Eavesdropping prevention method and device for wireless intelligent voice control system

Abstract

The present application relates to an eavesdropping defense method and device for a wireless intelligent voice control system. The method includes: constructing a two-stage dynamic Bayesian game model between the eavesdropper and the server end that adapts to the wireless channel environment, defining the strategies, benefits and states of the eavesdropper and the server end; the server end dynamically adjusts the two-stage dynamic Bayesian game model by recording and updating the attack behavior of the eavesdropper; the server end calculates the total value of the intelligent voice control system in the wireless channel environment and the signal strength under the interference signal, and determines the eavesdropping and defense costs; the server end constructs a bipartite graph, calculates the weight matrix of the bipartite graph edge, and uses the Hungarian algorithm to calculate the optimal defense strategy. Compared with traditional solutions, this method is more flexible and effective in dealing with diverse scenarios, and in the case of limited resources, it improves the server's benefits and resource utilization efficiency, effectively enhances the anti-eavesdropping capability in a wireless environment, and improves the success rate of defending against eavesdropping.

Description

Technical Field

[0001] The present application relates to the field of security technology, and in particular to an eavesdropping defense method and device for a wireless intelligent voice control system. Background Art

[0002] The intelligent voice control system is an important human-computer interaction component of the intelligent system. The use scenarios headed by intelligent systems such as smart home and smart driving cannot do without the support of the intelligent voice control system. In the context of the continuous development of intelligent systems in today's society, the development of intelligent voice control systems is also accelerating.

[0003] In order to realize the application of voice control system, it is necessary to deploy sound collection equipment in the use scenario and associate it with the smart device that needs to be controlled. In order to achieve accurate and fast voice recognition, or even to verify the voice of a specific individual, it is necessary to train the corresponding machine learning model. However, due to the limited performance of the local terminal, it is impossible to meet the training requirements of machine learning, or even the operation requirements of the model. Therefore, voice control devices usually upload sound data and control data to the server of their production company for processing and training.

[0004] Given the networking requirements of multiple terminals in smart scenes, wireless networking is a common choice for smart scenes, and smart voice control systems are no exception. This introduces new security issues. Compared with wired transmission, wireless channels are more susceptible to interference and eavesdropping, and the eavesdropping problem is particularly prominent in the application of wireless smart voice control systems. Unauthorized visitors may exploit the vulnerability of wireless channels and try to steal or tamper with the user's voice control system data, resulting in sound data leakage, service interruption, or abnormal operation of the voice control system. These security risks not only affect the user's service experience and infringe on user privacy, but may also have a serious impact on the market reputation of voice control system companies, and even endanger users or social security in scenarios such as smart driving.

[0005] Current research mainly focuses on the functions and data management of intelligent voice control systems, and research on wireless communication security is relatively insufficient. In a wireless environment, it is crucial to ensure the secure transmission of intelligent voice control system data. The main challenges include but are not limited to the following aspects:

[0006] Real-time adaptability: The intelligent voice control system must have real-time adaptability to cope with eavesdropping in a dynamically changing wireless channel environment. Wireless channels are more susceptible to external interference and eavesdropping, and require rapid adjustment and optimization capabilities to ensure the stability and security of data transmission. In the process of attack and defense, the defense strategy is dynamically adjusted according to the attacker's strategy to improve the success rate of defense.

[0007] Limited defense resources: Intelligent voice control systems face diverse and dynamic security threats, but defense resources are limited. It is crucial to effectively configure and manage limited defense resources to improve the security and anti-attack capabilities of the system.

[0008] Differences in node capabilities: In intelligent voice control systems, different intelligent voice control systems have different loads and different node locations in application scenarios, so they should be protected to different degrees. Summary of the invention

[0009] Based on this, it is necessary to provide a method and device for preventing eavesdropping of a wireless intelligent voice control system in response to the above technical problems.

[0010] A method for preventing eavesdropping of a wireless intelligent voice control system, the method comprising:

[0011] A two-stage dynamic Bayesian game model between the eavesdropper and the server that adapts to the wireless channel environment is constructed, and the strategies, benefits and states of the eavesdropper and the server are defined.

[0012] The server side dynamically adjusts the two-stage dynamic Bayesian game model by recording and updating the eavesdropper's attack behavior.

[0013] The server side calculates the total value of the intelligent voice control system in the wireless channel environment and the signal strength under interference signals to determine the eavesdropping and defense costs.

[0014] The server constructs a bipartite graph, calculates the weight matrix of the bipartite graph edges, and uses the Hungarian algorithm to calculate the optimal defense strategy based on the weight matrix.

[0015] An eavesdropping defense device for a wireless intelligent voice control system, the device comprising:

[0016] The two-stage dynamic Bayesian game model building module is used to build a two-stage dynamic Bayesian game model between the eavesdropper and the server that adapts to the wireless channel environment, and defines the strategies, benefits and states of the eavesdropper and the server.

[0017] The dynamic adjustment module is used to dynamically adjust the two-stage dynamic Bayesian game model by recording and updating the attack behavior of the eavesdropper through the server.

[0018] The wireless channel modeling module is used to calculate the total value of the intelligent voice control system in the wireless channel environment and the signal strength under the interference signal through the server side, and determine the eavesdropping and defense costs.

[0019] The defense strategy generation module is used to construct a bipartite graph through the server, calculate the weight matrix of the bipartite graph edges, and calculate the optimal defense strategy using the Hungarian algorithm based on the weight matrix.

[0020] The above-mentioned wireless intelligent voice control system eavesdropping defense method and device, the method includes: constructing a two-stage dynamic Bayesian game model between the eavesdropper and the server end that adapts to the wireless channel environment, defining the strategies, benefits and states of the eavesdropper and the server end; the server end dynamically adjusts the two-stage dynamic Bayesian game model by recording and updating the eavesdropper's attack behavior; the server end calculates the total value of the intelligent voice control system in the wireless channel environment and the signal strength under the interference signal, and determines the eavesdropping and defense costs; the server end constructs a bipartite graph, calculates the weight matrix of the bipartite graph edge, and uses the Hungarian algorithm to calculate the optimal defense strategy based on the weight matrix. Compared with traditional solutions, this method is more flexible and effective in dealing with diverse scenarios, and in the case of limited resources, it improves the server's benefits and resource utilization efficiency, effectively enhances the anti-eavesdropping capability in a wireless environment, and improves the success rate of defending against eavesdropping. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of a flow chart of an eavesdropping defense method for a wireless intelligent voice control system in one embodiment;

[0022] Figure 2 is a flowchart of the interaction between the wireless intelligent voice control system, the server and the eavesdropper in one embodiment;

[0023] Figure 3 The structure block diagram of the eavesdropping protection device of the wireless intelligent voice control system in one embodiment. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0025] In one embodiment, Figure 1 As shown, a method for preventing eavesdropping of a wireless intelligent voice control system is provided, the method comprising the following steps:

[0026] Step 100: Construct a two-stage dynamic Bayesian game model between the eavesdropper and the server that adapts to the wireless channel environment, and define the strategies, benefits, and states of the eavesdropper and the server.

[0027] Specifically, the entities involved in this method include a wireless intelligent voice control system, an eavesdropper, and a server. This method establishes a Bayesian game model that adapts to the wireless channel environment, takes into account the impact of the wireless environment on the defense success rate, and adjusts the strategies and probability distribution of the participants in real time, making the present invention more flexible and effective in dealing with diverse scenarios compared to traditional solutions.

[0028] A Bayesian game is a game in which the participants do not have complete information about the type of their opponents. Therefore, a Bayesian game is also called an incomplete information game.

[0029] The participants in the game include the eavesdropper and server side The eavesdropper's strategy set Including channel monitoring ( )、Signal Interference( ), Do not attack ( ). The server-side policy set Includes: Defense ( ) and no defense ( ). The cost and benefit parameters of the eavesdropper include the monitoring cost , Penalty for Monitoring , Information Benefits , interference cost , Voice Control System Value , the defense energy consumption on the server side is .

[0030] Establish a two-stage dynamic Bayesian game model: formulate the optimal defense strategy by predicting the eavesdropper's behavior and adjust it in real time according to the actual situation, so that the intelligent voice control system can provide more flexible and effective defense strategies in a dynamically changing environment, thereby enhancing system security.

[0031] Step 102: The server dynamically adjusts the two-stage dynamic Bayesian game model by recording and updating the attack behavior of the eavesdropper.

[0032] Specifically, the server side expands the model into multiple stages by recording and updating the eavesdropper's attack behavior.

[0033] Step 104: The server calculates the total value of the intelligent voice control system in the wireless channel environment and the signal strength under the interference signal, and determines the eavesdropping and defense costs.

[0034] Specifically, the attacker and defender are modeled based on the wireless channel, and the ability differences of different nodes in the game model are considered to more accurately evaluate the influence and potential threats of the nodes and improve the detection accuracy of eavesdropping behavior.

[0035] Step 106: The server constructs a bipartite graph, calculates the weight matrix of the bipartite graph edges, and uses the Hungarian algorithm to calculate the optimal defense strategy based on the weight matrix.

[0036] Specifically, the server constructs a bipartite graph, calculates the weight matrix of the bipartite graph edges, and uses the Hungarian algorithm to calculate the optimal defense strategy based on the weight matrix, the eavesdropping cost, and the defense cost.

[0037] The Hungarian algorithm is introduced to calculate the weight matrix in the intelligent voice control system scenario, and the optimal defense strategy generation algorithm is designed, so that the system can achieve the best defense effect with low cost and high benefit under the condition of limited resources, thereby improving the server's revenue and resource utilization efficiency.

[0038] The interaction process between the wireless intelligent voice control system, the server and the eavesdropper is as follows: Figure 2 shown. Figure 2 The numbers 1 to 12 marked on the arrow lines indicate the order in which data transmission is performed in the interaction process between the wireless intelligent voice control system, the server and the eavesdropper.

[0039] In the above-mentioned eavesdropping defense method of the wireless intelligent voice control system, the method includes: constructing a two-stage dynamic Bayesian game model between the eavesdropper and the server end that adapts to the wireless channel environment, defining the strategies, benefits and states of the eavesdropper and the server end; the server end dynamically adjusts the two-stage dynamic Bayesian game model by recording and updating the attack behavior of the eavesdropper; the server end calculates the total value of the intelligent voice control system in the wireless channel environment and the signal strength under the interference signal, and determines the eavesdropping and defense costs; the server end constructs a bipartite graph, calculates the weight matrix of the bipartite graph edge, and uses the Hungarian algorithm to calculate the optimal defense strategy based on the weight matrix. Compared with the traditional solution, this method is more flexible and effective in dealing with diversified scenarios, and improves the server's benefits and resource utilization efficiency when resources are limited, effectively enhances the anti-eavesdropping capability in a wireless environment, and improves the success rate of defending against eavesdropping.

[0040] In one embodiment, step 100 includes: initializing the game participants and their parameters; the game participants include: the eavesdropper and server side ; The parameters include: the eavesdropper's strategy set, the server's strategy set, the eavesdropper's cost and benefit parameters, the voice-controlled terminal value, and the server's defense energy consumption; the eavesdropper and the server perform monitoring operations in the T1 phase and calculate their own benefits; the eavesdropper enters the T2 phase to perform signal interference and calculate its own benefits; the server calculates its own benefits in the T2 phase; based on their own benefits in the T1 phase and the T2 phase, the total benefits of the eavesdropper and the server are determined.

[0041] In one embodiment, the total benefits of the eavesdropper and the server are determined as follows based on their own benefits in phases T1 and T2:

[0042] (1)

[0043] (2)

[0044] (3)

[0045] (4)

[0046] (5)

[0047] (6)

[0048] in, is the total revenue of the eavesdropper, is the total revenue of the server, is the stage return weight; is the income of the eavesdropper in stage T1, The cost and benefit parameters for the eavesdropper include the monitoring cost, Punishment for monitoring, For information benefit; The revenue of the server in the T1 phase. Defense energy consumption for the server side; is the income of the eavesdropper in stage T2; The value of voice-controlled terminals; This is the revenue of the server in the T2 phase.

[0049] In one embodiment, step 102 includes: the server records the attack behavior of the eavesdropper in each round, and constructs the prior probability of the game state T using the Beta distribution according to the attack behavior; after observing the historical behavior of the eavesdropper, the server calculates the behavior taken by the attacker when the game type is T Probability ; The eavesdropper performs the action Afterwards, the server updates the posterior probability of the current game state T based on the observed behavior.

[0050] In one embodiment, the posterior probability of the current game state T is:

[0051] (7)

[0052] (8)

[0053] in, Current game status The posterior probability of For attacking, ,in For the i An attack behavior, i =1,2,3,… n ; nis the total number of attack behaviors, usually determined by prior knowledge and historical behaviors, for distributed, To adopt Distributed construction of game state The prior probability of To calculate the attacker's behavior when the game type is T The probability of Taking action for attackers The probability of is the normalization factor.

[0054] Specifically, the server side expands the model into multiple stages according to the following steps.

[0055] Step 200: Record the attack behavior. The server records the attack behavior of the eavesdropper in each round. .

[0056] Step 202: Construct a priori probability. The server uses Distributed construction of game state The prior probability .

[0057] Step 204: Update the posterior probability. After observing the eavesdropper's historical behavior, when the server calculates the game type to be T, the attacker takes action Probability The server calculates the normalization factor to ensure that the sum of the posterior probabilities is 1. Finally, update the posterior probabilities: perform actions on the eavesdropper Afterwards, the server uses formula (7) to update the posterior probability of the current game state T according to the observed behavior.

[0058] In one embodiment, step 104 includes: the server side calculates the weight of the voice control system, and calculates the signal strength in the presence of a malicious interference signal; the server side calculates the interference coefficient based on the signal strength of the normal signal, the antenna gain, and the distance between the signal transmitter and the intelligent voice control system and the signal strength of the malicious interference signal, the antenna gain, and the distance between the interference source and the intelligent voice control system; and the server side calculates the weight of the voice control system based on the interference coefficient. The eavesdropper attacks The cost of each intelligent voice control terminal; according to the unit energy consumption and monitoring time of each measure to monitor malicious signals, calculate the cost of the first Defender monitors The cost of an intelligent voice control system; the measures for monitoring malicious signals include signal strength detection, carrier sensing, packet loss rate and spectrum analysis; Defender monitors The cost of an intelligent voice control system is:

[0059] (9)

[0060] in, They are the unit energy consumption of signal strength detection, carrier sensing, packet loss rate and spectrum analysis respectively; , They are monitoring time and analysis time respectively.

[0061] In one embodiment, the server side calculates the weight of the voice control system, including: according to the weight coefficient and usage load of the voice control system, and the correlation and weight coefficient of the intelligent voice control system with the key equipment in the voice control environment, calculating the total value of the intelligent voice control system as:

[0062] (10)

[0063] in, is the total value of the intelligent voice control system, is the weight coefficient of the voice control system, is the load, The correlation between the intelligent voice control system and key devices in the voice control environment. It is the weight coefficient of the intelligent voice control system and key equipment in the voice control environment.

[0064] In one embodiment, The eavesdropper attacks The cost of an intelligent voice-controlled terminal is:

[0065] (11)

[0066] in, For the The eavesdropper attacks The cost of a smart voice-controlled terminal is is the interference coefficient, To implement the disruption time, To interfere with the implementation of consumption, To remove the unit energy consumption of the system operation except for the attack and defense behaviors, is the system running time, is the interference signal strength, are orthogonal carrier channel conditions respectively.

[0067] Specifically, the server calculates the cost and benefit in the wireless channel environment according to the following steps:

[0068] Step 301: Calculate the voice control system weight. and equipment load , and the correlation between the intelligent voice control system and key equipment in the intelligent system and weight coefficient , and the total value of the intelligent voice control system is calculated using formula (10).

[0069] Step 302: Calculate the cost. In the case of a malicious interference signal, the signal strength is calculated as:

[0070] (12)

[0071] in, is the signal strength in the presence of malicious interference signals, is the environmental noise signal strength, is the transmission channel gain, which is related to the orthogonal carrier condition of the transmission channel. is the interference signal strength, is the environmental noise signal, To transmit the signal.

[0072] The server side determines the signal strength of the normal signal , Antenna gain And the distance between the signal transmitter and the intelligent voice control system , the signal strength of the malicious interference signal , Antenna gain , the distance between the interference source and the intelligent voice control system , calculate the interference coefficient:

[0073] (13)

[0074] in, is the interference coefficient.

[0075] Formula (11) is used to calculate the The eavesdropper attacks The cost of an intelligent voice control system. The unit energy consumption of four methods including signal strength detection, carrier sensing, packet loss rate and spectrum analysis are calculated based on the monitoring measures for malicious signals. The monitoring time is calculated using formula (9). Defender monitors The cost of an intelligent voice control system.

[0076] In one embodiment, step 106 includes: constructing a bipartite graph of the eavesdropper and the intelligent voice control system and a bipartite graph of the defender and the intelligent voice control system; adding virtual nodes as needed to balance the number of nodes in the bipartite graph; calculating the first The value of an intelligent voice control system; The value of an intelligent voice control system, An eavesdropper attacks The cost value of the intelligent voice control system and the Defender Defender The cost value of the intelligent voice control system is calculated, and the weights of the edges between the eavesdropper, the defender and the intelligent voice control system are calculated to obtain the weight matrix of the bipartite graph edge; among them, the weights of the edges between the eavesdropper, the defender and the intelligent voice control system are:

[0077] (14)

[0078] (15)

[0079] in, is the weight of the edge between the eavesdropper and the intelligent voice control system, is the weight of the edge between the defender and the intelligent voice control system, For the Defender Defender The cost value of an intelligent voice control system, For the An eavesdropper attacks The cost value of an intelligent voice control system, For the The value of an intelligent voice control system, is the eavesdropper signal strength, is the defender signal strength.

[0080] The Hungarian algorithm is used to obtain the maximum matching of the weight matrix, and the best defense strategy is obtained based on the maximum matching.

[0081] Specifically, the server generates a defense strategy according to the following steps:

[0082] Step 401: construct a bipartite graph. Construct a bipartite graph of the eavesdropper and the intelligent voice control system and a bipartite graph of the defender and the intelligent voice control system. Add virtual nodes as needed to balance the number of nodes in the bipartite graph.

[0083] Step 402: Calculate the weight of the edge. First, calculate The value of an intelligent voice control system , and then calculate the An eavesdropper attacks The cost of an intelligent voice control system , No. Defender Defender The cost of an intelligent voice control system , then the weight of the edge between the eavesdropper and the intelligent voice control system is shown in formula (14), and the weight of the edge between the defender and the intelligent voice control system is shown in formula (15).

[0084] Step 403: Generate a defense strategy. Use the Hungarian algorithm to solve the maximum match. Use the Hungarian algorithm to obtain the maximum match of the weight matrix, and obtain the best defense strategy based on the maximum match.

[0085] In one embodiment, the process of the eavesdropping defense method of the wireless intelligent voice control system is as follows: Figure 3 The steps of the method include: initialization modeling, dynamic expansion, wireless channel modeling (including the intelligent voice control system value calculation stage, the eavesdropper and defender cost calculation stage), and defense strategy generation (including the bipartite graph generation stage, the weight matrix calculation stage, and the Hungarian algorithm optimization stage).

[0086] During eavesdropping, the game model can dynamically adapt to the game results, that is, the server administrator changes the defense strategy in real time according to the eavesdropper's eavesdropping behavior;

[0087] In the eavesdropping defense scheme, the game model considers the impact of the wireless environment, that is, the defense strategy is adjusted by introducing wireless channel parameters;

[0088] In the eavesdropping defense scheme, the defense strategy should fully consider the limited resources and optimize resource allocation to achieve the maximum benefit.

[0089]

[0090] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0091] In one embodiment, Figure 3 As shown, an eavesdropping defense device for a wireless intelligent voice control system is provided, comprising: a two-stage dynamic Bayesian game model building module, a dynamic adjustment module, a wireless channel modeling module and a defense strategy generation module, wherein:

[0092] Two-stage dynamic Bayesian game model building module, used to build a two-stage dynamic Bayesian game model between the eavesdropper and the server that adapts to the wireless channel environment, and defines the strategies, benefits and states of the eavesdropper and the server;

[0093] A dynamic adjustment module is used to dynamically adjust the two-stage dynamic Bayesian game model by recording and updating the attack behavior of the eavesdropper on the server side;

[0094] The wireless channel modeling module is used to calculate the total value of the intelligent voice control system in the wireless channel environment and the signal strength under the interference signal through the server side to determine the eavesdropping and defense costs;

[0095] The defense strategy generation module is used to construct a bipartite graph through the server, calculate the weight matrix of the bipartite graph edges, and calculate the optimal defense strategy using the Hungarian algorithm based on the weight matrix.

[0096] In one embodiment, the two-stage dynamic Bayesian game model building module is also used to initialize the game participants and their parameters; the game participants include: eavesdroppers and server side ; The parameters include: the eavesdropper's strategy set, the server's strategy set, the eavesdropper's cost and benefit parameters, the voice-controlled terminal value, and the server's defense energy consumption; the eavesdropper and the server perform monitoring operations in the T1 phase to calculate their own benefits; the eavesdropper enters the T2 phase to perform signal interference and calculate its own benefits; the server calculates its own benefits in the T2 phase; based on their own benefits in the T1 phase and the T2 phase, the total benefits of the eavesdropper and the server are determined.

[0097] In one embodiment, the two-stage dynamic Bayesian game model building module is also used to determine the total benefits of the eavesdropper and the server according to their own benefits in stages T1 and T2, as shown in formulas (1) to (6).

[0098] In one embodiment, the dynamic adjustment module is also used for the server to record the attack behavior of the eavesdropper in each round, and to construct the prior probability of the game state T using the Beta distribution according to the attack behavior; after observing the historical behavior of the eavesdropper, the server calculates the behavior taken by the attacker when the game type is T. Probability ; The eavesdropper performs the action Afterwards, the server updates the posterior probability of the current game state T based on the observed behavior.

[0099] In one embodiment, the posterior probability of the current game state T in the dynamic adjustment module is shown in formula (7) to formula (8).

[0100] In one of the embodiments, the wireless channel modeling module is also used for the server to calculate the weight of the voice control system and calculate the signal strength in the presence of a malicious interference signal; the server calculates the interference coefficient based on the signal strength of the normal signal, the antenna gain, and the distance between the signal transmitter and the intelligent voice control system and the signal strength of the malicious interference signal, the antenna gain, and the distance between the interference source and the intelligent voice control system; the server calculates the first The eavesdropper attacks The cost of each intelligent voice control terminal; according to the unit energy consumption and monitoring time of each measure to monitor malicious signals, calculate the cost of the first Defender monitors The cost of an intelligent voice control system; the measures for monitoring malicious signals include signal strength detection, carrier sensing, packet loss rate and spectrum analysis; Defender monitors The cost of an intelligent voice control system is shown in formula (9).

[0101] In one of the embodiments, the server side in the wireless channel modeling module calculates the weight of the voice control system, including: using formula (10) to calculate the total value of the intelligent voice control system according to the weight coefficient and usage load of the voice control system, and the correlation and weight coefficient of the intelligent voice control system with key equipment in the voice control environment.

[0102] In one embodiment, the wireless channel modeling module The eavesdropper attacks The cost of an intelligent voice-controlled terminal is shown in formula (11).

[0103] In one embodiment, the defense strategy generation module is further used to construct a bipartite graph between the eavesdropper and the intelligent voice control system and a bipartite graph between the defender and the intelligent voice control system; add virtual nodes as needed to balance the number of nodes in the bipartite graph; calculate the The value of an intelligent voice control system; The value of an intelligent voice control system, An eavesdropper attacks The cost value of the intelligent voice control system and the Defender Defender The cost value of the intelligent voice control system is calculated, and the weights of the edges between the eavesdropper, defender and intelligent voice control system are calculated to obtain the weight matrix of the bipartite graph edge; among which, the weights of the edges between the eavesdropper, defender and intelligent voice control system are shown in formula (14) and formula (15) respectively. The Hungarian algorithm is used to obtain the maximum matching of the weight matrix, and the optimal defense strategy is obtained based on the maximum matching.

[0104] For the specific definition of the eavesdropping defense device of the wireless intelligent voice control system, please refer to the definition of the eavesdropping defense method of the wireless intelligent voice control system mentioned above, which will not be repeated here. Each module in the eavesdropping defense device of the above-mentioned wireless intelligent voice control system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for preventing eavesdropping of a wireless intelligent voice control system, characterized in that: The method comprises: A two-stage dynamic Bayesian game model between the eavesdropper and the server that adapts to the wireless channel environment is constructed, and the strategies, benefits and states of the eavesdropper and the server are defined; The server dynamically adjusts the two-stage dynamic Bayesian game model by recording and updating the attack behavior of the eavesdropper; The server side calculates the total value of the intelligent voice control system in the wireless channel environment and the signal strength under the interference signal, and determines the eavesdropping and defense costs; The server constructs a bipartite graph and calculates a weight matrix of the edges of the bipartite graph; the bipartite graph is a bipartite graph of the eavesdropper and the intelligent voice control system and a bipartite graph of the defender and the intelligent voice control system; the weight matrix of each edge in the bipartite graph is calculated based on the defense cost value of the defender to the intelligent voice control system and the attack cost value of the eavesdropper to the intelligent voice control system; The Hungarian algorithm is used to calculate according to the weight matrix to generate an optimal defense strategy; the optimal defense strategy is used to maximize the benefits by optimizing resource allocation when the preset resources are limited; The server dynamically adjusts the two-stage dynamic Bayesian game model by recording and updating the attack behavior of the eavesdropper, including: The server records the attack behavior of the eavesdropper in each round, and constructs the prior probability of the game state T using Beta distribution according to the attack behavior; After observing the eavesdropper's historical behavior, the server calculates the game type as T when the attacker takes action Probability ; Execute behavior on eavesdropper Afterwards, the server updates the posterior probability of the current game state T based on the observed behavior.

2. The eavesdropping defense method of the wireless intelligent voice control system according to claim 1, characterized in that: A two-stage dynamic Bayesian game model between the eavesdropper and the server that adapts to the wireless channel environment is constructed, and the strategies, benefits and states of the eavesdropper and the server are defined, including: Initialize the game participants and their parameters; the game participants include: eavesdropper and server side The parameters include: the eavesdropper's strategy set, the server's strategy set, the eavesdropper's cost and benefit parameters, the voice-controlled terminal value, and the server's defense energy consumption; The eavesdropper and the server perform monitoring operations in phase T1 and calculate their own benefits; The eavesdropper enters the T2 stage to perform signal interference and calculate its own benefits; The server calculates its own benefits in the T2 stage; The total benefits of the eavesdropper and the server are determined based on their own benefits in phases T1 and T2.

3. The eavesdropping defense method of the wireless intelligent voice control system according to claim 2, characterized in that: According to the benefits of the eavesdropper and the server in phases T1 and T2, the total benefits of the eavesdropper and the server are determined as: in, is the total revenue of the eavesdropper, is the total revenue of the server, is the income weight of stage T1; is the income of the eavesdropper in stage T1; The revenue of the server in the T1 stage; is the income of the eavesdropper in stage T2; This is the revenue of the server in the T2 phase.

4. The eavesdropping defense method of the wireless intelligent voice control system according to claim 1, characterized in that: The posterior probability of the current game state T is: in, Current game status The posterior probability of is the attack behavior set, ,in For the i An attack behavior, i =1,2,3,… n , n is the total number of attack behaviors, for distributed, To adopt Distributed construction of game state The prior probability of To calculate the attacker's behavior when the game type is T The probability of Taking action for attackers probability.

5. The eavesdropping defense method of the wireless intelligent voice control system according to claim 1, characterized in that: The server side calculates the total value of the intelligent voice control system in the wireless channel environment and the signal strength under the interference signal, and determines the eavesdropping and defense costs, including: The server calculates the weight of the voice control system and the signal strength in the presence of malicious interference signals; The server calculates the interference coefficient based on the signal strength, antenna gain, and distance between the signal transmitter and the intelligent voice control system of the normal signal and the signal strength, antenna gain, and distance between the interference source and the intelligent voice control system of the malicious interference signal; According to the interference coefficient, the The eavesdropper attacks The cost of a smart voice-controlled terminal; According to the unit energy consumption and monitoring time of each measure for monitoring malicious signals, the Defender monitors The cost of an intelligent voice control system; the measures for monitoring malicious signals include signal strength detection, carrier sensing, packet loss rate and spectrum analysis; Defender monitors The cost of an intelligent voice control system is: in, They are the unit energy consumption of signal strength detection, carrier sensing, packet loss rate and spectrum analysis, , They are monitoring time and analysis time respectively.

6. The eavesdropping defense method of the wireless intelligent voice control system according to claim 5, characterized in that: The server calculates the weight of the voice control system, including: According to the weight coefficient and usage load of the voice control system, as well as the correlation and weight coefficient between the intelligent voice control system and key equipment in the voice control environment, the total value of the intelligent voice control system is calculated as: in, is the total value of the intelligent voice control system, is the weight coefficient of the voice control system, is the load, The correlation between the intelligent voice control system and key devices in the voice control environment. It is the weight coefficient of the intelligent voice control system and key equipment in the voice control environment.

7. The eavesdropping defense method of the wireless intelligent voice control system according to claim 5, characterized in that: No. The eavesdropper attacks The cost of an intelligent voice-controlled terminal is: in, For the The eavesdropper attacks The cost of a smart voice-controlled terminal is is the interference coefficient, To implement the disruption time, To interfere with the implementation of consumption, To remove the unit energy consumption of the system operation except for the attack and defense behaviors, is the system running time, is the interference signal strength, is the orthogonal carrier channel condition.

8. The eavesdropping defense method of the wireless intelligent voice control system according to claim 1, characterized in that: The server constructs a bipartite graph, calculates a weight matrix of the bipartite graph edges, and calculates an optimal defense strategy using the Hungarian algorithm according to the weight matrix, including: Construct a bipartite graph between the eavesdropper and the intelligent voice control system, and a bipartite graph between the defender and the intelligent voice control system; Add virtual nodes as needed to balance the number of nodes in the bipartite graph; Calculate the The value of an intelligent voice control system; According to The value of an intelligent voice control system, An eavesdropper attacks The cost value of the intelligent voice control system and the Defender Defense The cost value of the intelligent voice control system is calculated, and the weights of the edges between the eavesdropper, the defender and the intelligent voice control system are calculated to obtain the weight matrix of the bipartite graph edge; among them, the weights of the edges between the eavesdropper, the defender and the intelligent voice control system are: in, is the weight of the edge between the eavesdropper and the intelligent voice control system, is the weight of the edge between the defender and the intelligent voice control system, For the Defender Defense The cost value of an intelligent voice control system, For the An eavesdropper attacks The cost value of an intelligent voice control system, For the The value of an intelligent voice control system, is the eavesdropper signal strength, is the defender signal strength; The Hungarian algorithm is used to obtain the maximum matching of the weight matrix, and the best defense strategy is obtained based on the maximum matching.

9. An eavesdropping protection device for a wireless intelligent voice control system, characterized in that: The device comprises: A two-stage dynamic Bayesian game model construction module is used to construct a two-stage dynamic Bayesian game model between the eavesdropper and the server end that adapts to the wireless channel environment, and defines the strategies, benefits and states of the eavesdropper and the server end; A dynamic adjustment module, used for dynamically adjusting the two-stage dynamic Bayesian game model by recording and updating the attack behavior of the eavesdropper through the server side; A wireless channel modeling module, used to calculate the total value of the intelligent voice control system in the wireless channel environment and the signal strength under the interference signal through the server side, and determine the eavesdropping and defense costs; A defense strategy generation module is used to construct a bipartite graph through the server side and calculate the weight matrix of the bipartite graph edge; the Hungarian algorithm is used to calculate according to the weight matrix to generate the best defense strategy; the bipartite graph is a bipartite graph of the eavesdropper and the intelligent voice control system and a bipartite graph of the defender and the intelligent voice control system; the weight matrix of each edge in the bipartite graph is calculated according to the defense cost value of the defender to the intelligent voice control system and the attack cost value of the eavesdropper to the intelligent voice control system; the best defense strategy is used to achieve maximum benefit by optimizing resource allocation under the condition of limited preset resources; The dynamic adjustment module is used for the server to record the attack behavior of the eavesdropper in each round, and construct the prior probability of the game state T using the Beta distribution according to the attack behavior; after observing the historical behavior of the eavesdropper, the server calculates the behavior taken by the attacker when the game type is T. Probability ; The eavesdropper performs the action Afterwards, the server updates the posterior probability of the current game state T based on the observed behavior.

Images