A wireless sensor network security analysis method

Abstract

This invention relates to the technical field of security analysis and discloses a method for security analysis of wireless sensor networks. The method includes: dividing sensor nodes into central nodes and leaf nodes; constructing a security evaluation model for the leaf nodes of the wireless sensor network and evaluating the security of the leaf nodes; constructing a security evaluation model for the central nodes of the wireless sensor network and evaluating the security of the central nodes; determining adaptive trust thresholds for the leaf nodes and central nodes respectively; and filtering out sensor nodes whose security evaluation results are lower than the trust thresholds from the wireless sensor network. This invention evaluates the security of sensor nodes based on their communication frequency, communication behavior, and the reliability of the sensed data, determines adaptive trust thresholds for the leaf nodes and central nodes respectively, and filters out sensor nodes whose security evaluation results are lower than the trust thresholds from the wireless sensor network, thereby achieving security analysis of wireless sensor networks.

Description

Technical Field

[0001] This invention relates to the technical field of security analysis, and more particularly to a method for network security analysis of wireless sensor networks. Background Technology

[0002] Wireless sensor networks (WSNs) are flexible in deployment, have low-cost sensor nodes, come in a wide variety of types, and are easy to replace and deploy. Therefore, they are often deployed in complex and harsh environments for data acquisition and have a wide range of applications across various fields, attracting considerable attention and research. However, limitations in the infrastructure, communication methods, and deployment environment of WSNs prevent the direct application of mature security detection technologies used in wired networks. Researching and analyzing the security issues present in WSNs, including attack detection models for unique specific attacks and general attack detection models for diverse attack forms, is of great significance for advancing the development of WSNs. To address this issue, this invention proposes a network security analysis method for wireless sensor networks. Summary of the Invention

[0003] In view of this, the present invention provides a wireless sensor network security analysis method, the purpose of which is to: 1) divide sensor nodes into central nodes and leaf nodes according to the remaining energy of the sensor nodes and the number of neighboring nodes, wherein the central node is responsible for integrating the information of the leaf nodes and communicating with the data control center, and select sensor nodes with higher remaining energy and more neighboring nodes as central nodes, and establish security assessment systems for central nodes and leaf nodes respectively, so as to realize the security assessment of sensor nodes in the wireless sensor network; 2) construct a security assessment model for leaf nodes and central nodes of the wireless sensor network, assess the security of sensor nodes according to the communication frequency, communication behavior and reliability of the sensed data, and determine the adaptive trust thresholds for leaf nodes and central nodes respectively, and filter out sensor nodes whose security assessment results are lower than the trust thresholds from the wireless sensor network, so as to realize the security analysis and malicious node management of the wireless sensor network.

[0004] To achieve the above objectives, the present invention provides a wireless sensor network security analysis method, comprising the following steps: S1: Cluster the sensor nodes in the wireless sensor network, dividing the sensor nodes into central nodes and leaf nodes. S2: Construct a security evaluation model for leaf nodes of a wireless sensor network and evaluate the security of the leaf nodes. The security evaluation model for leaf nodes of a wireless sensor network consists of a multi-dimensional trust sub-model, including an interactive trust sub-model, a reliable trust sub-model, and a data trust sub-model. S3: Construct a security assessment model for the central node of a wireless sensor network and assess the security of the central node. The security assessment model for the central node of the wireless sensor network consists of a multi-dimensional trust sub-model, including a communication trust sub-model, a behavior trust sub-model, and a content trust sub-model. S4: Determine the adaptive trust thresholds for leaf nodes and center nodes based on the leaf node security assessment model and the center node security assessment model, respectively. S5: Based on the adaptive trust thresholds of leaf nodes and center nodes, sensor nodes whose security assessment results are lower than the trust thresholds are filtered out from the wireless sensor network.

[0005] As a further improvement of the present invention: Optionally, step S1, which divides the sensor nodes in the wireless sensor network into central nodes and leaf nodes, includes: Sensor nodes in a wireless sensor network are clustered, dividing them into central nodes and leaf nodes. Each sensor node is responsible for acquiring and transmitting sensing data within its sensing range. Each cluster contains several leaf nodes and one central node, forming a cluster. Leaf nodes can communicate with the central node within the cluster, sending their sensed data to it. The central node in each cluster can communicate with the central nodes of other clusters. The central node is responsible for sending all sensing data within its cluster to the data control center. The transmission path nodes for data transmission from the central node to the data control center are all central nodes in the wireless sensor network. The sensor node partitioning process is as follows: S11: Obtain the remaining energy information and location information of the sensor nodes in the wireless sensor network, wherein the information set in the wireless sensor network is as follows: in: Represents sensor nodes Information on remaining energy, Represents sensor nodes Location information, This represents the number of the nth sensor node in the wireless sensor network, where N represents the total number of sensor nodes in the wireless sensor network. S12: Calculate the Euclidean distance between the position information of any two sensor nodes; S13: Set distance threshold For any sensor node in a wireless sensor network , will with The Euclidean distance between them is less than The sensor node is marked as a sensor node. The neighboring nodes are used to obtain the sensor nodes. Number of neighboring nodes ; S14: Calculate the center parameter of the currently unclustered sensor node; the formula for calculating the center parameter is: in: Represents sensor nodes The center parameter; S15: Select the unclustered sensor node with the largest current central parameter as the central node of the k-th group clustering result. Central node The set of neighboring nodes is the set of leaf nodes in the clustering result of the kth group, thus completing the clustering process of the clustering result of the kth group, where the initial value of k is 1; Let k = k + 1, return to step S14, and continue until the K-group clustering results are obtained; S16: Assign the currently unclustered sensor nodes to the nearest clustering result and update the leaf node set of the clustering result. Then, the leaf node set of the k-th clustering result is: in: Indicates the central node The i-th neighboring node, i.e. the i-th leaf node in the k-th group cluster result; The distance from the sensor node to the clustering result is the Euclidean distance from the sensor to the central node in the clustering result.

[0006] Optionally, the step S2, which involves constructing a security assessment model for the leaf nodes of a wireless sensor network, includes: A security assessment model for leaf nodes in a wireless sensor network is constructed. This model consists of a multi-dimensional trust sub-model, including an interaction trust sub-model, a reliable trust sub-model, and a data trust sub-model. The interaction trust sub-model calculates the interaction trust level based on the number of communications between the leaf node and the central node. The reliable trust sub-model calculates the reliable trust level based on the proportion of successful communications between the leaf node and the central node. The data trust sub-model calculates the perceived data difference between the leaf node and its neighboring nodes based on the average of the data transmitted by the leaf node and the data received by the central node, and calculates the data trust level based on this perceived data difference.

[0007] Optionally, step S2 involves using a wireless sensor network leaf node security assessment model to assess the security of leaf nodes, including: The security of leaf nodes is assessed using a security evaluation model for leaf nodes in wireless sensor networks. The security assessment process is as follows: S21: Obtaining the leaf node in the interactive trust sub-model and the central node of the corresponding cluster Within the time range Number of communications within And calculate the leaf nodes. Interactive trust : ; in: This indicates that all leaf nodes in the result of the k-th group cluster are the same as the center node. Within the time range The average number of communications; All leaf nodes in the k-th group cluster result are the same as the central node. Within the time range Maximum number of communications; Represents an exponential function with the natural constant as its base; express The number of digits; S22: Reliable Trust Submodel Obtains Leaf Node The central node of the corresponding cluster Within the time range Number of successful internal communications Number of unsuccessful communications , And calculate the leaf nodes. Reliability and trust : ; In this embodiment of the invention, when the central node receives the data transmitted by the leaf node, it will send an acknowledgment message to the leaf node. If the leaf node does not receive the acknowledgment message from the central node within a specified time, it will retransmit the data and add the message of the previous communication failure. S23: Data Trust Submodel Obtains Leaf Node To the central node The sensed data information sent and the central node The average value of the received sensing data is used to calculate the leaf node. Data trust : ; in: Represents leaf nodes To the central node The sensed data information sent at time t, Indicates the central node The mean value of the received sensing data at time t. Represents leaf nodes To the central node The initial sensing time of the transmitted sensing data information. Represents leaf nodes To the central node The final sensing moment of the transmitted sensing data information; S24: Calculate the leaf nodes Security assessment results: ; in: Represents leaf nodes The safety assessment results; Optionally, the security assessment model for the central node of the wireless sensor network constructed in step S3 includes: A security assessment model for the central node of a wireless sensor network is constructed. This model consists of a multi-dimensional trust sub-model, including a communication trust sub-model, a behavior trust sub-model, and a content trust sub-model. The communication trust sub-model calculates the communication trust level based on the number of communications between the central node and the data control center. The behavior trust sub-model calculates the behavior trust level based on the number of successful and unsuccessful forwardings by the central node. The content trust sub-model calculates the content trust level of the central node based on the data trust level of all leaf nodes in the cluster to which the central node belongs. The higher the data trust level of the leaf nodes, the higher the consistency of the perceived data information within the cluster, and the higher the credibility of the perceived data information transmitted by the central node.

[0008] Optionally, step S3 involves using a wireless sensor network central node security assessment model to assess the security of the central node, including: The security of the central node is assessed using a security evaluation model for wireless sensor networks, including the assessment of the central node's security. The security assessment process is as follows: S31: Communication Trust Sub-model Obtains Central Node With the data control center within the time frame Number of communications within And calculate the center node Communication trust : ; in: This indicates that the central node in the K-group clustering results is within the same time range as the data control center. The average number of communications; This indicates that all central nodes in the K-group cluster results are within the same time range as the data control center. Maximum number of communications; express The number of digits; S32: Obtaining the Central Node in the Behavioral Trust Sub-model Within the time range Number of successful forwards Number of unsuccessful forwards And calculate the center node Behavioral trust : ; When the central node Upon receiving data to be forwarded from other central nodes, the central node... Monitor the behavior of the central node. If the forwarded data is forwarded to the next node, the forwarding is marked as successful. If the central node... If the forwarded data is not forwarded to the next node, the forwarding is marked as unsuccessful and the forwarding behavior is abnormal. S33: Content Trust Submodel Obtains Central Node The data trust level of all leaf nodes within the cluster is calculated, and the central node is obtained. Content trust : ; S34: Calculate the center node Security assessment results: ; in: Indicates the central node The safety assessment results; Optionally, the adaptive trust thresholds for the leaf nodes and the center node are determined in step S4, including: Determine the adaptive trust thresholds for the leaf nodes and the center node respectively, where the formula for determining the adaptive trust threshold is: ; in: This represents the adaptive threshold of the central node. This represents the standard deviation of the security assessment results for the K central nodes; This represents the adaptive threshold for the leaf nodes in the k-th clustering result. This represents the standard deviation of the safety assessment results for NK leaf nodes.

[0009] Optionally, step S5, which filters out sensor nodes whose security assessment results are below the trust threshold from the wireless sensor network, includes: Based on the adaptive trust thresholds of the central node and leaf nodes, the security assessment results of the central node and leaf nodes are compared with their corresponding adaptive trust thresholds. Sensor nodes with security assessment results lower than the trust thresholds are removed from the wireless sensor network. In this embodiment of the invention, for sensor nodes deleted from the wireless sensor network, they can be manually added back to the wireless sensor network. If the deleted node is a central node, the leaf node with the highest remaining energy in the cluster to which the central node belongs is selected as the updated central node.

[0010] To address the above problems, the present invention provides an electronic device, the electronic device comprising: Memory, storing at least one instruction; Communication interfaces enable communication between electronic devices; and The processor executes the instructions stored in the memory to implement the wireless sensor network security analysis method described above.

[0011] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the wireless sensor network security analysis method described above.

[0012] Compared with existing technologies, this invention proposes a wireless sensor network security analysis method, which has the following advantages: First, this scheme proposes a wireless sensor network construction and clustering method. Sensor nodes in the wireless sensor network are clustered, divided into central nodes and leaf nodes. Each sensor node is responsible for acquiring and transmitting sensing data within its sensing range. Each clustering result contains several leaf nodes and one central node, forming a cluster. Leaf nodes can communicate with the central node in the clustering result, sending their sensed data to the central node. The central node in each cluster can communicate with the central nodes of other clusters. The central node is responsible for sending all sensing data within its cluster to the data control center. The transmission path nodes for data transmission from the central node to the data control center are all central nodes in the wireless sensor network. The sensor node partitioning process involves acquiring the remaining energy and location information of the sensor nodes in the wireless sensor network. The information set in the wireless sensor network is as follows: ; in: Represents sensor nodes Information on remaining energy, Represents sensor nodes Location information, The nth sensor node is represented by its index, and N represents the total number of sensor nodes in the wireless sensor network. The Euclidean distance between any two sensor nodes is calculated, and a distance threshold is set. For any sensor node in a wireless sensor network , will with The Euclidean distance between them is less than The sensor node is marked as a sensor node. The neighboring nodes are used to obtain the sensor nodes. Number of neighboring nodes ; Calculate the center parameters of the currently unclustered sensor nodes; The formula for calculating the center parameters is: in: Represents sensor nodes The central parameter is selected; the unclustered sensor node with the largest current central parameter is selected as the central node of the k-th group cluster result. Central node The set of neighboring nodes is the set of leaf nodes in the clustering result of the kth group, thus completing the clustering process of the clustering result of the kth group, where the initial value of k is 1; Let k = k + 1, return to step S14, until the Kth group of clustering results are obtained; assign the currently unclustered sensor nodes to the nearest clustering results, and update the leaf node set of the clustering results. Then the leaf node set of the kth group of clustering results is: ; in: Indicates the central node The i-th neighboring node is the i-th leaf node in the k-th group clustering result. Based on the remaining energy of the sensor node and the number of neighboring nodes, the sensor nodes are divided into central nodes and leaf nodes. The central node is responsible for integrating leaf node information and communicating with the data control center. Sensor nodes with higher remaining energy and more neighboring nodes are selected as central nodes. Security assessment systems are established for both central nodes and leaf nodes to achieve security assessment of sensor nodes in the wireless sensor network.

[0013] Simultaneously, this solution proposes a wireless sensor network security evaluation system, constructing a security evaluation model for leaf nodes in a wireless sensor network. This model comprises multi-dimensional trust sub-models, including an interaction trust sub-model, a reliable trust sub-model, and a data trust sub-model. The interaction trust sub-model calculates the interaction trust level based on the number of communications between the leaf node and the central node. The reliable trust sub-model calculates the reliable trust level based on the proportion of successful communications between the leaf node and the central node. The data trust sub-model calculates the perceived data difference between the leaf node and its neighboring nodes based on the average of the data transmitted by the leaf node and the data received by the central node, and calculates the data trust level based on this perceived data difference. A security evaluation model for the central node of the wireless sensor network is also constructed. The central node security assessment model consists of multi-dimensional trust sub-models, including a communication trust sub-model, a behavior trust sub-model, and a content trust sub-model. The communication trust sub-model calculates the communication trust level based on the number of communications between the central node and the data control center. The behavior trust sub-model calculates the behavior trust level based on the number of successful and unsuccessful forwards by the central node's forwarding behavior. The content trust sub-model calculates the content trust level of the central node based on the data trust level of all leaf nodes in the cluster where the central node resides. A higher data trust level for a leaf node indicates higher consistency of perceived data information within the cluster, and higher reliability of the perceived data information transmitted by the central node. Adaptive trust thresholds are determined for both leaf nodes and the central node, using the following formula: ; in: This represents the adaptive threshold of the central node. This represents the standard deviation of the security assessment results for the K central nodes; This represents the adaptive threshold for the leaf nodes in the k-th clustering result. This represents the standard deviation of the security assessment results for NK leaf nodes. Based on the adaptive trust thresholds for the central node and leaf nodes, the security assessment results for both are compared with their corresponding adaptive trust thresholds. Sensor nodes with security assessment results below the trust thresholds are removed from the wireless sensor network. This scheme constructs security assessment models for leaf nodes and the central node of a wireless sensor network. It assesses the security of sensor nodes based on their communication frequency, communication behavior, and the reliability of the sensed data. Adaptive trust thresholds are determined for both leaf nodes and the central node. Sensor nodes with security assessment results below the trust thresholds are removed from the wireless sensor network, enabling security analysis and malicious node management in the wireless sensor network. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a wireless sensor network security analysis method according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of an electronic device that implements a wireless sensor network security analysis method according to an embodiment of the present invention.

[0015] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0017] This application provides a wireless sensor network security analysis method. The executing entity of the wireless sensor network security analysis method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the wireless sensor network security analysis method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0018] Example 1: S1: Cluster the sensor nodes in the wireless sensor network, dividing them into central nodes and leaf nodes.

[0019] Step S1 involves dividing the sensor nodes in the wireless sensor network into central nodes and leaf nodes, including: Sensor nodes in a wireless sensor network are clustered, dividing them into central nodes and leaf nodes. Each sensor node is responsible for acquiring and transmitting sensing data within its sensing range. Each cluster contains several leaf nodes and one central node, forming a cluster. Leaf nodes can communicate with the central node within the cluster, sending their sensed data to it. The central node in each cluster can communicate with the central nodes of other clusters. The central node is responsible for sending all sensing data within its cluster to the data control center. The transmission path nodes for data transmission from the central node to the data control center are all central nodes in the wireless sensor network. The sensor node partitioning process is as follows: S11: Obtain the remaining energy information and location information of the sensor nodes in the wireless sensor network, wherein the information set in the wireless sensor network is as follows: ; in: Represents sensor nodes Information on remaining energy, Represents sensor nodes Location information, This represents the number of the nth sensor node in the wireless sensor network, where N represents the total number of sensor nodes in the wireless sensor network. S12: Calculate the Euclidean distance between the position information of any two sensor nodes; S13: Set distance threshold For any sensor node in a wireless sensor network , will with The Euclidean distance between them is less than The sensor node is marked as a sensor node. The neighboring nodes are used to obtain the sensor nodes. Number of neighboring nodes ; S14: Calculate the center parameter of the currently unclustered sensor node; the formula for calculating the center parameter is: in: Represents sensor nodes The center parameter; S15: Select the unclustered sensor node with the largest current central parameter as the central node of the k-th group clustering result. Central node The set of neighboring nodes is the set of leaf nodes in the clustering result of the kth group, thus completing the clustering process of the clustering result of the kth group, where the initial value of k is 1; Let k = k + 1, return to step S14, and continue until the K-group clustering results are obtained; S16: Assign the currently unclustered sensor nodes to the nearest clustering result and update the leaf node set of the clustering result. Then, the leaf node set of the k-th clustering result is: ; in: Indicates the central node The i-th neighboring node, i.e. the i-th leaf node in the k-th group cluster result; The distance from the sensor node to the clustering result is the Euclidean distance from the sensor to the central node in the clustering result.

[0020] S2: Construct a security assessment model for leaf nodes in a wireless sensor network and assess the security of the leaf nodes.

[0021] The S2 step involves constructing a security assessment model for the leaf nodes of a wireless sensor network, including: A security assessment model for leaf nodes in a wireless sensor network is constructed. This model consists of a multi-dimensional trust sub-model, including an interaction trust sub-model, a reliable trust sub-model, and a data trust sub-model. The interaction trust sub-model calculates the interaction trust level based on the number of communications between the leaf node and the central node. The reliable trust sub-model calculates the reliable trust level based on the proportion of successful communications between the leaf node and the central node. The data trust sub-model calculates the perceived data difference between the leaf node and its neighboring nodes based on the average of the data transmitted by the leaf node and the data received by the central node, and calculates the data trust level based on this perceived data difference.

[0022] Step S2 involves using a wireless sensor network leaf node security assessment model to evaluate the security of leaf nodes, including: The security of leaf nodes is assessed using a security evaluation model for leaf nodes in wireless sensor networks. The security assessment process is as follows: S21: Obtaining the leaf node in the interactive trust sub-model and the central node of the corresponding cluster Within the time range Number of communications within And calculate the leaf nodes. Interactive trust : ; in: This indicates that all leaf nodes in the result of the k-th group cluster are the same as the center node. Within the time range The average number of communications; All leaf nodes in the k-th group cluster result are the same as the central node. Within the time range Maximum number of communications; Represents an exponential function with the natural constant as its base; ,in express The number of digits; S22: Reliable Trust Submodel Obtains Leaf Node The central node of the corresponding cluster Within the time range Number of successful internal communications Number of unsuccessful communications , And calculate the leaf nodes. Reliability and trust : ; In this embodiment of the invention, when the central node receives the data transmitted by the leaf node, it will send an acknowledgment message to the leaf node. If the leaf node does not receive the acknowledgment message from the central node within a specified time, it will retransmit the data and add the message of the previous communication failure. S23: Data Trust Submodel Obtains Leaf Node To the central node The sensed data information sent and the central node The average value of the received sensing data is used to calculate the leaf node. Data trust : ; in: Represents leaf nodes To the central node The sensed data information sent at time t, Indicates the central node The mean value of the received sensing data at time t. Represents leaf nodes To the central node The initial sensing time of the transmitted sensing data information. Represents leaf nodes To the central node The final sensing moment of the transmitted sensing data information; S24: Calculate the leaf nodes Security assessment results: ; in: Represents leaf nodes The results of the safety assessment.

[0023] S3: Construct a security assessment model for the central node of a wireless sensor network and conduct a security assessment of the central node.

[0024] The S3 step involves constructing a security assessment model for the central node of the wireless sensor network, including: A security assessment model for the central node of a wireless sensor network is constructed. This model consists of a multi-dimensional trust sub-model, including a communication trust sub-model, a behavior trust sub-model, and a content trust sub-model. The communication trust sub-model calculates the communication trust level based on the number of communications between the central node and the data control center. The behavior trust sub-model calculates the behavior trust level based on the number of successful and unsuccessful forwardings by the central node. The content trust sub-model calculates the content trust level of the central node based on the data trust level of all leaf nodes in the cluster to which the central node belongs. The higher the data trust level of the leaf nodes, the higher the consistency of the perceived data information within the cluster, and the higher the credibility of the perceived data information transmitted by the central node.

[0025] Step S3 involves using a wireless sensor network central node security assessment model to evaluate the security of the central node, including: The security of the central node is assessed using a security evaluation model for wireless sensor networks, including the assessment of the central node's security. The security assessment process is as follows: S31: Communication Trust Sub-model Obtains Central Node With the data control center within the time frame Number of communications within And calculate the center node Communication trust : ; in: This indicates that the central node in the K-group clustering results is within the same time range as the data control center. The average number of communications; This indicates that all central nodes in the K-group cluster results are within the same time range as the data control center. Maximum number of communications; express The number of digits; S32: Obtaining the Central Node in the Behavioral Trust Sub-model Within the time range Number of successful forwards Number of unsuccessful forwards And calculate the center node Behavioral trust : ; When the central node Upon receiving data to be forwarded from other central nodes, the central node... Monitor the behavior of the central node. If the forwarded data is forwarded to the next node, the forwarding is marked as successful. If the central node... If the forwarded data is not forwarded to the next node, the forwarding is marked as unsuccessful and the forwarding behavior is abnormal. S33: Content Trust Submodel Obtains Central Node The data trust level of all leaf nodes within the cluster is calculated, and the central node is obtained. Content trust : ; S34: Calculate the center node Security assessment results: ; in: Indicates the central node The results of the safety assessment.

[0026] S4: Determine the adaptive trust thresholds for leaf nodes and central nodes based on the leaf node security assessment model and the central node security assessment model, respectively.

[0027] The S4 step, which determines the adaptive trust thresholds for the leaf nodes and the center node, includes: Determine the adaptive trust thresholds for the leaf nodes and the center node respectively, where the formula for determining the adaptive trust threshold is: ; in: This represents the adaptive threshold of the central node. This represents the standard deviation of the security assessment results for the K central nodes; This represents the adaptive threshold for the leaf nodes in the k-th clustering result. This represents the standard deviation of the safety assessment results for NK leaf nodes.

[0028] S5: Based on the adaptive trust thresholds of leaf nodes and center nodes, sensor nodes whose security assessment results are lower than the trust thresholds are filtered out from the wireless sensor network.

[0029] Step S5 involves filtering out sensor nodes whose security assessment results are below the trust threshold from the wireless sensor network, including: Based on the adaptive trust thresholds of the central node and leaf nodes, the security assessment results of the central node and leaf nodes are compared with their corresponding adaptive trust thresholds, and sensor nodes with security assessment results lower than the trust thresholds are removed from the wireless sensor network.

[0030] Example 2: like Figure 2 The diagram shown is a structural schematic of an electronic device that implements a wireless sensor network security analysis method according to an embodiment of the present invention.

[0031] The electronic device 1 may include a processor 10, a memory 11, a communication interface 13 and a bus, and may also include a computer program, such as program 12, stored in the memory 11 and executable on the processor 10.

[0032] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of program 12, but also to temporarily store data that has been output or will be output.

[0033] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device via various interfaces and lines. It executes programs or modules stored in the memory 11 (such as program 12 for implementing wireless sensor network security analysis) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0034] The communication interface 13 may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices, and to realize communication between internal components of the electronic device.

[0035] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0036] Figure 2 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 2 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0037] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0038] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0039] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0040] The program 12 stored in the memory 11 of the electronic device 1 is a combination of multiple instructions, which, when run in the processor 10, can achieve the following: The sensor nodes in the wireless sensor network are clustered, and the sensor nodes are divided into central nodes and leaf nodes. Construct a security assessment model for leaf nodes in a wireless sensor network and conduct security assessments on the leaf nodes; Construct a security assessment model for the central node of a wireless sensor network and conduct a security assessment of the central node; The adaptive trust thresholds for leaf nodes and center nodes are determined based on the leaf node security assessment model and the center node security assessment model, respectively. Based on the adaptive trust thresholds of leaf nodes and center nodes, sensor nodes whose security assessment results are lower than the trust thresholds are filtered out from the wireless sensor network.

[0041] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 2 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0042] It should be noted that the sequence numbers of the above embodiments of the present invention are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0043] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0044] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A wireless sensor network security analysis method, characterized by, The method includes: S1: Cluster the sensor nodes in the wireless sensor network, dividing the sensor nodes into central nodes and leaf nodes. S2: Construct a security assessment model for leaf nodes in a wireless sensor network and assess the security of leaf nodes; The security assessment model for leaf nodes in the wireless sensor network consists of a multi-dimensional trust sub-model, including an interactive trust sub-model, a reliable trust sub-model, and a data trust sub-model. The interactive trust sub-model calculates the interactive trust level based on the number of communications between the leaf node and the central node. The reliable trust sub-model calculates the reliable trust level based on the proportion of successful communications between the leaf node and the central node. The data trust sub-model calculates the perceived data difference between the leaf node and its neighboring nodes based on the average of the data transmitted by the leaf node and the data received by the central node, and calculates the data trust level based on the perceived data difference. The security of leaf nodes is assessed using a security evaluation model for leaf nodes in wireless sensor networks. The security assessment process is as follows: S21: Obtaining the leaf node in the interactive trust sub-model and the central node of the corresponding cluster Within the time range Number of communications within And calculate the leaf nodes. Interactive trust : in: represents all leaf nodes in the kth cluster result that are co-centric with the node average number of communications in a time range average number of communications in a time range All leaf nodes in the kth group clustering result are the same as the central node The maximum number of communications in the time range The maximum number of communications denotes an exponential function with base the natural constant; denotes the number of bits; S22: Reliable Trust Submodel Obtains Leaf Node The central node of the corresponding cluster Within the time range Number of successful internal communications Number of unsuccessful communications , And calculate the leaf nodes. Reliability and trust : ； S23: Data Trust Submodel Obtains Leaf Node To the central node The sensed data information sent and the central node The average value of the received sensing data is used to calculate the leaf node. Data trust : ； in: Represents leaf nodes To the central node The sensed data information sent at time t, Indicates the central node The mean value of the received sensing data at time t. Represents leaf nodes To the central node The initial sensing time of the transmitted sensing data information. Represents leaf nodes To the central node The final sensing moment of the transmitted sensing data information; S24: Calculate the security evaluation result of the leaf node S24: Calculate the security evaluation result of the leaf node ； in: a security assessment result of a leaf node representing a leaf node A security assessment model for the central node of a wireless sensor network is constructed. This model consists of a multi-dimensional trust sub-model, including a communication trust sub-model, a behavior trust sub-model, and a content trust sub-model. The communication trust sub-model calculates the communication trust level based on the number of communications between the central node and the data control center. The behavior trust sub-model calculates the behavior trust level based on the number of successful and unsuccessful forwardings by the central node. The content trust sub-model calculates the content trust level of the central node based on the data trust level of all leaf nodes in the cluster to which the central node belongs. The higher the data trust level of the leaf nodes, the higher the consistency of the perceived data information within the cluster, and the higher the credibility of the perceived data information transmitted by the central node. S3: Construct a security assessment model for the central node of a wireless sensor network and conduct a security assessment of the central node; The security of the central node is assessed using a security evaluation model for wireless sensor networks, including the assessment of the central node's security. The security assessment process is as follows: S31: Communication Trust Sub-model Obtains Central Node With the data control center within the time frame Number of communications within And calculate the center node Communication trust : ； in: representing the average number of communications of the central node in the K-component cluster result with the data control center in the time range of the central node in the K-component cluster result with the data control center in the time range represents the maximum number of communications between all the central nodes in the K-component cluster result and the data control center in the time horizon of the time horizon; denotes the number of bits; S32: Obtaining the Central Node in the Behavioral Trust Sub-model Within the time range Number of successful forwards Number of unsuccessful forwards And calculate the center node Behavioral trust : ； When the central node Upon receiving data to be forwarded from other central nodes, the central node... Monitor the behavior of the central node. If the forwarded data is forwarded to the next node, the forwarding is marked as successful. If the central node... If the forwarded data is not forwarded to the next node, the forwarding is marked as unsuccessful and the forwarding behavior is abnormal. S33: Content Trust Submodel Obtains Central Node The data trust level of all leaf nodes within the cluster is calculated, and the central node is obtained. Content trust : ； S34: Calculate the center node Security assessment results: ； in: a security assessment result of the central node ; S4: Determine the adaptive trust thresholds for leaf nodes and center nodes based on the leaf node security assessment model and the center node security assessment model, respectively. S5: Based on the adaptive trust thresholds of leaf nodes and center nodes, sensor nodes whose security assessment results are lower than the trust thresholds are filtered out from the wireless sensor network.

2. The method of claim 1, wherein the method further comprises: Step S1 involves dividing the sensor nodes in the wireless sensor network into central nodes and leaf nodes, including: Sensor nodes in a wireless sensor network are clustered, dividing them into central nodes and leaf nodes. Each sensor node is responsible for acquiring and transmitting sensing data within its sensing range. Each cluster contains several leaf nodes and one central node, forming a cluster. Leaf nodes can communicate with the central node within the cluster, sending their sensed data to it. The central node in each cluster can communicate with the central nodes of other clusters. The central node is responsible for sending all sensing data within its cluster to the data control center. The transmission path nodes for data transmission from the central node to the data control center are all central nodes in the wireless sensor network. The sensor node partitioning process is as follows: S11: Obtain the remaining energy information and location information of the sensor nodes in the wireless sensor network, wherein the information set in the wireless sensor network is as follows: ； in: Represents sensor nodes Information on remaining energy, Represents sensor nodes Location information, This represents the number of the nth sensor node in the wireless sensor network, where N represents the total number of sensor nodes in the wireless sensor network. S12: Calculate the Euclidean distance between the position information of any two sensor nodes; S13: Set distance threshold For any sensor node in a wireless sensor network , will with The Euclidean distance between them is less than The sensor node is marked as a sensor node. The neighboring nodes are used to obtain the sensor nodes. Number of neighboring nodes ; S14: Calculate the center parameter of the currently unclustered sensor node; the formula for calculating the center parameter is: in: representing a center parameter of the sensor node representing a center parameter of the sensor node S15: Select the unclustered sensor node with the largest current central parameter as the central node of the k-th group clustering result. Central node The set of neighboring nodes is the set of leaf nodes in the clustering result of the kth group, thus completing the clustering process of the clustering result of the kth group, where the initial value of k is 1; Let k = k + 1, return to step S14, and continue until the K-group clustering results are obtained; S16: Assign the currently unclustered sensor nodes to the nearest clustering result and update the leaf node set of the clustering result. Then, the leaf node set of the k-th clustering result is: ； in: Indicates the central node The i-th neighboring node, i.e. the i-th leaf node in the k-th group cluster result; The distance from the sensor node to the clustering result is the Euclidean distance from the sensor to the central node in the clustering result.

3. The method of claim 1, wherein the method further comprises: The S4 step, which determines the adaptive trust thresholds for the leaf nodes and the center node, includes: Determine the adaptive trust thresholds for the leaf nodes and the center node respectively, where the formula for determining the adaptive trust threshold is: ； in: an adaptive threshold value representing the central node, a standard deviation representing the K central node security assessment results; denotes the adaptive threshold value of the leaf node in the kth clustering result, denotes the standard deviation of the N-K leaf node security assessment results.

4. The method of claim 3, wherein the method further comprises: Step S5 involves filtering out sensor nodes whose security assessment results are below the trust threshold from the wireless sensor network, including: Based on the adaptive trust thresholds of the central node and leaf nodes, the security assessment results of the central node and leaf nodes are compared with their corresponding adaptive trust thresholds, and sensor nodes with security assessment results lower than the trust thresholds are removed from the wireless sensor network.

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