables;

[0018] Calculate a single executive E i Historical confidence

[0019] Calculating historical confidence in mimicry defense systems

[0020] Furthermore, the fuzzy scoring and fuzzy grade assessment based on fuzzy mathematics security are specifically as follows:

[0021] Construct fuzzy comprehensive evaluation index {very poor, poor, good, very good};

[0022] Construct factor set {heterogeneity, historical confidence};

[0023] Generate weight vector w = [w1, w2] (w1 + w2 = 1)

[0024] Define the security status fuzzy scoring matrix P = [p1, p2, p3, p4], where p1 to p4 represent the fuzzy scores of the four fuzzy levels in the corresponding review set of the mimicry defense system security status;

[0025] Substitute the input into the membership function to construct the judgment matrix R;

[0026] Calculate the comprehensive membership vector

[0027] Get the security fuzz level fuzz_state = argmax(b m );

[0028] Calculating security fuzzy scores

[0029] When input x=[x1,x2]=[feedback he ,feedback trust ], then output the fuzzy level fuzz_state and fuzzy score fuzz_score of the mimetic defense system security status;

[0030] When input x=[x1,x2]=[he_relative i ,fb_tr i ], then the output execution body E i Security performance fuzzy score selected_score i .

[0031] Furthermore, if b2 is the maximum value in vector B, the corresponding security fuzzy level is "poor".

[0032] Furthermore, the correspondence between the semi-continuous scheduling period and the fuzzy level of the mimic defense system security status is specifically as follows:

[0033] The higher the system security status fuzziness level, the longer the semi-continuous scheduling period. The current semi-continuous scheduling period is randomly selected within the semi-continuous scheduling period range corresponding to the current mimicry defense security status fuzziness level, and the counter uses a countdown method to determine the cycle progress.

[0034] Furthermore, the security status level of the mimetic defense system "very poor" corresponds to the semi-continuous scheduling period [30,80]; "poor" corresponds to the semi-continuous scheduling period [90,140]; "good" corresponds to the semi-continuous scheduling period [150,200]; and "very good" corresponds to the semi-continuous scheduling period [210,260].

[0035] Furthermore, the dynamic scheduling of the execution body based on fuzzy mathematical feedback is specifically as follows:

[0036] All executors in the heterogeneous pool generate a scheduling random seed rand_prob with a value of (0,1) i ;

[0037] Computing Executor E i Scheduling probability selected_prob i =a1selected_score i +a2rand_prob i , where rand_prob i is the random probability of generation, a1 and a2 are the weights of security and randomness respectively, and a1+a2=1;

[0038] Sort the scheduling probabilities in descending order;

[0039] By taking the first n2 executables, we can get the set of executables selected that execute the task at time t2.

[0040] Furthermore, there are two triggering methods for quantity updates: security threshold triggering and semi-continuous cycle end triggering. The security threshold triggering is to judge the current system security fluctuation based on the fuzzy score and fuzzy level of the mimetic defense system's security status to decide whether to update the number of executors and the semi-continuous scheduling period. The semi-continuous cycle end triggering is to judge whether the number of executors needs to be updated based on a probability probKeep close to 1 when the semi-continuous cycle is about to end.

[0041] Furthermore, the updating of the number of executables based on semi-persistent scheduling further includes, when the counter of the semi-persistent scheduling period is decremented to 1, determining whether the number of executables needs to be updated after the semi-persistent period ends based on probability, specifically:

[0042] If the randomly generated probability randpro is greater than or equal to the probability probKeep, the number of executors needs to be updated, and the number of executors is updated according to the fluctuation percentage of the latest meta-defence system security fuzzy score:

[0043]

[0044] Wherein, n1 represents the number of executor scheduling at t1, n2 represents the number of executor scheduling at t2

[0045] According to the security level, the semi-persistent period is updated, and the task counter is updated, and a new semi-persistent scheduling period is entered.

[0046] If the randomly generated probability randpro is less than the probability probKeep, the number of executors is unchanged, the semi-persistent period is updated according to the security level, and the task counter is updated, and a new semi-persistent scheduling period is entered.

[0047] Further, the executor number updating based on semi-persistent scheduling further comprises, when the counter of the semi-persistent scheduling period is not decremented to 1,

[0048] The meta-defence system security fuzzy score decreases by more than the threshold value Wherein, fuzz_ t1 Indicates the meta-defence system security fuzzy score at t1, fuzz_ t2 Indicates the meta-defence system security fuzzy score at t2, fuzz__ indicates the meta-defence system security fuzzy score decrease percentage threshold, and the number of executors is increased according to the meta-defence system security decrease ratio Wherein, n1 represents the number of executor scheduling at t1, n2 represents the number of executor scheduling at t2, and then the semi-persistent period SPSperiod is updated according to the meta-defence system security level t2 And the task counter is updated.

[0049] The meta-defence system security fuzzy level is "good" or "very good" when the system has been executing state_period tasks, that is, the system is excellent and stable for a long time, so that the number of executors is reduced by Q, the semi-persistent period is not updated, and the task counter is reduced by one.

[0050] If the above two system security fluctuations do not occur, the number of executors is unchanged, the semi-persistent period is not updated, and the task counter is reduced by one.

[0051] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0052] (1) Based on fuzzy mathematics, the present invention improves the feedback index calculation, semi-continuous period setting and other aspects in the semi-continuous scheduling method, and improves it into an executor scheduling method suitable for mimicry defense technology. Fuzzy mathematics is used to calculate the security status of the mimicry defense system and the changes and fluctuations in the status. The number of executors is increased or decreased using a semi-continuous scheduling method according to the calculated changes and fluctuations. Finally, a simple scheduling is performed according to the number of executors to obtain an executor set.

[0053] (2) This method retains the timeline characteristics of semi-continuous scheduling and increases the number of executors to ensure system security performance when the system security situation declines. The semi-continuous scheduling method can effectively reduce the average number of online executors during system operation, reduce additional scheduling delays, and improve the user experience of the mimic defense system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of the overall timeline of the present invention;

[0055] Figure 2 This is a schematic diagram of the overall process and data flow of the present invention;

[0056] Figure 3 This is a schematic diagram of the main process of calculating the security evaluation index based on mimicry defense in the present invention;

[0057] Figure 4 This is a schematic diagram of the fuzzy scoring and fuzzy grade assessment process based on fuzzy mathematics security of the present invention;

[0058] FIG5(a) and FIG5(h) are diagrams of membership function in fuzzy mathematics of the present invention;

[0059] Figure 6 This is a schematic diagram of the process of updating the number of executables based on semi-persistent scheduling of the present invention;

[0060] Figure 7 The figure is a flow chart of the dynamic scheduling of the execution body based on fuzzy mathematical feedback of the present invention. DETAILED DESCRIPTION

[0061] The present invention will be further described in detail below with reference to the examples, but the embodiments of the present invention are not limited thereto.

[0062] A semi-continuous scheduling method for mimicry defense based on fuzzy mathematics, whose timeline is as follows Figure 1 As shown, the patented method retains the timeline characteristics of semi-continuous scheduling and improves the semi-continuous scheduling algorithm on the timeline based on fuzzy mathematics to be suitable for the mimicry defense system.

[0063] The steps include:

[0064] S101, calculation window, consists of a security evaluation index calculation step based on mimicry defense and a fuzzy scoring and fuzzy level evaluation step based on fuzzy mathematics security;

[0065] S102. The threshold triggers the update of the number of executors, which is determined by the fuzzy score and fuzzy level of the mimic defense system's security status. If the system remains stable for a long time, the number of executors may be appropriately reduced; if the security status deteriorates rapidly, the number of executors may be appropriately increased.

[0066] S103, executor scheduling window. When the semi-continuous window of the number of executors is about to end, or the threshold triggers the update of the number of executors, the executor scheduling window is entered, and the executor scheduling set is newly scheduled based on the update of the number of executors in semi-continuous scheduling and the dynamic scheduling of executors based on fuzzy mathematical feedback.

[0067] S104, a semi-persistent window of the number of executors, the window length is a semi-persistent period SPSperiod, SPSperiod refers to the number of times or the duration of the task run by the current number of executors, and is determined by the fuzzy level of the security status of the mimic defense system.

[0068] like Figure 2 As shown, the overall structure and data flow of the mimicry defense semi-continuous scheduling method based on fuzzy mathematics disclosed by this method are as follows Figure 2 As shown:

[0069] The calculation of safety performance evaluation indicators is as follows: based on the operation information of the mimic defense system, the average heterogeneity and historical confidence of the current set of executors of the mimic defense system are obtained; based on the operation information of the executor set of the last task scheduling, the relative heterogeneity and historical confidence of each executor are calculated;

[0070] Based on fuzzy mathematics security fuzzy scoring and fuzzy grade assessment, specifically: the fuzzy score and fuzzy grade of the mimetic defense system's security status are calculated based on the average heterogeneity and historical confidence of the current set of executors of the mimetic defense system; the fuzzy score of the security performance of each executor is calculated based on the relative heterogeneity and historical confidence of each executor;

[0071] The number of executors is updated based on semi-continuous scheduling. Specifically, the length of the semi-continuous scheduling cycle is defined according to the fuzzy level of the mimetic defense system's security status, and the increase or decrease of the number of executors is determined according to the change in the fuzzy score of the mimetic defense system's security status.

[0072] Dynamic scheduling of executors based on fuzzy mathematical feedback is specifically: the set of executors that execute the current task of the system is obtained by sorting the fuzzy scores of the executor's safety performance and the weighted results of random factors in descending order, maximizing the safety performance of the executor scheduling set and adding scheduling dynamics.

[0073] Furthermore, the security evaluation index calculation step based on mimicry defense and the fuzzy scoring and fuzzy level evaluation step based on fuzzy mathematics security are specifically as follows: Figure 3 And such as Figure 4 As shown:

[0074] Divide the functional equivalent into multiple layer_num independent functional implementation structures {layer1, layer2, ..., layer layer_num};

[0075] The heterogeneity is calculated as a heterogeneity score, and the kth independent functional layer layer of any two executors is calculated. k The heterogeneity of Laida Criteria Determine μ and σ. The greater the difference, the higher the score obtained.

[0076] Calculate the normalized heterogeneity value of any two executives to obtain the heterogeneity matrix. Each element in the matrix represents the executive E i and executive E j Heterogeneity And he i,j =he j,i ,he i＝j =0, where i, j are the serial numbers of the executables and num_total is the total number of executables.

[0077] Calculate the average heterogeneity of a set of n executors

[0078] Compute the executor E in the heterogeneous pool i The average degree of structure relative to other executors in the heterogeneous pool is

[0079] Calculate a single executive E i Historical confidence

[0080] Calculating historical confidence in mimicry defense systems

[0081] Combination of safety fuzzy scoring and fuzzy grade evaluation based on fuzzy mathematics Figure 4 The specific process is as follows:

[0082] S401. Construct a fuzzy comprehensive evaluation index {very poor, poor, good, very good};

[0083] S402, construct factor set {heterogeneity, historical confidence};

[0084] Input heterogeneity and historical confidence x = [x1, x2] respectively;

[0085] S403: Generate weight vector w = [w1, w2] (w1 + w2 = 1)

[0086] S404. Define a security status fuzzy scoring matrix P = [p1, p2, p3, p4], where p1 to p4 represent the fuzzy scores of the four fuzzy levels in the corresponding review set for the security status of the mimicry defense system.

[0087] The membership function diagram in fuzzy mathematics of the present invention is shown in Figure 5(a) and Figure 5(h):

[0088] According to the actual distribution of heterogeneity and historical confidence, the membership degree is determined as a trapezoidal function and its specific parameters using the designation method.

[0089] S405. Substitute the input into the membership function shown in FIG5(a) and FIG5(h) to construct the judgment matrix R. Substitute x1 into the four-segment membership function of heterogeneity (very poor, poor, good, and very good) to obtain four values, which are the first row of the judgment matrix. Similarly, substitute x2 into the four-segment membership function of historical confidence to obtain the second row of the judgment matrix.

[0090] S406, calculate and obtain the comprehensive membership vector

[0091] S407, obtain the security fuzz level fuzz_state = argmax(b m ), for example, if b2 is the maximum value in vector B, then the security fuzzy level corresponds to “poor”;

[0092] S408. Calculate security fuzzy score

[0093] When input x=[x1,x2]=[feedback he ,feedback trust ], then output the fuzzy level fuzz_state and fuzzy score fuzz_score of the mimetic defense system security status;

[0094] When input x=[x1,x2]=[he_relative i ,fb_tr i ], then the output execution body E i Security performance fuzzy score selected_score i .

[0095] Furthermore, the corresponding relationship between the semi-duration period and the fuzzy level of the security status of the mimicry defense system is shown in Table 1, specifically:

[0096] The current number of executors scheduled is the number of times (or time period) that the system task is executed. A higher security status fuzziness level indicates a more appropriate number of executors, so the frequency of executor number updates does not need to be too high, meaning the higher the semi-persistent period, and vice versa. The current semi-persistent scheduling period is randomly selected within the range of the semi-persistent scheduling period corresponding to the current mimic defense security status fuzziness level, and the counter (timer) uses a countdown method to determine the progress of the period.

[0097] The process of updating the number of executable bodies based on semi-persistent scheduling disclosed in the present invention is as follows: Figure 6 As shown:

[0098] According to the fuzzy level of the security status of the mimicry defense system, the semi-continuous scheduling period is determined according to Table 1;

[0099] If the task counter in step S601 is decremented to 1, the semi-persistent scheduling period ends and the process continues with step S602; otherwise, the process continues with step S605.

[0100] S602 determines whether the number of executables needs to be updated after the end of the semi-persistent period based on an algorithm adjustable parameter (default is 0.8) probKeep, and randomly generates a probability randpro~U(0,1, and the probability

[0101] probKeep compares and determines whether to update the number of executables.

[0102] S603 randomly generates a probability randpro. If the randomly generated probability randpro is less than the probability probKeep, the number of executables remains unchanged, the semi-continuous period is updated according to the safety status level, and the task counter is updated to enter a new semi-continuous scheduling period.

[0103] S604 randomly generates the probability randpro. If randpro is greater than or equal to the probability probKeep, the number of executables needs to be updated. The number of executables is updated according to the fluctuation percentage of the most recent fuzzy score of the security status of the mimicry defense system:

[0104]

[0105] Among them, n1 represents the number of execution bodies scheduled at time t1, and n2 represents the number of execution bodies scheduled at time t2

[0106] The semi-continuous period is updated according to the safety status level, and the task counter is updated at the same time to enter a new semi-continuous scheduling period.

[0107] S605: If the semi-persistent scheduling period has not ended, and:

[0108] S606, the percentage of the fuzzy score of the mimicry defense system security status decreases by more than the threshold Among them, fuzz_ t1 represents the fuzzy score of the security status of the mimicry defense system at time t1, fuzz_ t2 Indicates the fuzzy score of the security status of the mimetic defense system at time t2. fuzz__ indicates the percentage threshold of the fuzzy score decrease of the security status of the mimetic defense system. The number of executors is increased according to the percentage of the decrease in the security status of the mimetic defense system. Among them, n1 represents the number of scheduled execution bodies at time t1, and n2 represents the number of scheduled execution bodies at time t2. Then according to fuzz_ t2 Update semi-period SPSperiod t2 And update the task counter;

[0109] S607: If the fuzzy security status of the mimic defense system during the execution of state_period tasks in the past was "good" or "very good", indicating that the system has been excellent and stable for a long time, Q execution bodies are reduced, the half-duration period is not updated, and the task counter is decremented by one;

[0110] S608: If the system security fluctuations described in S606 and S607 do not occur, the number of executables remains unchanged, the half-duration period is not updated, and the task counter is reduced by one.

[0111] Table 1

[0112]

[0113] Further, if Figure 7 As shown in the figure, the dynamic nature of scheduling, i.e. dynamic scheduling, is specifically:

[0114] S701. Generate a scheduling random seed rand_prob with a value of (0,1) for all executors in the heterogeneous pool. i ;

[0115] S702, calculation execution body E i Scheduling probabilityselected_ i =a1selected_e i +a2rand_prob i , where rand_pRob i is the random probability of generation, a1 and a2 are the weights of security and randomness respectively, and a1+a2=1;

[0116] S703, sorting the scheduling probabilities in descending order;

[0117] S704: Take the first n2 executables to obtain the executable set selected that executes the task at time t2.

[0118] This embodiment also provides a device, including

[0119] The safety performance evaluation index calculation module specifically obtains the average heterogeneity and historical confidence of the current set of executors of the mimic defense system based on the operation information of the mimic defense system, and calculates the relative heterogeneity and historical confidence of each executor based on the operation information of the executor set of the last task scheduling;

[0120] Based on the fuzzy mathematics security fuzzy scoring and fuzzy grade evaluation module, specifically: the fuzzy score and fuzzy grade of the mimetic defense system's security status are calculated based on the average heterogeneity and historical confidence of the current set of executors of the mimetic defense system; the fuzzy score of the security performance of each executor is calculated based on the relative heterogeneity and historical confidence of each executor;

[0121] The module for updating the number of executors based on semi-continuous scheduling specifically defines the length of the semi-continuous scheduling cycle according to the fuzzy level of the mimetic defense system's security status, and determines the increase or decrease of the number of executors based on the change in the fuzzy score of the mimetic defense system's security status;

[0122] The dynamic scheduling module of executors based on fuzzy mathematical feedback specifically sorts the executor set that executes the current task in the system in descending order based on the weighted results of the fuzzy score of the executor's safety performance and the random factor, maximizes the safety performance of the executor scheduling set and adds scheduling dynamics.

[0123] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A semi-continuous scheduling method for mimicry defense based on fuzzy mathematics, characterized in that: include: The calculation of safety performance evaluation indicators is as follows: based on the operation information of the mimic defense system, the average heterogeneity and historical confidence of the current set of executors of the mimic defense system are obtained; based on the operation information of the executor set of the last task scheduling, the relative heterogeneity and historical confidence of each executor are calculated; Based on fuzzy mathematics security fuzzy scoring and fuzzy grade assessment, specifically: the fuzzy score and fuzzy grade of the mimetic defense system's security status are calculated based on the average heterogeneity and historical confidence of the current set of executive bodies of the mimetic defense system; The fuzzy score of the safety performance of each executive body is calculated based on the relative heterogeneity and historical confidence of each executive body; The number of executors is updated based on semi-continuous scheduling. Specifically, the length of the semi-continuous scheduling cycle is defined according to the fuzzy level of the mimetic defense system's security status, and the increase or decrease of the number of executors is determined according to the change in the fuzzy score of the mimetic defense system's security status. Dynamic scheduling of executors based on fuzzy mathematical feedback is specifically: the set of executors that execute the current task of the system is obtained by sorting the fuzzy scores of the executor's safety performance and the weighted results of random factors in descending order, maximizing the safety performance of the executor scheduling set and adding scheduling dynamics.

2. The mimicry defense semi-continuous scheduling method according to claim 1 is characterized in that: The safety performance index calculation is specifically as follows: Divide the functional equivalent into multiple layer_num independent functional implementation structures {layer1, ayer2,…, ayer layer_num }; The heterogeneity is calculated as a heterogeneity score, and the Kth independent functional layer LAyer of any two executors is calculated. k The heterogeneity of Laida Criteria Determine μ and σ; Calculate the normalized heterogeneity value of any two executives to obtain the heterogeneity matrix. Each element in the matrix represents the executive E i and executive E j Heterogeneity And he i,j =he j,i ,he i＝j =0; Calculate the average heterogeneity of a set of n executors Compute the executor E in the heterogeneous pool i The average degree of conformation relative to other executors in the heterogeneous pool Where num_ is the number of executables; Calculate a single executive E i Historical confidence Calculating historical confidence in mimicry defense systems 3. The mimicry defense semi-continuous scheduling method according to claim 1, characterized in that: The fuzzy scoring and fuzzy grade assessment based on fuzzy mathematics security are specifically as follows: Construct fuzzy comprehensive evaluation index {very poor, poor, good, very good}; Construct factor set {heterogeneity, historical confidence}; Generate weight vector w = [w1, w2] (w1 + w2 = 1) Define the security status fuzzy scoring matrix P = [p1, p2, p3, p4], where p1 to p4 represent the fuzzy scores of the four fuzzy levels in the corresponding review set of the mimicry defense system security status; Substitute the input into the membership function to construct the judgment matrix R; Calculate the comprehensive membership vector Get the security fuzz level fuzz_state = argmax(b m ); Calculating security fuzzy scores When input x=[x1,x2]=[feedback he ,feedback trust ], then output the fuzzy level fuzz_state and fuzzy score fuzz_score of the mimetic defense system security status; When input x=[x1,x2]=[he_relaive i ,fb_tr i ], then the output execution body E i Security performance fuzzy score selected_score i .

4. The mimicry defense semi-continuous scheduling method according to claim 3 is characterized in that: If b2 is the maximum value in vector B, the corresponding security fuzzy level is "poor".

5. The mimicry defense semi-continuous scheduling method according to any one of claims 1 to 4, characterized in that: The corresponding relationship between the semi-continuous scheduling period and the fuzzy level of the mimic defense system security status is specifically as follows: The higher the system security status fuzziness level, the longer the semi-continuous scheduling period. The current semi-continuous scheduling period is randomly selected within the semi-continuous scheduling period range corresponding to the current mimicry defense security status fuzziness level, and the counter uses a countdown method to determine the cycle progress.

6. The mimicry defense semi-continuous scheduling method according to claim 5, characterized in that: The security status level of the mimetic defense system "very poor" corresponds to the semi-continuous scheduling period [30,80]; "poor" corresponds to the semi-continuous scheduling period [90,140]; "good" corresponds to the semi-continuous scheduling period [150,200]; and "very good" corresponds to the semi-continuous scheduling period [210,260].

7. The mimicry defense semi-continuous scheduling method according to claim 1, characterized in that: The dynamic scheduling of the execution body based on fuzzy mathematical feedback is specifically as follows: All executors in the heterogeneous pool generate a scheduling random seed rand_prob with a value of (0,1) i ; Computing Executor E i Scheduling probability selected_prob i =a1selected_score i +a2rand_prob i , where rand_prob i is the random probability of generation, a1 and a2 are the weights of security and randomness respectively, and a1+a2=1; Sort the scheduling probabilities in descending order; By taking the first n2 executables, we can get the set of executables selected that execute the task at time t2.

8. The mimicry defense semi-continuous scheduling method according to claim 1, characterized in that: There are two triggering methods for quantity updates: security threshold triggering and semi-continuous cycle end triggering. The security threshold triggering determines whether to update the number of executors and the semi-continuous scheduling period based on the fuzzy score and fuzzy level of the mimic defense system's security status. The semi-continuous cycle end triggering determines whether the number of executors needs to be updated based on a probability probKeep close to 1 when the semi-continuous cycle is about to end.

9. The mimicry defense semi-continuous scheduling method according to claim 8, characterized in that: The updating of the number of executables based on semi-persistent scheduling further includes, when the counter of the semi-persistent scheduling period is decremented to 1, determining whether the number of executables needs to be updated after the semi-persistent period ends based on probability, specifically: If the random generation probability randpro is greater than or equal to the probability probKeep, the number of executables needs to be updated. The number of executables is updated according to the fluctuation percentage of the most recent fuzzy score of the security status of the mimicry defense system: Where n1 represents the number of executors scheduled at time t1, and n2 represents the number of executors scheduled at time t2. The semi-continuous period is updated according to the security status level, and the task counter is updated at the same time to enter a new semi-continuous scheduling period; If the randomly generated probability randpro is less than the probability probKeep, the number of executors remains unchanged, the semi-continuous period is updated according to the safety status level, and the task counter is updated to enter a new semi-continuous scheduling period.

10. The mimicry defense semi-continuous scheduling method according to claim 9, characterized in that: The updating of the number of executables based on semi-persistent scheduling further includes, when the counter of the semi-persistent scheduling period does not decrease to 1, The percentage of the fuzzy score of the mimicry defense system's security status falling exceeds the threshold Among them, fuzz_score t1 Indicates the fuzzy score of the security status of the mimicry defense system at time t1, fuzz_score t2 Indicates the fuzzy score of the security status of the mimetic defense system at time t2. fuzz_score_thres indicates the percentage threshold of the fuzzy score decrease of the security status of the mimetic defense system. The number of executors is increased according to the percentage of the decrease in the security status of the mimetic defense system. Among them, n1 represents the number of executors scheduled at time t1, n2 represents the number of executors scheduled at time t2, and then the semi-period SPSperiod is updated according to the security status level of the mimic defense system. t2 And update the task counter; If the fuzzy security status of the mimetic defense system is "good" or "very good" when executing state_period tasks in the past, indicating that the system has been excellent and stable for a long time, Q executors will be reduced, the half-duration period will not be updated, and the task counter will be decremented by one; If the above two system safety fluctuations do not occur, the number of executive bodies remains unchanged, the half-duration period is not updated, and the task counter is reduced by one.

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