Network security attack path prediction system based on graph neural network

Abstract

The invention discloses a network security attack path prediction system based on a graph neural network, and relates to the technical field of network security, and the system comprises a data collection module which collects network full-link time sequence security data, and outputs standardized time sequence data through time sequence alignment and abnormal noise reduction processing; the time sequence diagram construction module is used for constructing a dynamic attack graph containing nodes and time sequence edges; the feature learning module introduces a time sequence attention mechanism, calculates a time-space fusion attention coefficient based on a graph attention network framework, and outputs a node embedding vector; the reasoning and pruning module is used for generating attack paths based on node embedding vectors and outputting a high-value attack path set; and the analysis decision module is used for carrying out importance sorting on all nodes on the high-value attack path, determining a path core risk point and generating a key node decision basis of the attack path. According to the method, the problem that the traditional technology cannot accurately capture the attack behavior time sequence dependence is solved, and the high-precision prediction of the attack path is realized.

Description

Technical Field

[0001] This invention relates to the field of network security technology, and more specifically, to a network security attack path prediction system based on graph neural networks. Background Technology

[0002] With the continuous iteration of cyberattack methods, complex attack behaviors such as APT (Advanced Persistent Threat) attacks and lateral movement attacks are becoming increasingly frequent. These attacks are characterized by strong concealment, obvious temporal patterns, and variable attack paths, posing a significant challenge to cybersecurity defense. Attack path prediction, as a core technology of proactive defense, aims to identify potential intrusion paths by attackers in advance, providing a basis for defense strategy formulation. Existing attack path prediction technologies are mainly divided into two categories: one is rule engine-based methods, which generate paths by matching attack behaviors with preset attack rules. However, this type of method relies on manually defined rules, making it difficult to adapt to new attack techniques and unable to capture the temporal dependencies of attack behaviors. The other is graph neural network (GNN)-based methods, which model network assets, vulnerabilities, and attack actions as graph structures and achieve path reasoning by learning node features through GNNs. However, existing GNN-based methods still have the following specific problems:

[0003] The lack of temporal context leads to insufficient prediction accuracy: Existing GNN models mostly use static graph modeling, ignoring the temporal characteristics of attack behavior (such as the difference in the probability of lateral movement by an attacker in different time windows, and the decay of the impact of early asset interactions on the current attack path over time). For example, if an attacker exploits vulnerability A to compromise asset X one hour ago, compared to the same action one week ago, the former has a significantly greater impact on subsequent attack paths (such as lateral movement from asset X to asset Y). However, existing static graph models cannot distinguish this temporal difference, resulting in low path prediction accuracy.

[0004] Low-value path interference leads to low prediction efficiency: Existing methods mostly adopt a pattern of generating all paths and then filtering them, without layering constraints based on the hierarchical characteristics of attack paths (such as the entry layer, lateral movement layer, and core asset layer). This results in the generation of a large number of low-value paths (such as paths that cannot reach core assets or paths that exploit low-risk vulnerabilities), increasing model inference overhead and easily interfering with security personnel's identification of high-risk paths. For example, in an enterprise intranet containing 100 assets, existing methods may generate thousands of attack paths, most of which only involve peripheral assets and have no actual attack value, leading to low efficiency in the response of security personnel.

[0005] Poor interpretability hinders accurate defense: Existing attack path prediction models based on GNNs are mostly "black box" models, which can only output predicted attack paths without specifying the decision-making basis for path generation (e.g., whether the core reason a path is judged as high-risk is a high-risk vulnerability or two asset interaction behaviors). This makes it difficult for security personnel to accurately identify defense priorities and fails to meet the compliance audit requirements for the traceability of defense decisions.

[0006] To address the above problems, this invention proposes a solution. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a network security attack path prediction system based on graph neural networks. This system can accurately capture attack timing characteristics, filter low-value paths, and provide clear decision-making basis, thereby solving the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A network security attack path prediction system based on graph neural networks includes a data acquisition module, a time series graph construction module, a feature learning module, an inference and pruning module, and an analysis and decision-making module, with connections between the modules:

[0010] The data acquisition module is used to collect time-series security data across the entire network link, extract attack behavior timestamps and asset interaction time-series features, and output standardized time-series data through time-series alignment and anomaly denoising processing.

[0011] The time series graph construction module constructs a dynamic attack graph containing nodes and time series edges based on standardized time series data. The time series edges carry time decay weights and interaction frequency attributes, and support incremental updates of the graph structure.

[0012] The feature learning module introduces a temporal attention mechanism, which calculates the spatiotemporal fusion attention coefficients based on the graph attention network framework, combining time decay weights and interaction frequency attributes, and outputs node embedding vectors.

[0013] The inference and pruning module generates attack paths based on node embedding vectors, and performs dynamic pruning by combining risk thresholds at each layer, outputting a set of high-value attack paths.

[0014] The analysis and decision-making module ranks all nodes on high-value attack paths by spatiotemporal fusion attention coefficients, identifies the core risk points of the path, and generates decision-making basis for key nodes of the attack path.

[0015] In a preferred embodiment, the data acquisition module is implemented as follows:

[0016] Lightweight data collection agents are deployed at each core node of the network to collect time-series security data across the entire network link in a targeted manner, and core time-series features are extracted from the collected time-series data.

[0017] Convert the timestamps of all collected data to UTC standard time to ensure consistency in the time dimension;

[0018] The 3σ criterion is used in conjunction with business rules to remove and reduce abnormal data, and the processed time series data and features are standardized to obtain standardized time series data.

[0019] In a preferred embodiment, the timing graph construction module is implemented as follows:

[0020] Based on standardized time-series data, the asset nodes, vulnerability nodes, and attack action nodes of the dynamic attack graph are defined, and node initialization is completed.

[0021] Define the core temporal edges and assign time decay weights and interaction frequency attributes to each edge;

[0022] Based on the defined nodes, edges, and edge attributes, an initial dynamic temporal attack graph is constructed, and a hierarchical incremental update mechanism is established to dynamically update the graph structure according to the update frequency of data types.

[0023] In a preferred embodiment, the spatiotemporal fusion attention coefficient acquisition process is as follows:

[0024] Receive the dynamic temporal attack graph output by the temporal graph construction module, and preprocess the node feature matrix and edge attribute matrix in the graph;

[0025] The static features of each node are concatenated with the standardized temporal features to obtain the initial feature vector of each node. The vector dimension is set according to the number of features.

[0026] Based on the basic framework of graph attention networks, spatial attention coefficients between nodes are calculated.

[0027] A temporal attention mechanism is introduced, which combines the time decay weight and interaction frequency in the edge attributes to calculate the temporal attention coefficient;

[0028] The spatial attention coefficient and the temporal attention coefficient are multiplied and fused to obtain the spatiotemporal fused attention coefficient.

[0029] In a preferred embodiment, the process of outputting the node embedding vector that fuses temporal dependencies and spatial associations is as follows:

[0030] Based on the fused attention coefficient, a weighted sum of the neighbor features of all neighbor nodes of the target node is calculated to obtain the aggregated features;

[0031] The aggregated features are nonlinearly transformed using the ReLU activation function to obtain the updated embedding vector of the target node.

[0032] In a preferred embodiment, the process of outputting a set of high-value attack paths is as follows:

[0033] Based on the node embedding vectors output by the feature learning module, potential attack entry nodes are filtered.

[0034] Starting with the set of entry nodes, calculate the lateral movement probability between any two asset nodes based on the node embedding vector;

[0035] Set a lateral movement probability threshold, remove paths with a lateral movement probability less than the lateral movement probability threshold, and generate a set of lateral movement layer paths;

[0036] Starting from the endpoint of the path in the set of lateral movement paths, continue reasoning to the paths of each node in the set of core asset nodes, forming a complete attack path of entry node → lateral movement node → core asset node;

[0037] Set a vulnerability risk threshold, remove all paths containing vulnerability scores lower than the vulnerability risk threshold, and retain the set of core asset layer paths that depend on high-risk vulnerabilities.

[0038] Calculate a risk score for each path in the core asset layer path set to quantify the path risk level;

[0039] Set a risk scoring threshold, and filter paths with a risk score greater than or equal to the risk scoring threshold as high-value attack paths; sort the filtered paths from high to low risk score, and output a set of high-value attack paths.

[0040] In a preferred embodiment, potential attack entry nodes are screened based on the node embedding vectors output by the feature learning module, as follows:

[0041] By mapping node embedding vectors to attack entry probabilities through a fully connected layer, setting an entry probability threshold, and filtering asset nodes with attack entry probabilities greater than or equal to the threshold as a set of potential attack entry nodes.

[0042] In a preferred embodiment, starting with the set of entry nodes, the lateral movement probability between any two asset nodes is calculated based on the node embedding vector, as follows:

[0043] Perform a dot product operation on the final embedding vectors corresponding to the asset node at the starting point of the lateral movement and the final embedding vector corresponding to the asset node at the target of the lateral movement to obtain the original value reflecting the degree of correlation between the two asset nodes.

[0044] The original value is input into the sigmoid activation function, which maps the original value to the [0,1] interval to obtain the probability of lateral movement between two asset nodes.

[0045] In a preferred embodiment, the path risk score calculation process is as follows:

[0046] The first part is to sum the risk contribution values ​​of all edges on the path. For the attack path currently being calculated, first obtain the spatiotemporal fusion attention coefficient corresponding to each edge on the path, then obtain the CVSS score of the vulnerability associated with each edge, multiply the spatiotemporal fusion attention coefficient of each edge by the corresponding CVSS score to obtain the risk contribution value of a single edge, and finally sum the risk contribution values ​​of all edges on the path.

[0047] The second part is the path length penalty item. First, determine the path length, and then multiply the path length by the path length penalty coefficient to obtain the path length penalty value.

[0048] The path risk score is obtained by adding the sum of the edge risk contribution values ​​in the first part to the path length penalty value in the second part.

[0049] In a preferred embodiment, the process of determining the core risk points of the path is as follows:

[0050] Extract the temporal attention coefficient and spatiotemporal fusion attention coefficient corresponding to each edge on the high-value attack path from the feature learning module;

[0051] Construct a temporal attention weight heatmap with time as the horizontal axis and node pairs on the path as the vertical axis;

[0052] The importance of all nodes on high-value attack paths is ranked based on the spatiotemporal fusion attention coefficient.

[0053] The Shapley value algorithm is used to calculate the contribution ratio of each node feature in a high-value attack path to the path risk score, and the vulnerability with the highest contribution is obtained. Combined with the node ranking results, the core risk points of the path are obtained.

[0054] The technical effects and advantages of the network security attack path prediction system based on graph neural networks of the present invention are as follows:

[0055] 1. This invention, through the collaborative design of a temporal graph construction module and a feature learning module, introduces a time decay weight attribute into the dynamic attack graph and integrates a spatiotemporal attention mechanism using a Temporal Graph Attention Network (T-GAT). This effectively solves the problem that traditional attack path prediction techniques cannot accurately capture the temporal dependence of attack behaviors, achieving high-precision attack path prediction. Compared to traditional static graph modeling methods, this invention can quantify the influence of attack interaction behaviors within different time windows, enabling node embedding vectors to simultaneously integrate spatial correlation and temporal dependence features. Ultimately, the attack path prediction accuracy is improved by 10%-15%, especially for complex attack paths with obvious temporal characteristics such as APT attacks and lateral movement.

[0056] 2. This invention employs a hierarchical path reasoning mechanism within its reasoning and pruning modules, applying risk threshold pruning at each level from the "entry layer" to the "lateral movement layer" and then to the "core asset layer." Combined with risk contribution quantification and key node tracing design in the interpretability analysis module, this addresses the low prediction efficiency caused by low-value path interference in traditional full-path generation models and overcomes the limitations of poor interpretability in graph neural network "black box" models. On one hand, hierarchical pruning improves the efficiency of high-value attack path identification by over 85%, significantly reducing the burden on security personnel. On the other hand, through temporal attention weight tracing, core risk point localization, and natural language decision report generation, it provides a clear basis for defense strategy formulation, meets the compliance audit requirements for decision traceability, and supports precise proactive defense. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the network security attack path prediction system based on graph neural networks according to the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1, Figure 1 This invention presents a network security attack path prediction system based on graph neural networks.

[0060] The data acquisition module is used to collect time-series security data across the entire network link, extract attack behavior timestamps and asset interaction time-series features, and output standardized time-series data through time-series alignment and anomaly denoising processing.

[0061] Deploy lightweight data collection agents at each core network node (firewall, IDS / IPS, vulnerability scanner, endpoint EDR, router, etc.) to ensure low resource consumption and avoid impacting the normal operation of network devices; collect time-series security data across the entire network link through these agents, specifically including:

[0062] Asset interaction log: Collect source IP, destination IP, interaction timestamp, interaction type (such as remote login RDP, file sharing SMB, database access, etc.), and interaction status (success / failure);

[0063] Vulnerability-related data: Collect vulnerability number (e.g., CVE number), corresponding associated asset IP, CVSS score, vulnerability discovery time, vulnerability type (e.g., remote code execution, buffer overflow), and vulnerability exploitation difficulty;

[0064] Attack behavior log: Collects attack action type, attack trigger timestamp, attack source address, target assets involved, attack result (attempt / success / failure), and vulnerabilities exploited in the attack (if any);

[0065] Basic asset information: Collect static attribute data for each asset, such as IP address, operating system type and version, open ports, permission level, and the business module to which the asset belongs.

[0066] Core time-series features are extracted from the collected time-series data, including:

[0067] Attack behavior temporal characteristics: timestamp sequence of attack behavior, time interval between two adjacent attacks, and attack frequency against the same asset per unit time (e.g., 1 hour);

[0068] Asset interaction time sequence characteristics: the interaction timestamp sequence of the same asset pair, the interaction frequency per unit time, the interaction duration, and the peak interaction period;

[0069] Vulnerability timing characteristics: the time of vulnerability discovery, the duration of vulnerability existence, and the time sequence of vulnerability attempts.

[0070] Unify the time base for time series data from different sources and in different formats, convert the timestamps of all data to UTC standard time to ensure consistency in the time dimension; remove data with missing timestamps, and supplement data with fuzzy timestamps (such as data only accurate to the hour) with default minute / second time (such as 30 minutes after the hour).

[0071] Anomaly removal and noise reduction are performed using the 3σ criterion combined with business rules:

[0072] Based on the 3σ criterion: calculate the mean μ and standard deviation σ of each time series data (such as the frequency of interaction per unit time, attack interval), and remove outliers that exceed the range of [μ-3σ,μ+3σ] (such as sudden massive invalid logs, obvious false alarm data);

[0073] Based on business rules: remove attack alerts from IPs on the internal network whitelist, logs generated by known legitimate operation and maintenance operations (such as operation and maintenance backup interactions at fixed time periods), and redundant data collected repeatedly (deduplicated through data fingerprinting).

[0074] Standardize and transform the processed time-series data and features:

[0075] Data format standardization: Convert all data to JSON format, define fixed field names, and ensure field consistency;

[0076] Feature standardization: Numerical time-series features (such as interaction frequency, time interval, CVSS score) are transformed to the [0,1] interval using the min-max normalization method to eliminate dimensional differences; categorical features (such as attack type, interaction type) are processed by one-hot encoding.

[0077] The dataset, after time-series alignment, anomaly denoising, and standardization, is packaged and output as input data for the subsequent time-series graph construction module. At the same time, a data quality verification mechanism is established to ensure the integrity (missing rate ≤5%) and time-series consistency of the output data.

[0078] The time-series graph construction module, based on standardized time-series data, constructs a dynamic attack graph containing three types of nodes—assets, vulnerabilities, and attack actions—and multiple types of time-series edges. The time-series edges carry time decay weights and interaction frequency attributes, and support incremental updates of the graph structure.

[0079] Based on standardized time-series data, three types of core nodes in the dynamic attack graph are defined, and node initialization is completed, including:

[0080] Asset Node: Uniquely identified by the asset IP address, node attributes include operating system type and version, open ports, permission level, business module to which it belongs, and standardized static feature vector of the asset;

[0081] Vulnerability Node: Uniquely identified by vulnerability number (e.g., CVE number), node attributes include CVSS score, vulnerability type, exploitation difficulty, discovery time, and standardized vulnerability time-series feature vector;

[0082] Attack Action Node: The node is uniquely identified by the combination of "attack type - target asset". Its attributes include attack action type, attack success rate, attack trigger time sequence, and standardized attack time sequence feature vector.

[0083] Three types of core temporal edges are defined, the relationships between edges are clarified, and each edge is assigned a time decay weight and an interaction frequency attribute:

[0084] Asset-asset edge: Two asset nodes that interact with each other, and the relationship between the edges is "access / interaction relationship";

[0085] Asset-Vulnerability Edge: Associates an asset with a vulnerability with its corresponding vulnerability node. The relationship between the edges is "vulnerability existence relationship".

[0086] Vulnerability-Attack Action Edge: This edge links the attack action to the vulnerability node being exploited, and the relationship between the edge is "vulnerability-triggered attack relationship".

[0087] Interaction frequency attribute: The number of interactions between node pairs associated with the corresponding edge within a unit time window (e.g., the last 24 hours);

[0088] The time decay weight is calculated using the exponential decay formula.

[0089] Based on the defined nodes, edges, and edge attributes, an initial dynamic temporal attack graph is constructed, and a hierarchical incremental update mechanism is established to dynamically update the graph structure according to the update frequency of data types.

[0090] Log data updates (minute-level): New asset interaction logs and attack behavior logs are received every 5-10 minutes. Edges are added or the time decay weight and interaction frequency of the corresponding edges are updated only for the node pairs corresponding to the new logs (such as newly interacted asset pairs, the association between new attack actions and vulnerabilities). There is no need to reconstruct the entire graph.

[0091] Vulnerability data updates (hourly): New vulnerability scan reports are received every 2-4 hours. If a new vulnerability is found, a vulnerability node is added, and an "existence relationship" edge between the new vulnerability and the corresponding asset is established. If the status of an existing vulnerability changes (e.g., it is patched), the corresponding vulnerability node is deleted or the node status is marked.

[0092] Asset data update (daily): The basic asset information is verified and updated once every day at midnight. If there are new assets, the asset node and associated edges are added. If an asset is taken offline, the corresponding node and all related edges are deleted.

[0093] After each update, the graph structure is validated, including: node uniqueness validation (to avoid duplicate nodes), edge association validity validation (such as avoiding non-existent asset-vulnerability associations), and edge attribute integrity validation (to ensure that time decay weights and interaction frequencies are not missing). Isolated nodes (nodes without any associated edges) are temporarily retained and marked. If a node remains an isolated node after three consecutive updates, it is removed to optimize the graph structure size.

[0094] The feature learning module introduces a temporal attention mechanism. Based on the graph attention network framework, it calculates the spatiotemporal fusion attention coefficient by combining the time decay weight and the interaction frequency attribute, and outputs the node embedding vector that fuses temporal dependence and spatial association.

[0095] Receive the dynamic temporal attack graph output by the temporal graph construction module, and preprocess the node feature matrix and edge attribute matrix in the graph:

[0096] Node feature completion: For nodes with a small number of missing features, the feature average of nodes of the same type is used to complete the feature completion.

[0097] Edge attribute normalization: The time decay weight and interaction frequency of the edge are normalized to the [0,1] interval to ensure that they are consistent with the node feature dimensions.

[0098] Graph structure sampling: If the graph is too large (number of nodes > 1000), a neighbor-based sampling strategy is adopted (e.g., sampling 20 neighbors for each node) to generate a subgraph for model training, thus avoiding overloading computational resources.

[0099] The static features of each node (such as asset access level and vulnerability CVSS score) are concatenated with the standardized time-series features (such as interaction frequency per unit time and vulnerability duration) to obtain the initial feature vector of each node. The vector dimension is set according to the number of features.

[0100] Based on the basic framework of Graph Attention Network (GAT), the spatial attention coefficient between nodes is calculated to reflect the spatial association importance of neighboring nodes to the target node;

[0101] A temporal attention mechanism is introduced, which combines the time decay weight and interaction frequency in the edge attributes to calculate the temporal attention coefficient, reflecting the importance of the temporal influence of neighboring nodes on the target node;

[0102] The spatial attention coefficient and the temporal attention coefficient are multiplied and fused to obtain the final spatiotemporal fusion attention coefficient, which simultaneously characterizes the spatial correlation strength and temporal influence between nodes.

[0103] Based on the fused attention coefficients, the features of the target node's neighbor nodes are weighted and aggregated to update the target node's embedding vector:

[0104] Neighbor feature aggregation: Calculate the weighted sum of the features of all neighboring nodes of the target node, with the weights being the fusion attention coefficients;

[0105] Activation Update: The aggregated features are nonlinearly transformed using the ReLU activation function to obtain the updated embedding vector of the target node.

[0106] The inference and pruning module generates attack paths in layers according to the node embedding vector, namely "entry layer - lateral movement layer - core asset layer". It combines the risk threshold of each layer to realize dynamic pruning and output a set of high-value attack paths.

[0107] Based on the node embedding vectors output by the feature learning module, potential attack entry points are filtered out. The process is as follows:

[0108] By mapping node embedding vectors to attack entry probabilities through a fully connected layer, setting an entry probability threshold, and filtering asset nodes whose attack entry probabilities are greater than or equal to the entry probability threshold as a set of potential attack entry nodes;

[0109] Starting with the set of entry nodes, the probability of lateral movement between any two asset nodes is calculated based on the node embedding vectors, as follows:

[0110] First, the two core vectors involved in the calculation are identified: the final embedding vector corresponding to the asset node that serves as the starting point of the lateral movement, and the final embedding vector corresponding to the asset node that serves as the target of the lateral movement. Then, the dot product operation is performed on these two embedding vectors to obtain the original value that reflects the degree of correlation between the two asset nodes. Finally, the original value is input into the sigmoid activation function, which maps the original value to the interval [0,1], and the result is the probability value of the lateral movement between the two asset nodes.

[0111] Set a lateral movement probability threshold, directly eliminate paths with a lateral movement probability less than the threshold, retain high-probability lateral movement paths, and generate a set of lateral movement layer paths.

[0112] For paths in the lateral movement probability threshold, add attack action nodes and vulnerability nodes on the path to clarify the attack actions and vulnerabilities that each lateral movement path depends on.

[0113] Starting from the endpoint of the path in the set of lateral movement paths, continue reasoning to the paths of each node in the set of core asset nodes, forming a complete attack path of "entry node → lateral movement node → core asset node".

[0114] Set a vulnerability risk threshold, remove paths where all vulnerability scores are less than the vulnerability risk threshold, and retain the set of core asset layer paths that depend on high-risk vulnerabilities.

[0115] The risk score is calculated for each path in the core asset layer path set to quantify the path risk level. The process is as follows:

[0116] The calculation logic of the route risk score can be broken down into two parts summed to finally obtain the score result that quantifies the route risk level;

[0117] The first part is to sum the risk contribution values ​​of all edges on the path. For the attack path currently being calculated, first obtain the spatiotemporal fusion attention coefficient corresponding to each edge on the path, then obtain the CVSS score of the vulnerability associated with each edge (if the edge has no associated vulnerability, then take the basic risk score of the corresponding attack action, for example, the basic risk score of remote code execution attack is 9.0), multiply the spatiotemporal fusion attention coefficient of each edge by the corresponding CVSS score (or the basic risk score of the attack action) to obtain the risk contribution value of a single edge, and finally sum the risk contribution values ​​of all edges on the path.

[0118] The second part is the path length penalty. First, the path length (i.e., the total number of nodes contained in the path) is determined. Then, the path length is multiplied by the path length penalty coefficient (fixed value of 0.2) to obtain the path length penalty value. The purpose of this penalty is to suppress the risk score of excessively long paths, because the longer the path, the more attack steps the attacker needs to complete, the higher the attack difficulty, and the lower the corresponding actual risk. The final path risk score is the sum of the edge risk contribution values ​​in the first part and the path length penalty value in the second part.

[0119] Set a risk scoring threshold and select paths with a risk score greater than or equal to the threshold as high-value attack paths. Sort the selected paths from high to low risk scores and output a Top-N (e.g., Top-5 / Top-10) set of high-value attack paths, along with the node sequence, edge sequence, risk score, and contribution percentage of each core indicator for each path.

[0120] The analysis and decision-making module ranks all nodes on high-value attack paths by spatiotemporal fusion attention coefficients, determines the core risk points of the path by combining the contribution ratio of each node's characteristics to the path risk score, and generates the decision basis for key nodes of the attack path.

[0121] Extract the temporal attention coefficient and spatiotemporal fusion attention coefficient corresponding to each edge on the high-value attack path from the feature learning module;

[0122] Using time as the horizontal axis and node pairs along the path as the vertical axis, a temporal attention weight heatmap is constructed to intuitively display the temporal importance of interactions between node pairs within different time windows.

[0123] The importance of all nodes on the high-value attack path is ranked based on the spatiotemporal fusion attention coefficient. The Shapley value algorithm is used to calculate the contribution ratio of each node feature (especially vulnerability-related features) to the path risk score, and the vulnerability with the highest contribution is obtained. The core risk points of the path are obtained by combining the node ranking results.

[0124] If the first node in the sorted list is a vulnerable node, then that vulnerability must be a core risk point in the path.

[0125] If the nodes at the top of the sort are high-privilege assets (such as domain controllers), then the contribution of the permission level of that asset will inevitably be high, which is one of the core risk points.

[0126] If the nodes ranked at the top are attack entry points, then their corresponding temporal interaction contribution is high (recent high-frequency interaction), which is a key entry point for attackers to intrude and falls into the category of core risk points.

[0127] By integrating temporal attention weights, risk contribution, and key node tracing information, the complete decision-making logic for determining a path to be high-value is analyzed, clarifying "which features / nodes / interactions lead to a higher risk for the path"; the path structure, temporal attention heatmap, and contribution percentage pie chart are integrated into a visual interface to help security personnel intuitively understand the decision-making logic.

[0128] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0129] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0130] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0131] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0132] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0133] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A network security attack path prediction system based on graph neural networks, characterized in that, It includes a data acquisition module, a time series graph construction module, a feature learning module, an inference and pruning module, and an analysis and decision-making module, with connections between the modules: The data acquisition module is used to collect time-series security data across the entire network link, extract attack behavior timestamps and asset interaction time-series features, and output standardized time-series data through time-series alignment and anomaly denoising processing. The time series graph construction module constructs a dynamic attack graph containing nodes and time series edges based on standardized time series data. The time series edges carry time decay weights and interaction frequency attributes, and support incremental updates of the graph structure. The feature learning module introduces a temporal attention mechanism, which calculates the spatiotemporal fusion attention coefficients based on the graph attention network framework, combining time decay weights and interaction frequency attributes, and outputs node embedding vectors. The inference and pruning module generates attack paths based on node embedding vectors, and performs dynamic pruning by combining risk thresholds at each layer, outputting a set of high-value attack paths. The analysis and decision-making module ranks all nodes on high-value attack paths by spatiotemporal fusion attention coefficients, identifies the core risk points of the path, and generates decision-making basis for key nodes of the attack path.

2. The network security attack path prediction system based on graph neural networks according to claim 1, characterized in that, The data acquisition module is implemented as follows: Lightweight data collection agents are deployed at each core node of the network to collect time-series security data across the entire network link in a targeted manner, and core time-series features are extracted from the collected time-series data. Convert the timestamps of all collected data to UTC standard time to ensure consistency in the time dimension; The 3σ criterion is used in conjunction with business rules to remove and reduce abnormal data, and the processed time series data and features are standardized to obtain standardized time series data.

3. The network security attack path prediction system based on graph neural networks according to claim 2, characterized in that, The implementation process of the sequence diagram construction module is as follows: Based on standardized time-series data, the asset nodes, vulnerability nodes, and attack action nodes of the dynamic attack graph are defined, and node initialization is completed. Define the core temporal edges and assign time decay weights and interaction frequency attributes to each edge; Based on the defined nodes, edges, and edge attributes, an initial dynamic temporal attack graph is constructed, and a hierarchical incremental update mechanism is established to dynamically update the graph structure according to the update frequency of data types.

4. The network security attack path prediction system based on graph neural networks according to claim 3, characterized in that, The process of obtaining the attention coefficient through spatiotemporal fusion is as follows: Receive the dynamic temporal attack graph output by the temporal graph construction module, and preprocess the node feature matrix and edge attribute matrix in the graph; The static features of each node are concatenated with the standardized temporal features to obtain the initial feature vector of each node. The vector dimension is set according to the number of features. Based on the basic framework of graph attention networks, spatial attention coefficients between nodes are calculated. A temporal attention mechanism is introduced, which combines the time decay weight and interaction frequency in the edge attributes to calculate the temporal attention coefficient; The spatial attention coefficient and the temporal attention coefficient are multiplied and fused to obtain the spatiotemporal fused attention coefficient.

5. The network security attack path prediction system based on graph neural networks according to claim 4, characterized in that, The process of outputting the node embedding vectors that fuse temporal dependencies and spatial associations is as follows: Based on the fused attention coefficient, a weighted sum of the neighbor features of all neighbor nodes of the target node is calculated to obtain the aggregated features; The aggregated features are nonlinearly transformed using the ReLU activation function to obtain the updated embedding vector of the target node.

6. The network security attack path prediction system based on graph neural networks according to claim 5, characterized in that, The process of outputting a set of high-value attack paths is as follows: Based on the node embedding vectors output by the feature learning module, potential attack entry nodes are filtered. Starting with the set of entry nodes, calculate the lateral movement probability between any two asset nodes based on the node embedding vector; Set a lateral movement probability threshold, remove paths with a lateral movement probability less than the lateral movement probability threshold, and generate a set of lateral movement layer paths; Starting from the endpoint of the path in the set of lateral movement paths, continue reasoning to the paths of each node in the set of core asset nodes, forming a complete attack path of entry node → lateral movement node → core asset node; Set a vulnerability risk threshold, remove all paths containing vulnerability scores lower than the vulnerability risk threshold, and retain the set of core asset layer paths that depend on high-risk vulnerabilities. Calculate a risk score for each path in the core asset layer path set to quantify the path risk level; Set a risk scoring threshold and filter paths with a risk score greater than or equal to the risk scoring threshold as high-value attack paths; The filtered paths are sorted from high to low risk scores, and a set of high-value attack paths is output.

7. A network security attack path prediction system based on graph neural networks according to claim 6, characterized in that, Based on the node embedding vectors output by the feature learning module, potential attack entry points are filtered out. The process is as follows: By mapping node embedding vectors to attack entry probabilities through a fully connected layer, setting an entry probability threshold, and filtering asset nodes with attack entry probabilities greater than or equal to the threshold as a set of potential attack entry nodes.

8. A network security attack path prediction system based on graph neural networks according to claim 7, characterized in that, Starting with the set of entry nodes, the probability of lateral movement between any two asset nodes is calculated based on the node embedding vectors, as follows: Perform a dot product operation on the final embedding vectors corresponding to the asset node at the starting point of the lateral movement and the final embedding vector corresponding to the asset node at the target of the lateral movement to obtain the original value reflecting the degree of correlation between the two asset nodes. The original value is input into the sigmoid activation function, which maps the original value to the [0,1] interval to obtain the probability of lateral movement between two asset nodes.

9. A network security attack path prediction system based on graph neural networks according to claim 8, characterized in that, The path risk score calculation process is as follows: The first part is to sum the risk contribution values ​​of all edges on the path. For the attack path currently being calculated, first obtain the spatiotemporal fusion attention coefficient corresponding to each edge on the path, then obtain the CVSS score of the vulnerability associated with each edge, multiply the spatiotemporal fusion attention coefficient of each edge by the corresponding CVSS score to obtain the risk contribution value of a single edge, and finally sum the risk contribution values ​​of all edges on the path. The second part is the path length penalty item. First, determine the path length, and then multiply the path length by the path length penalty coefficient to obtain the path length penalty value. The path risk score is obtained by adding the sum of the edge risk contribution values ​​in the first part to the path length penalty value in the second part.

10. A network security attack path prediction system based on graph neural networks according to claim 9, characterized in that, The process of identifying the core risk points of the path is as follows: Extract the temporal attention coefficient and spatiotemporal fusion attention coefficient corresponding to each edge on the high-value attack path from the feature learning module; Construct a temporal attention weight heatmap with time as the horizontal axis and node pairs on the path as the vertical axis; The importance of all nodes on high-value attack paths is ranked based on the spatiotemporal fusion attention coefficient. The Shapley value algorithm is used to calculate the contribution ratio of each node feature in a high-value attack path to the path risk score, and the vulnerability with the highest contribution is obtained. Combined with the node ranking results, the core risk points of the path are obtained.

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