Network attack early warning method based on abnormal behavior graph

Abstract

The invention relates to the technical field of Internet of Things security, in particular to a network attack early warning method based on an abnormal behavior map. Comprising; constructing a subject-behavior-resource three-layer heterogeneous graph, establishing a low-activity baseline model based on historical data, calculating weak association strength between nodes by adopting wavelet decomposition and a signal enhancement algorithm, and identifying a latent attack chain; the attack chain features are converted into multi-dimensional vectors, and an attack collaboration time window and intensity are predicted through a vector convergence angle and a distance change rate; the vulnerability of the graph topology is quantified, the edge weight is dynamically adjusted by using a gradient descent algorithm, and high-risk nodes are isolated; the convergence parameters are fed back to the baseline model and correlation strength calculation to form closed-loop optimization; and calculating a comprehensive threat index based on three-dimensional weighting of the latent threat intensity, the convergence urgency degree and the topology anti-attack capability, and realizing layered early warning. According to the method, the APT attack detection rate is remarkably improved, and the early warning response time is shortened.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) security technology, specifically to a network attack early warning method based on abnormal behavior graphs. Background Technology

[0002] With the explosive growth of IoT devices, advanced persistent threats (APS) are becoming increasingly rampant in the IoT environment. The heterogeneity of IoT devices, the diversity of communication protocols, and the complexity of their behavioral patterns make accurately identifying latent attack chains disguised as low-activity, normal behavior a core challenge in IoT security defense. Developing early warning technologies based on dynamic behavioral collaborative analysis is of great significance for protecting critical IoT infrastructure such as smart manufacturing and smart cities.

[0003] Current mainstream security solutions for the Internet of Things (IoT) environment have significant technical flaws: Intrusion detection systems based on rule bases and feature matching can only identify known attack patterns, and have a low detection rate for latent attacks that deliberately reduce activity and simulate normal behavior, with a false negative rate as high as 30% to 40% in the IoT environment; Traditional threshold alarm mechanisms rely on fixed parameter settings, ignoring the convergence characteristics of attack vectors in the spatiotemporal dimension, resulting in delayed early warning response, often only being detected after the attack has been completed; In addition, existing technologies generally lack quantitative assessment and dynamic adjustment mechanisms for the vulnerability of IoT topologies, and cannot suppress the spread of attack chains in real time. Summary of the Invention

[0004] This invention provides a network attack early warning method based on abnormal behavior graphs. By analyzing low-activity anomalies through behavior graph analysis, it can achieve collaborative convergence prediction and dynamic blocking of attack chains, which is of great significance for improving the protection capabilities of IoT infrastructure such as smart manufacturing and smart cities.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A network attack early warning method based on abnormal behavior graphs includes: S100: Construct an abnormal behavior graph containing main nodes, behavior nodes, and resource nodes, and establish a low-activity baseline model based on historical network data. Use a weak signal enhancement algorithm to calculate the weak correlation strength between nodes to identify latent attack chains. S200: The correlation strength of the latent attack chain is transformed into a feature vector to construct a multi-dimensional attack vector space. The convergence trend is analyzed by calculating the convergence angle and distance change rate between vectors, and the convergence time window and convergence strength parameters are obtained. S300: Based on the convergence time window and convergence strength parameters, the topological vulnerability of the graph is evaluated, and the gradient descent algorithm is used to dynamically adjust the node connection weights and temporarily isolate high-risk nodes. S400: Based on the evaluation results of the node connection weight adjustment, the convergence angle parameter is fed back to update the activity threshold setting, and the distance change rate parameter is fed back to adjust the association strength calculation weight. S500: A weighted evaluation model is established based on three dimensions: the intensity of latent attack threats, the urgency of convergence, and the topology's anti-attack capability. A comprehensive threat index is calculated, and when the comprehensive threat index exceeds the dynamic judgment threshold based on graph connectivity, hierarchical early warning information is output.

[0006] As a preferred embodiment of the present invention, the construction of the abnormal behavior map includes: Users, IoT devices, and applications in the network are defined as subject nodes; login, access, transmission, and execution of operations are defined as behavior nodes; and servers, databases, and files are defined as resource nodes. Directed edge connections are established between the main node and the behavior node, and between the behavior node and the resource node, forming a three-layer heterogeneous graph structure; Assign behavior frequency, access time, and data volume attributes to each node, and assign association strength and duration weight parameters to each edge.

[0007] As a preferred embodiment of the present invention, the weak correlation strength between computing nodes includes: Collect historical behavior data of each node in the abnormal behavior map within a preset time period, and calculate the average activity, activity variance, and activity distribution range of each type of node; The dynamic baseline parameters are updated using a sliding time window method, and the baseline values ​​are corrected according to changes in behavioral patterns over different time periods. Set a lower boundary threshold for normal activity, mark the behavior of nodes below the threshold as potential latent states, and use wavelet transform to decompose the behavior time series of nodes marked as latent states to extract trend signals and noise signals. High-frequency noise interference is removed by an adaptive threshold denoising method, while retaining weak feature signals of latent attack behavior; Weak feature signals are linearly enhanced using signal amplification techniques, and the correlation strength between nodes in the latent attack chain is output.

[0008] As a preferred embodiment of the present invention, the construction of the multidimensional attack vector space includes: Extract the node association strength, time interval, and behavior type features of each latent attack chain in the abnormal behavior map, and combine them to form a multi-dimensional feature vector; Establish a high-dimensional vector space with attack behavior characteristics as coordinate axes, and map all latent attack chains into vector points in the space; The cosine of the angle between vectors is calculated to quantify the similarity of attack behaviors, and the Euclidean distance is calculated to quantify the proximity of attack vectors.

[0009] As a preferred embodiment of the present invention, the step of obtaining the convergence time window and convergence strength parameters includes: Vector clustering is used to identify attack vector combinations with a co-current trend; Calculate the rate of change of the convergence angle of the attack vector combination over the time series; Calculate the rate of change of distance of the attack vector combination at a spatial location; The convergence time window is determined based on the period during which the rate of change of the convergence angle is continuously lower than the preset angle threshold, and the convergence strength parameter is determined based on the ratio of the maximum to the minimum value of the rate of change of distance.

[0010] As a preferred embodiment of the present invention, the step of evaluating the topological vulnerability of the map includes: Calculate the importance weights of the main node, behavior node, and resource node respectively; The degree-centrality of computing nodes is used to calculate the number of connected nodes, the betweenness-centrality of computing nodes as a bridge in the attack path, and the propagation range of compact centrality computing nodes. Construct a cross-layer vulnerability propagation model to simulate the propagation process of attack behavior from the subject node through the behavior node to the resource node; Generate a vulnerability assessment matrix to quantify the vulnerability of each node and edge in the abnormal behavior graph under attack scenarios.

[0011] As a preferred embodiment of the present invention, the graph adjustment of the gradient descent algorithm includes: An optimization function is established with the goal of maximizing the anti-attack capability of the abnormal behavior graph, and the connection weights of each edge in the graph are set as optimization variables; Calculate the gradient value of the optimization function with respect to each edge weight to determine the direction of weight adjustment, and use the gradient descent method to iteratively update the edge weight values; For the three-layer structure of the abnormal behavior graph, weight adjustment constraints are set for the connection edges between the main layer, behavior layer and resource layer, and an adaptive learning strategy is adopted to control the weight update step size.

[0012] As a preferred embodiment of the present invention, the temporary isolation of high-risk nodes includes: Based on the vulnerability scores and importance rankings of nodes in the abnormal behavior graph, nodes with vulnerability scores exceeding a preset vulnerability threshold and ranking in the top 10% of importance are selected as isolation candidates. Assess the impact of isolating candidate nodes on the connectivity of the anomalous behavior graph, and select the high-risk nodes that have the least impact on the overall connectivity of the graph after isolation; Reduce the access permission level of the main node to read-only, prohibiting it from performing sensitive operations; set an operation blocking flag for the behavior node, suspending the execution permission of this type of behavior; set a temporary access barrier for the resource node, limiting the list of authorized users; The isolation duration for each type of node is determined based on the time window for attack vector convergence prediction.

[0013] As a preferred embodiment of the present invention, the S400 implementation step includes: The difference in attack resistance before and after adjusting the connection weights of nodes in the abnormal behavior graph is used as the adjustment evaluation score. When the graph adjustment score exceeds the preset success threshold, the statistical features of the attack vector convergence angle are extracted as reference data for activity threshold optimization. The distribution characteristics of the attack vector distance change rate are extracted and used as the basis for adjusting the weights in the node association strength calculation. A weighted average method is used to fuse and update the newly extracted parameter features with historical parameters.

[0014] As a preferred embodiment of the present invention, the step of outputting hierarchical early warning information includes: The threat intensity index of latent attacks is calculated based on the number of latent attack chains, the number of nodes involved, and the average correlation strength in the abnormal behavior graph. The convergence urgency index is calculated based on the convergence speed of the attack vector in the multidimensional space, the predicted convergence time, and the convergence tightness. The topology's resistance to attacks is calculated based on the connectivity robustness, structural redundancy, and number of critical paths after topology adjustment of the abnormal behavior graph. Weighting coefficients related to the size of the abnormal behavior map were assigned to the three indicators, and the comprehensive threat index was calculated by weighted summation. Based on the comprehensive threat index range of the abnormal behavior map, four warning levels are set, and corresponding information on the main node type, abnormal behavior node type, and threatened resource node type is output for different warning levels.

[0015] The beneficial effects of this invention are: 1. This invention constructs a three-layer heterogeneous graph of subject, behavior, and resource, combined with wavelet decomposition and signal enhancement algorithms. This effectively amplifies weak attack signals hidden in normal traffic and identifies low-activity latent attack chains that are difficult to detect using traditional detection methods. It is particularly suitable for detecting covert attacks on IoT devices such as smart sensors and edge computing devices in the IoT environment. Furthermore, by transforming attack chain features into multi-dimensional vectors and analyzing their convergence trends, it achieves a shift from passive detection to proactive early warning. It can provide a time window warning before the coordinated implementation of attacks, significantly improving the detection rate and early warning timeliness of advanced threats such as APTs in the IoT environment.

[0016] 2. This invention organically combines attack vector convergence analysis, graph topological vulnerability assessment, gradient descent optimization, and node isolation to construct a closed-loop optimization system of early warning, protection, and feedback. By reducing access permissions for high-risk nodes, setting operation blocking flags, and limiting the authorized user list, dynamic access control for IoT devices is achieved. By feeding convergence parameters back to the baseline model and correlation strength calculation, the system can automatically adjust detection thresholds and weight parameters according to the evolution of the IoT environment and attack patterns, avoiding misjudgments and missed judgments caused by fixed rules, and achieving a dynamic balance between detection accuracy and environmental adaptability. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the structure of a network attack early warning method based on abnormal behavior graphs according to the present invention. Detailed Implementation

[0018] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0019] Example 1: As Figure 1 As shown, the present invention provides a network attack early warning method based on abnormal behavior graphs, comprising: S100: Construct an abnormal behavior graph containing main nodes, behavior nodes, and resource nodes, and establish a low-activity baseline model based on historical network data. Use a weak signal enhancement algorithm to calculate the weak correlation strength between nodes to identify latent attack chains. Furthermore, the construction of the abnormal behavior map includes: Users, IoT devices, and applications in the network are defined as subject nodes; login, access, transmission, and execution of operations are defined as behavior nodes; and servers, databases, and files are defined as resource nodes. Directed edge connections are established between the main node and the behavior node, and between the behavior node and the resource node, forming a three-layer heterogeneous graph structure; Assign behavior frequency, access time, and data volume attributes to each node, and assign association strength and duration weight parameters to each edge.

[0020] Specifically, a three-layer heterogeneous graph structure is first established. User accounts, server devices, terminal devices, and network applications in the IoT network environment are defined as principal nodes, and each principal node is assigned a unique identifier. The set of principal nodes is represented as follows: , where n is the total number of main nodes.

[0021] Login operations, file access operations, data transfer operations, and program execution operations occurring on the network are defined as behavior nodes. The set of behavior nodes is represented as... , where m is the total number of behavior nodes.

[0022] File servers, database servers, shared file directories, and sensitive data files in the network are defined as resource nodes. A set of resource nodes is represented as follows: , where k is the total number of resource nodes.

[0023] Directed edges are established between subject nodes and behavior nodes to represent the relationship of a subject performing a specific behavior; the directed edges point from the subject node to the behavior node. Similarly, directed edges are established between behavior nodes and resource nodes to represent the relationship of a behavior acting on a specific resource; the directed edges point from the behavior node to the resource node. This connection method forms a three-layer heterogeneous graph structure: subject layer – behavior layer – resource layer.

[0024] Assign a behavior frequency attribute to each entity node, recording the number of times the entity performs various behaviors per unit time; assign an access time attribute, recording the distribution of the entity's active time periods; and assign a data volume attribute, recording the amount of data traffic processed by the entity. Assign an execution frequency attribute to each behavior node, recording the frequency statistics of the behavior being executed; assign a duration attribute, recording the average execution time of the behavior; and assign a resource quantity attribute, recording the number of resource nodes involved in the behavior. Assign access frequency, stored data volume, and number of visitors attributes to each resource node.

[0025] A correlation strength parameter, ranging from 0 to 1, is assigned to the directed edges between the subject node and the behavior node, representing the degree to which the subject performs the behavior; a duration weight parameter is also assigned, representing the temporal stability of the correlation. Similarly, correlation strength and duration weight parameters are assigned to the directed edges between the behavior node and the resource node.

[0026] By constructing this abnormal behavior graph structure, different types of entities and their behaviors are uniformly incorporated into the graph framework, enabling the attack path to be fully presented in the structured graph, laying the foundation for the identification of subsequent latent attack chains and the analysis of propagation paths.

[0027] Furthermore, the weak correlation strength between the computing nodes includes: Collect historical behavior data of each node in the abnormal behavior map within a preset time period, and calculate the average activity, activity variance, and activity distribution range of each type of node; The dynamic baseline parameters are updated using a sliding time window method, and the baseline values ​​are corrected according to changes in behavioral patterns over different time periods. Set a lower boundary threshold for normal activity, mark the behavior of nodes below the threshold as potential latent states, and use wavelet transform to decompose the behavior time series of nodes marked as latent states to extract trend signals and noise signals. High-frequency noise interference is removed by an adaptive threshold denoising method, while retaining weak feature signals of latent attack behavior; Weak feature signals are linearly enhanced using signal amplification techniques, and the correlation strength between nodes in the latent attack chain is output.

[0028] Historical IoT network behavior data from the past 30 days is collected as the data source for establishing the baseline. For each subject node, the number of times it performs various behaviors within each hourly time period is counted, and the behavior frequency distribution over 24 hours is calculated. In this embodiment, the observation period is set to 7 days, and the average behavior frequency per hour over 7 days is calculated for each subject node as the activity benchmark value for that time period.

[0029] For the main node During the time period The formula for calculating activity level is: ; in Representing the subject In time period Execution frequency, Representing the subject Number of associated behavior types.

[0030] The mean and standard deviation of activity levels for all subject nodes across different time periods are calculated to establish an activity distribution model. The low-activity threshold is set based on the 3σ principle in statistics. Considering the unique characteristics of abnormal behavior in IoT networks, the threshold is set as the mean minus 1.5 times the standard deviation. This coefficient was determined through data analysis of 150 different IoT network environments and can identify abnormally low-activity behavior with a 95% confidence level. When the activity level of a subject node falls below this threshold, it is marked as a potentially abnormally low-activity state. A sliding time window of 3 days is used to continuously update the baseline parameters. Whenever new day's data is generated, the oldest day's data is removed, and the activity distribution parameters are recalculated to achieve dynamic adjustment of the baseline model.

[0031] Behavioral data of all nodes in the abnormal behavior map over the past 7 days were collected, and the behavioral time series of main nodes below the low activity threshold were marked as latent state sequences. A 5-level wavelet decomposition was performed on the latent state sequences using the db4 wavelet basis function, decomposing the time series into trend components and detail components. The trend component reflects the long-term change pattern of the behavior, while the detail component reflects short-term fluctuations and noise. A soft thresholding denoising method was applied to the detail components obtained from the wavelet decomposition. In this embodiment, the threshold was set to 0.3 times the standard deviation of the detail component. When the absolute value of the detail component was less than the threshold, it was set to zero; when the absolute value was greater than the threshold, it was subtracted from the threshold and retained. After denoising, the trend component and the processed detail components were reconstructed into a denoised behavioral time series. Linear amplification was applied to the denoised weak behavioral signal. The amplification factor was set to 3.0, which, through experiments, has been verified to effectively enhance the detectability of weak signals while avoiding distortion caused by over-amplification. The amplitude of the weak signal was multiplied by the amplification factor to obtain the enhanced signal. The enhanced signal can highlight the previously masked latent attack behavior characteristics. The correlation strength between nodes is calculated based on the enhanced signal. For the master node... and behavior nodes The node association strength is calculated as follows: ; in as the main body In time The enhanced signal value, For behavior In time The indicator function is used. By setting the association strength threshold to 0.15, which is determined based on a large amount of experimental data, the false alarm rate can be controlled within an acceptable range while ensuring the detection rate. Node pairs with association strength exceeding this threshold are selected to form a latent attack chain. Multiple nodes with temporal correlation are connected into a complete attack chain, forming an attack path from the subject node through the behavior node to the resource node.

[0032] By decomposing, denoising, and enhancing the signals of latent nodes, it is possible to effectively amplify the attack signs hidden in low-activity behavior, making the abnormal connections that were originally covered by noise visible, thereby improving the accuracy of identifying latent attack chains.

[0033] S200: The correlation strength of the latent attack chain is transformed into a feature vector to construct a multi-dimensional attack vector space. The convergence trend is analyzed by calculating the convergence angle and distance change rate between vectors, and the convergence time window and convergence strength parameters are obtained. Furthermore, the construction of the multidimensional attack vector space includes: Extract the node association strength, time interval, and behavior type features of each latent attack chain in the abnormal behavior map, and combine them to form a multi-dimensional feature vector; Establish a high-dimensional vector space with attack behavior characteristics as coordinate axes, and map all latent attack chains into vector points in the space; The cosine of the angle between vectors is calculated to quantify the similarity of attack behaviors, and the Euclidean distance is calculated to quantify the proximity of attack vectors.

[0034] Specifically, three types of feature parameters are extracted from the latent attack chain obtained in step S100. For each latent attack chain, the node association strength sequence is extracted, that is, the numerical sequence of the association strength between each pair of adjacent nodes in the chain; the time interval sequence is extracted, that is, the time interval sequence between each behavior node in the chain; and the behavior type feature sequence is extracted, which uses one-hot encoding to represent the combination of behavior types involved in the chain.

[0035] The feature parameters of each latent attack chain are combined to form a multi-dimensional feature vector. The feature vector is composed of: the mean, variance, and maximum value of node association strength as the first three dimensions, used to characterize the intensity distribution characteristics of the attack chain; the mean, variance, and minimum value of the time interval as the fourth to sixth dimensions, used to characterize the temporal characteristics of the attack chain; and the binary encoding of the behavior type features as the subsequent eight dimensions, used to characterize the behavioral pattern characteristics of the attack chain. In this embodiment, each attack chain forms a 14-dimensional feature vector.

[0036] Establish a 14-dimensional attack vector space, mapping the feature vectors corresponding to all latent attack chains to vector points in this space. Calculate the cosine of the angle between the attack vectors: ; in Represents the dot product of vectors. and Let represent the Euclidean norm of the vectors. A cosine value of the included angle close to 1 indicates that the two attack vectors have highly similar behavioral patterns.

[0037] Calculate the Euclidean distance between attack vectors to quantify their proximity. sum vector The formula for calculating the Euclidean distance between them is: ; in and Representing vectors respectively and The k-th component. The smaller the distance value, the closer the two attack vectors are.

[0038] By transforming the characteristics of attack chains, such as correlation strength, time interval, and behavior type, into spatial vectors, the similarity and aggregation trend between different attack chains can be intuitively characterized geometrically. This allows distributed and covert attacks to exhibit convergence characteristics in high-dimensional space, making it easier to predict coordinated behavior.

[0039] Furthermore, the steps for obtaining the convergence time window and convergence strength parameters include: Vector clustering is used to identify attack vector combinations with a co-current trend; Calculate the rate of change of the convergence angle of the attack vector combination over the time series; Calculate the rate of change of distance of the attack vector combination at a spatial location; The convergence time window is determined based on the period during which the rate of change of the convergence angle is continuously lower than the preset angle threshold, and the convergence strength parameter is determined based on the ratio of the maximum to the minimum value of the rate of change of distance.

[0040] Specifically, the K-means clustering algorithm is used to identify combinations of attack vectors exhibiting co-current trends. The number of clusters K is determined using the elbow rule, which calculates the sum of squares within each cluster for different K values ​​and selects the inflection point where the rate of decrease in the sum of squares within each cluster significantly slows down as the optimal K value, typically between 3 and 7. Cluster centers are determined through iterative calculations, and attack vectors are assigned to the nearest cluster centers to form attack vector combinations.

[0041] For each attack vector combination, a time series is constructed by sorting the attack chain occurrence times. The average angle of attack vectors within a cluster is calculated using a 10-minute sampling interval. Where t is the time identifier, It is obtained by taking the mean of the angles between all pairs of vectors within the cluster.

[0042] The formula for calculating the rate of change of the convergence angle is: ; in The time interval is 10 minutes. The average angle at the previous time point. When the rate of change of the angle at three consecutive time points is less than 0.02 radians per minute, the attack vector is considered to have begun to converge. This threshold is determined by statistically analyzing the convergence characteristics of a large number of attack samples, which can effectively distinguish between true convergence and random fluctuations.

[0043] Calculate the rate of change of spatial distance. This involves calculating the average distance from the attack vector within a cluster to the cluster center. Perform time series analysis. It is obtained by calculating the arithmetic mean of the distances from all vectors within a cluster to the cluster center. The formula for calculating the rate of change of distance is: ; in This represents the average distance at the previous time point.

[0044] The convergence time window is determined based on the rate of change of the convergence angle. Timing begins when the rate of change of the angle is less than 0.02 radians per minute and continues until the rate of change of the angle drops below 0.01 radians per minute and remains stable; this time period is defined as the convergence time window. The convergence strength parameters are determined based on the rate of change of distance. The convergence strength parameter is defined as the ratio of the maximum to the minimum average distance during the convergence process, and the calculation formula is: ; in Within the convergence time window The maximum value, Within the convergence time window The minimum value of the convergence strength parameter. In this embodiment, a convergence strength parameter exceeding 2.5 is considered a high-intensity attack convergence. This threshold is determined based on the threat level assessment theory: a convergence strength less than 2.0 indicates a low threat, 2.0-2.5 indicates a medium threat, and greater than 2.5 indicates a high threat.

[0045] By analyzing the rate of change of angle and distance between attack vectors, it is possible not only to identify in advance the time point when multiple latent attack chains are about to converge, but also to quantify the tightness of the convergence. This mechanism effectively improves the foresight of early warning, enabling defensive measures to intervene before the attack is fully formed.

[0046] S300: Based on the convergence time window and convergence strength parameters, the topological vulnerability of the graph is evaluated, and the gradient descent algorithm is used to dynamically adjust the node connection weights and temporarily isolate high-risk nodes. Furthermore, the step of assessing the topological vulnerability of the graph includes: Calculate the importance weights of the main node, behavior node, and resource node respectively; The degree-centrality of computing nodes is used to calculate the number of connected nodes, the betweenness-centrality of computing nodes as a bridge in the attack path, and the propagation range of compact centrality computing nodes. Construct a cross-layer vulnerability propagation model to simulate the propagation process of attack behavior from the subject node through the behavior node to the resource node; Generate a vulnerability assessment matrix to quantify the vulnerability of each node and edge in the abnormal behavior graph under attack scenarios.

[0047] Convergence time window obtained based on step S200 and convergence strength parameters The importance weights of the main nodes, behavior nodes, and resource nodes in the abnormal behavior graph are calculated respectively.

[0048] For the main node, its importance weight is calculated using the following formula: in For node connectivity, The maximum connectivity in the graph. This is the normalized value for the permission level. As a balancing factor, based on experience in the field of IoT network security, it is set to 0.6 and 0.4. This weighting is determined by the analytic hierarchy process.

[0049] For behavior nodes, the formula for calculating their importance weight is: in This is a normalized value for the behavioral sensitivity level. For execution frequency, To achieve the maximum execution frequency, As a balance coefficient, it is set to [value] in this embodiment. The weighting is determined based on a behavioral risk assessment model, where behavioral sensitivity contributes more to risk.

[0050] For resource nodes, the formula for calculating their importance weight is: ; in This is the normalized value for resource sensitivity level. For access frequency, To maximize access frequency, As a balance coefficient, it is set to [value] in this embodiment. The weighting is based on the data security classification and protection standard, with resource sensitivity being the main risk factor.

[0051] The degree centrality algorithm is used to calculate the number of node connections. For a node... Its degree centrality is calculated as follows: ; in For nodes The number of connecting edges, This represents the total number of nodes in the graph. In this embodiment, the degree centrality value ranges from 0 to 1, with a larger value indicating denser node connections. The betweenness centrality algorithm is used to calculate the bridging role of nodes in the attack path. Node The formula for calculating betweenness centrality is: ; in Let be the number of shortest paths from node s to node t. Let be the number of shortest paths passing through node i. Betweenness centrality reflects the criticality of a node in information propagation. The compact centrality algorithm is used to calculate the propagation influence range of a node. The formula for calculating the compact centrality of node i is: in The shortest path length from node i to node j is summed to cover all other nodes in the graph. A higher compact centrality value indicates a wider propagation range. A cross-layer vulnerability propagation model is constructed to simulate the propagation process of an attack from the subject node through the action node to the resource node. The propagation probability is calculated using the following formula: in Let be the propagation probability from subject node s to behavior node b. Let be the propagation probability from behavior node b to resource node r. represents the weight coefficient of behavior node b. A vulnerability assessment matrix is ​​generated based on three centrality metrics and propagation probability. Each element in the matrix represents the numerical vulnerability of the corresponding node under the attack scenario.

[0052] Node propagation impact probability The calculation method is as follows: ; in: , The propagation attenuation coefficient is set to 3.0 based on network topology characteristics; Let j be the influence factor of node j, and the calculation formula is: ; Let be the shortest path length from node i to node j.

[0053] The vulnerability score calculation formula is as follows: ; The weighting coefficients are determined based on network topology security analysis theory: betweenness centrality has the highest weight (0.35) because it directly reflects the criticality of a node in the attack path; degree centrality has a weight of 0.25 to reflect the influence of connection density; tight centrality and propagation influence probability each account for 0.20, balancing the propagation range and the degree of influence.

[0054] Furthermore, the graph adjustment of the gradient descent algorithm includes: An optimization function is established with the goal of maximizing the anti-attack capability of the abnormal behavior graph, and the connection weights of each edge in the graph are set as optimization variables; Calculate the gradient value of the optimization function with respect to each edge weight to determine the direction of weight adjustment, and use the gradient descent method to iteratively update the edge weight values; For the three-layer structure of the abnormal behavior graph, weight adjustment constraints are set for the connection edges between the main layer, behavior layer and resource layer respectively, and an adaptive learning strategy is adopted to control the weight update step size.

[0055] Specifically, an optimization function is established with the objective of maximizing the attack resistance of the anomalous behavior graph. Attack resistance is defined as the graph's ability to maintain connectivity and functionality when attacked, a definition that comprehensively considers two key aspects: the structural stability and service continuity of the IoT network. The optimization function is expressed as: Where W is the connection weight vector of each edge in the graph, and the specific definitions of each term are as follows: Connectivity metrics: ; Robustness indicators: ; Attack success rate metrics: ; in: The size of the largest connected component. The total number of nodes; Average path length The structural integrity score of the graph after removing node k is calculated by the number and size of connected components; The probability of node k being attacked is determined based on the vulnerability score; The probability of success for a specific attack path is calculated by multiplying the vulnerabilities of nodes along that path. Balance coefficient. It is determined based on the principles of security protection priority and IoT network performance balance.

[0056] The gradient direction is determined by calculating the partial derivatives of the optimization function with respect to each edge weight, and the gradient is calculated using numerical differentiation methods. For the edge weights... Its gradient is calculated as follows: ; The edge weights are updated iteratively using the gradient descent method. The weight update formula is: ; in The learning rate is set to an initial value of 0.01 based on optimization theory and experience, and t is the number of iterations.

[0057] Weight adjustment constraints are set for each of the three-layer graph structure. The edge weight constraint from the main layer to the behavior layer is as follows: The edge weight constraint from the behavior layer to the resource layer is: Constraints ensure the rationality and stability of the map structure.

[0058] An adaptive learning rate strategy is used to control the step size of weight updates. The learning rate increases by 10% when the objective function value continuously decreases, and decreases by 50% when the objective function value increases. The learning rate adjustment formula is as follows: Where sign is an indicator of the direction of function value change; sign=1 when the function value decreases and sign=0 when it increases.

[0059] By combining gradient descent weight adjustment and node isolation strategies, the graph structure can be dynamically changed, thereby improving the overall topology's resistance to attacks before the attack chain converges.

[0060] Furthermore, the temporarily isolated high-risk nodes include: Based on the vulnerability scores and importance rankings of nodes in the abnormal behavior graph, nodes with vulnerability scores exceeding a preset vulnerability threshold and ranking in the top 10% of importance are selected as isolation candidates. Assess the impact of isolating candidate nodes on the connectivity of the anomalous behavior graph, and select the high-risk nodes that have the least impact on the overall connectivity of the graph after isolation; Reduce the access permission level of the main node to read-only, prohibiting it from performing sensitive operations; set an operation blocking flag for the behavior node, suspending the execution permission of this type of behavior; set a temporary access barrier for the resource node, limiting the list of authorized users; The isolation duration for each type of node is determined based on the time window for attack vector convergence prediction.

[0061] Specifically, the nodes in the vulnerability assessment matrix are sorted according to their vulnerability scores, and a comprehensive risk score is calculated by combining this score with the node importance. The formula for calculating the comprehensive risk score is as follows:

[0062] in For node importance, The convergence strength parameter is used as an intensity adjustment factor for risk assessment.

[0063] Nodes with vulnerability scores exceeding a preset vulnerability threshold of 0.7 and ranking in the top 10% of importance are selected as isolation candidate nodes. In this embodiment, the preset vulnerability threshold is dynamically adjusted according to the graph size; the threshold is 0.7 when the total number of nodes is less than 100, and 0.8 when the total number of nodes exceeds 100.

[0064] The impact of isolating candidate nodes on graph connectivity is evaluated. The connectivity impact is calculated using a connectivity metric in graph theory, which is the ratio of the size of the largest connected component in the isolated graph to the size of the original connected component. Candidate nodes with the smallest connectivity impact are selected for isolation to ensure that the overall functionality of the graph is not severely impaired.

[0065] Differentiated isolation strategies are adopted for different types of nodes, based on the security characteristics and business impact of each node type. Access restrictions are applied to principal nodes, downgrading their access permissions to read-only and prohibiting sensitive operations. Operation blocking is applied to behavioral nodes, suspending the execution permissions of that type of behavior and preventing related operations from taking place. Access isolation is applied to resource nodes, setting up temporary access barriers and allowing only authorized users to access them.

[0066] The isolation duration is determined based on the convergence time window. The isolation time for the main node is set to 1.5 times the convergence time window, the isolation time for the behavior node is set to 1.2 times the convergence time window, and the isolation time for the resource node is set to 2 times the convergence time window. In this embodiment, when the convergence time window is 30 minutes, the isolation time for the main node is 45 minutes, the isolation time for the behavior node is 36 minutes, and the isolation time for the resource node is 60 minutes.

[0067] By combining vulnerability scoring and importance ranking for screening, and then integrating connectivity impact analysis, we can ensure that isolation operations can block potential attack paths without causing excessive disruption to overall business communications, thus achieving a defense measure that balances security and availability.

[0068] S400: Based on the evaluation results of the node connection weight adjustment, the convergence angle parameter is fed back to update the activity threshold setting, and the distance change rate parameter is fed back to adjust the association strength calculation weight. Furthermore, the S400 implementation step includes: The difference in attack resistance before and after adjusting the connection weights of nodes in the abnormal behavior graph is used as the adjustment evaluation score. When the graph adjustment score exceeds the preset success threshold, the statistical features of the attack vector convergence angle are extracted as reference data for activity threshold optimization. The distribution characteristics of the attack vector distance change rate are extracted and used as the basis for adjusting the weights in the node association strength calculation. A weighted average method is used to fuse and update the newly extracted parameter features with historical parameters.

[0069] Specifically, based on the node connection weight adjustment completed in step S300, the difference in attack resistance capability of the abnormal behavior graph before and after the adjustment is calculated as the adjustment evaluation score. Attack resistance capability is comprehensively measured by three indicators: graph connectivity robustness, critical path redundancy, and node failure tolerance. The formula for calculating the adjustment effect score is as follows: ; in For the adjusted attack resistance capabilities, The attack resistance capability before the adjustment.

[0070] The specific calculation methods for attack resistance are as follows: connectivity robustness is calculated by assessing the probability of maintaining graph connectivity after removing critical nodes, using a progressive removal method; critical path redundancy is determined by calculating the number of alternative paths between important nodes, implemented using a multi-path search algorithm; node failure tolerance is assessed by simulating the degree of graph functionality retention after random node failures, using the Monte Carlo method for statistical analysis. The weights of the three indicators are set to 0.4, 0.3, and 0.3, respectively, based on network reliability theory. A success threshold of 0.15 is set, determined through numerous network topology optimization experiments, effectively distinguishing between significant improvements and minor changes. When the graph adjustment score exceeds this threshold, the topology adjustment is considered to have achieved significant results, and a parameter feedback update mechanism is initiated. The success threshold is dynamically adjusted according to the graph size: 0.10 for fewer than 50 nodes, 0.15 for 50 to 200 nodes, and 0.20 for more than 200 nodes. This tiered setting considers the complexity differences of networks of different sizes.

[0071] Statistical features are extracted from the convergence angle parameters obtained in step S200 as reference data for activity threshold optimization. The statistical features of the convergence angle parameters include three indicators: mean, standard deviation, and minimum. The mean convergence angle reflects the overall convergence degree of the attack vector, the standard deviation reflects convergence stability, and the minimum reflects the tightest convergence state. The statistical features of the convergence angle are then converted into an activity threshold adjustment factor. The formula for calculating the adjustment factor is: ; in The mean of the convergence angle, The reference angle value is set to 0.5 radians in this embodiment. The adjustment factor is used to correct the low activity threshold setting in step S100.

[0072] The updated formula for calculating the activity threshold is: ; in The original activity threshold, This is an adjustment factor calculated based on the convergence angle parameter.

[0073] Distribution characteristics are extracted from the distance change rate parameters obtained in step S200 and used as the basis for adjusting the weights in the node association strength calculation. The distribution characteristics of the distance change rate include three statistics: peak rate, variance, and skewness. The peak rate reflects the maximum convergence speed of the attack vector, the variance reflects the fluctuation of the convergence speed, and the skewness reflects the asymmetry of the convergence speed distribution. The distance change rate distribution characteristics are then converted into association strength weight adjustment coefficients. The formula for calculating the weight adjustment coefficients is as follows: ; in This represents the peak value of the distance change rate. The reference rate of change was set to 0.15 based on network behavior pattern analysis, and experiments verified that this maintained system stability. The weight adjustment coefficient was used to correct the correlation strength calculation weights in the weak signal enhancement algorithm during step S100.

[0074] The updated association strength calculation weights are: ; in These are the weight parameters used in the original association strength calculation.

[0075] A weighted average method is used to fuse and update the newly extracted parameter features with historical parameters. The fusion and update formula is as follows: ; in To ensure the fusion weighting coefficient, it is set to 0.7 in this embodiment. This value maintains system stability while ensuring the parameter update effect. The newly extracted parameter value. These are historical parameter values. The fusion update mechanism ensures the smoothness and stability of parameter adjustments, avoiding the impact of sudden parameter changes on system stability.

[0076] By feeding back the convergence angle and distance change rate into the calculation of the activity threshold and association strength weight, respectively, adaptive optimization of the parameters is achieved. In this way, as the IoT network environment and attack patterns evolve, the system can automatically adjust its detection sensitivity and robustness, avoiding false positives or false negatives caused by fixed thresholds.

[0077] S500: A weighted evaluation model is established based on three dimensions: the intensity of latent attack threats, the urgency of convergence, and the topology's anti-attack capability. A comprehensive threat index is calculated, and when the comprehensive threat index exceeds the dynamic judgment threshold based on graph connectivity, hierarchical early warning information is output.

[0078] The step of outputting hierarchical early warning information includes: The threat intensity index of latent attacks is calculated based on the number of latent attack chains, the number of nodes involved, and the average correlation strength in the abnormal behavior graph. The convergence urgency index is calculated based on the convergence speed of the attack vector in the multidimensional space, the predicted convergence time, and the convergence tightness. The topology's resistance to attacks is calculated based on the connectivity robustness, structural redundancy, and number of critical paths after topology adjustment of the abnormal behavior graph. Weighting coefficients related to the size of the abnormal behavior map were assigned to the three indicators, and the comprehensive threat index was calculated by weighted summation. Based on the comprehensive threat index range of the abnormal behavior map, four warning levels are set, and corresponding information on the main node type, abnormal behavior node type, and threatened resource node type is output for different warning levels.

[0079] Specifically, the latent attack threat intensity index is calculated based on the latent attack chains identified in step S100. The total number of latent attack chains in the anomalous behavior graph is counted, the number of nodes involved in each chain is recorded, and the arithmetic mean of the association strength of all chains is calculated. The formula for calculating the latent attack threat intensity is: ; in The number of stealth attack chains, This represents the average number of nodes involved in the chain. is the mean of the correlation strength, and n is the total number of nodes in the graph.

[0080] The convergence urgency index is calculated based on the attack vector convergence analysis results obtained from the S200 step. Three parameters are extracted: convergence speed, predicted convergence time, and convergence tightness of the attack vector in multidimensional space. The formula for calculating the convergence urgency is as follows: ; in The convergence rate is calculated using the rate of change of the vector distance. The degree of convergence tightness is determined by the ratio of the final cluster radius to the initial dispersion radius; To predict the convergence time.

[0081] The topology attack resistance index is calculated based on the topology adjustment results completed in step S300. The evaluation assesses three aspects of the anomalous behavior graph after topology adjustment: connectivity robustness, structural redundancy, and the number of critical paths. The formula for calculating topology attack resistance is: ; in For connectivity robustness scoring, Score the structural redundancy. The number of critical paths is scored. The weighting coefficients of the three indicators are determined based on the size of the anomalous behavior graph. For small-scale graphs with fewer than 100 nodes, the weighting for latent attack threat intensity is 0.5, convergence urgency is 0.3, and topology resistance to attack is 0.2. For large-scale graphs with more than 100 nodes, the three weights are adjusted to 0.4, 0.4, and 0.2, respectively.

[0082] The comprehensive threat index is calculated by weighted summation. The formula for calculating the comprehensive threat index is: ; in , , These are the corresponding weighting coefficients. In this embodiment, for medium-sized maps, the weights are set to 0.45, 0.35, and 0.20, respectively.

[0083] A dynamic judgment threshold is set based on the connectivity features of the anomalous behavior graph. The connectivity features are comprehensively evaluated using the graph's average path length, clustering coefficient, and number of connected components. The dynamic judgment threshold is calculated using the following formula: ; in The connectivity influencing factor is calculated by combining the average path length, clustering coefficient, and number of connected components of the graph.

[0084] When the comprehensive threat index exceeds the dynamic judgment threshold, a tiered early warning information output mechanism is activated. The warning levels are divided into four levels based on the range of the threat index values: a comprehensive threat index between 0.6 and 0.7 indicates a low-level warning; between 0.7 and 0.8 indicates a medium-level warning; between 0.8 and 0.9 indicates a high-level warning; and above 0.9 indicates an emergency warning.

[0085] The system outputs corresponding node type information based on different warning levels. Low-level warnings output the types of main nodes and nodes exhibiting abnormal behavior, including abnormal login users and suspicious access behaviors. Intermediate-level warnings add information on threatened resource nodes, including sensitive file types and critical server types. High-level and emergency warnings output complete attack chain information, including specific main node identifiers, behavior node sequences, and target resource node locations. A warning information output format is established, including five core elements: warning level, threat index value, statistics of involved nodes, expected impact range, and recommended protective measures. Warning information is pushed to network administrators in real time via the system interface and simultaneously recorded in the security log for subsequent analysis.

[0086] By weighted and fused from three dimensions—threat intensity, convergence urgency, and topology resistance to attack—the calculated comprehensive threat index can fully reflect the severity of the attack. Combined with dynamic threshold settings and multi-level early warning output, it can not only indicate whether a threat exists, but also provide a basis for graded response and guide different levels of defense measures.

[0087] Example 2: A company's IT department adopted this invention's network attack early warning method based on abnormal behavior graphs to protect the security of its internal network file server. The company has 80 office computers, 12 servers, and 200 employees. Experimental environment: CentOS 7.6 system, 16GB memory, developed using Python 3.8, data acquisition cycle of 10 seconds, analysis window of 7 days.

[0088] The system collects one week's worth of operational data and assigns it to users. , ,application Define the main node, define file access, data transfer, and permission query as behavior nodes, and define the personnel database, shared folder, and backup server as resource nodes.

[0089] Parameter settings: observation period T=7 days, sliding window length 3 days, activity threshold coefficient α=1.5, wavelet basis function db4, decomposition level 5, denoising threshold β=0.3, signal amplification factor γ=3.0, correlation strength threshold θ=0.15.

[0090] Construction of heterogeneous graphs reveals: synchronization procedures The program was abnormally active outside of working hours. Test data showed that under normal circumstances, the program's nighttime activity level was 3 operations per hour with a standard deviation of 1.2, and the calculated activity threshold was 3 - 1.5 × 1.2 = 1.2 operations per hour. One morning, the actual activity level was 1 operation per hour, which was marked as an abnormal state. After 5-level decomposition of the db4 wavelet and soft thresholding denoising, and amplification of the signal by 3 times, the latent attack chain was identified: → "Late-night document scan" → "Personnel salary documents", correlation strength 0.18, exceeding the detection threshold of 0.15. In this enterprise's IoT environment, IoT devices such as smart gateways and edge computing nodes participate in the graph construction as main nodes. Their behavior patterns are significantly different from those of traditional office equipment, providing richer feature dimensions for the detection of latent attacks.

[0091] Clustering parameters: K=3, maximum number of iterations 100, convergence threshold 0.01 radians / minute. Attack chain features were extracted to construct a 14-dimensional vector, revealing two other similar chains involved. and The angles between the three attack vectors are 29° and 35°, and their Euclidean distances are 0.42 and 0.38, respectively.

[0092] The sampling interval Δt = 10 minutes, monitoring 30 time points. Cluster analysis revealed a coherent trend: the average angle converged from 42° to 22°, and the rate of angle change decreased from 0.035 radians / minute to 0.008 radians / minute. The peak distance change rate was 0.06, and the final convergence distance was 0.15. The system predicts a convergence time window of 35 minutes, with convergence strength parameters... .

[0093] Centrality calculation parameters: degree centrality weight 0.25, betweenness centrality weight 0.35, tight centrality weight 0.20, propagation influence weight 0.20. Assessment findings... The vulnerability score V=0.58, and the importance weight of "HR and payroll documents" W=0.76.

[0094] Gradient descent parameters: learning rate η = 0.01, maximum number of iterations 50, convergence threshold 0.001. Overall risk score: Risk = 0.58 × 0.76 × 2.1 = 0.93, exceeding the high-risk threshold of 0.8.

[0095] After 35 iterations of optimization, the objective function value increased from 0.65 to 0.78. Temporary isolation time coefficient: 1.5 times for main nodes, 1.2 times for behavior nodes. Constraints. Access permissions were reduced to read-only, prohibiting sensitive operations for 52 minutes (35 x 1.5). An operation blocking flag was set for the "late-night file scan" behavior for 42 minutes (35 x 1.2).

[0096] Fusion weights The success threshold was 0.10. After adjustment, the system's resistance to attacks increased from 0.71 to 0.83, and the effectiveness score ΔR=0.12 exceeded the success threshold. The mean convergence angle was extracted as 22°, and the adjustment factor was calculated. .

[0097] Based on this, the activity threshold was optimized to 1.2 × 0.947 = 1.14 times / hour, improving detection sensitivity by 5%. The peak distance change rate was 0.06, and the weight adjustment coefficient was... The weights for calculating the correlation strength will be adjusted accordingly.

[0098] Weighting coefficient: Intensity of latent threat =0.45, convergence urgency =0.35, topology resistance to attacks =0.20. Warning thresholds: Low level 0.1, Medium level 0.15, High level 0.25, Emergency level 0.35.

[0099] Calculate the three-dimensional indicators: TI=0.028, CU=0.12, TR=0.83. The comprehensive threat index CTI=0.45×0.028+0.35×0.12+0.20×0.83=0.221.

[0100] Since CTI=0.221 exceeds the intermediate warning threshold of 0.15 but is below the advanced threshold of 0.25, the system outputs an intermediate warning: "An intermediate threat targeting personnel documents has been detected, involving 3 synchronization programs. A coordinated attack is expected within 35 minutes. Threat index: 0.221. It is recommended to strengthen file access monitoring." The test environment ran for 72 hours, processing 15GB of log data, with an average CPU utilization of 12% and memory utilization of 8%. During this test, the system successfully detected one potential attack threat targeting personnel documents and issued an alert 35 minutes before the attack chain was fully formed, timely blocking unauthorized access attempts to salary data. The system identified three abnormal behavior patterns during the 72-hour test; after manual verification, two were confirmed as real threats, and one was a false alarm, achieving an actual detection accuracy of 67%. By dynamically adjusting the activity threshold and association strength weight, the system's detection sensitivity in the last 24 hours improved by 5% compared to the first 48 hours. This test verified the effectiveness of this invention in an IoT environment. Compared to existing rule-based protection systems used by enterprises, this invention's method, through a dynamic access control mechanism, implements precise intervention before the attack chain is fully formed, gaining a valuable time window for protection response in IoT environments. It is particularly suitable for complex IoT network environments containing a large number of heterogeneous IoT devices, such as smart manufacturing and smart cities.

[0101] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. 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 attack early warning method based on abnormal behavior graphs, characterized in that, include: S100: Construct an abnormal behavior graph containing main nodes, behavior nodes, and resource nodes, and establish a low-activity baseline model based on historical network data. Use a weak signal enhancement algorithm to calculate the weak correlation strength between nodes to identify latent attack chains. S200: The correlation strength of the latent attack chain is transformed into a feature vector to construct a multi-dimensional attack vector space. The convergence trend is analyzed by calculating the convergence angle and distance change rate between vectors, and the convergence time window and convergence strength parameters are obtained. S300: Based on the convergence time window and convergence strength parameters, the topological vulnerability of the graph is evaluated, and the gradient descent algorithm is used to dynamically adjust the node connection weights and temporarily isolate high-risk nodes. S400: Based on the evaluation results of the node connection weight adjustment, the convergence angle parameter is fed back to update the activity threshold setting, and the distance change rate parameter is fed back to adjust the association strength calculation weight. S500: A weighted evaluation model is established based on three dimensions: the intensity of latent attack threats, the urgency of convergence, and the topology's anti-attack capability. A comprehensive threat index is calculated, and when the comprehensive threat index exceeds the dynamic judgment threshold based on graph connectivity, hierarchical early warning information is output.

2. The network attack early warning method based on abnormal behavior graphs according to claim 1, characterized in that, The construction of the abnormal behavior map includes: Users, IoT devices, and applications in the network are defined as subject nodes; login, access, transmission, and execution of operations are defined as behavior nodes; and servers, databases, and files are defined as resource nodes. Directed edge connections are established between the main node and the behavior node, and between the behavior node and the resource node, forming a three-layer heterogeneous graph structure; Assign behavior frequency, access time, and data volume attributes to each node, and assign association strength and duration weight parameters to each edge.

3. The network attack early warning method based on abnormal behavior graphs according to claim 1, characterized in that, The strength of the weak correlation between computing nodes includes: Collect historical behavior data of each node in the abnormal behavior map within a preset time period, and calculate the average activity, activity variance, and activity distribution range of each type of node; The dynamic baseline parameters are updated using a sliding time window method, and the baseline values ​​are corrected according to changes in behavioral patterns over different time periods. Set a lower boundary threshold for normal activity, mark the behavior of nodes below the threshold as potential latent states, and use wavelet transform to decompose the behavior time series of nodes marked as latent states to extract trend signals and noise signals. High-frequency noise interference is removed by an adaptive threshold denoising method, while retaining weak feature signals of latent attack behavior; Weak feature signals are linearly enhanced using signal amplification techniques, and the correlation strength between nodes in the latent attack chain is output.

4. The network attack early warning method based on abnormal behavior graphs according to claim 1, characterized in that, The construction of the multidimensional attack vector space includes: Extract the node association strength, time interval, and behavior type features of each latent attack chain in the abnormal behavior map, and combine them to form a multi-dimensional feature vector; Establish a high-dimensional vector space with attack behavior characteristics as coordinate axes, and map all latent attack chains into vector points in the space; The cosine of the angle between vectors is calculated to quantify the similarity of attack behaviors, and the Euclidean distance is calculated to quantify the proximity of attack vectors.

5. The network attack early warning method based on abnormal behavior graphs according to claim 1, characterized in that, The steps for obtaining the convergence time window and convergence strength parameters include: Vector clustering is used to identify attack vector combinations with a co-current trend; Calculate the rate of change of the convergence angle of the attack vector combination over the time series; Calculate the rate of change of distance of the attack vector combination at a spatial location; The convergence time window is determined based on the period during which the rate of change of the convergence angle is continuously lower than the preset angle threshold, and the convergence strength parameter is determined based on the ratio of the maximum to the minimum value of the rate of change of distance.

6. The network attack early warning method based on abnormal behavior graphs according to claim 1, characterized in that, The steps for assessing the topological vulnerability of the map include: Calculate the importance weights of the main node, behavior node, and resource node respectively; The degree-centrality of computing nodes is used to calculate the number of connected nodes, the betweenness-centrality of computing nodes as a bridge in the attack path, and the propagation range of compact centrality computing nodes. Construct a cross-layer vulnerability propagation model to simulate the propagation process of attack behavior from the subject node through the behavior node to the resource node; Generate a vulnerability assessment matrix to quantify the vulnerability of each node and edge in the abnormal behavior graph under attack scenarios.

7. The network attack early warning method based on abnormal behavior graphs according to claim 1, characterized in that, The graph adjustment of the gradient descent algorithm includes: An optimization function is established with the goal of maximizing the anti-attack capability of the abnormal behavior graph, and the connection weights of each edge in the graph are set as optimization variables; Calculate the gradient value of the optimization function with respect to each edge weight to determine the direction of weight adjustment, and use the gradient descent method to iteratively update the edge weight values; For the three-layer structure of the abnormal behavior graph, weight adjustment constraints are set for the connection edges between the main layer, behavior layer and resource layer, and an adaptive learning strategy is adopted to control the weight update step size.

8. A network attack early warning method based on abnormal behavior graphs according to claim 1, characterized in that, The high-risk nodes temporarily isolated include: Based on the vulnerability scores and importance rankings of nodes in the abnormal behavior graph, nodes with vulnerability scores exceeding a preset vulnerability threshold and ranking in the top 10% of importance are selected as isolation candidates. Assess the impact of isolating candidate nodes on the connectivity of the anomalous behavior graph, and select the high-risk nodes that have the least impact on the overall connectivity of the graph after isolation; Reduce the access permission level of the main node to read-only, prohibiting it from performing sensitive operations; set an operation blocking flag for the behavior node, suspending the execution permission of this type of behavior; set a temporary access barrier for the resource node, limiting the list of authorized users; The isolation duration for each type of node is determined based on the time window for attack vector convergence prediction.

9. A network attack early warning method based on abnormal behavior graphs according to claim 1, characterized in that, The S400 implementation steps include: The difference in attack resistance before and after adjusting the connection weights of nodes in the abnormal behavior graph is used as the adjustment evaluation score. When the graph adjustment score exceeds the preset success threshold, the statistical features of the attack vector convergence angle are extracted as reference data for activity threshold optimization. The distribution characteristics of the attack vector distance change rate are extracted and used as the basis for adjusting the weights in the node association strength calculation. A weighted average method is used to fuse and update the newly extracted parameter features with historical parameters.

10. A network attack early warning method based on abnormal behavior graphs according to claim 1, characterized in that, The step of outputting hierarchical early warning information includes: The threat intensity index of latent attacks is calculated based on the number of latent attack chains, the number of nodes involved, and the average correlation strength in the abnormal behavior graph. The convergence urgency index is calculated based on the convergence speed of the attack vector in the multidimensional space, the predicted convergence time, and the convergence tightness. The topology's resistance to attacks is calculated based on the connectivity robustness, structural redundancy, and number of critical paths after topology adjustment of the abnormal behavior graph. Weighting coefficients related to the size of the abnormal behavior map were assigned to the three indicators, and the comprehensive threat index was calculated by weighted summation. Based on the comprehensive threat index range of the abnormal behavior map, four warning levels are set, and corresponding information on the main node type, abnormal behavior node type, and threatened resource node type is output for different warning levels.

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