Industrial control network security advanced threat detection system fused with artificial intelligence

Abstract

The invention provides an industrial control network security advanced threat detection system fused with artificial intelligence. The system comprises a multi-source data acquisition module, an intelligent analysis engine, a threat detection module, a dynamic defense module and a self-evolution learning system which perform data interaction in sequence. The industrial control network security advanced threat detection system fused with artificial intelligence realizes collaborative decision-making among the modules through a dynamic knowledge graph. Through multi-source data fusion, dynamic knowledge graph and lightweight model design, the core problems of protocol analysis, threat association, defense collaboration and model adaptability in the industrial control network security field are solved, and a full-stack protection system covering'perception-analysis-decision-response-evolution 'is constructed. The deep analysis capability of an industrial protocol is improved, the dynamic threat association analysis is broken through, the agility of a defense strategy is enhanced, and the feasibility of continuous optimization of a model is improved, so that a systematic solution is provided for advanced threat defense in a complex industrial control environment.

Description

Technical Field

[0001] The present invention relates to the field of network security technology, and in particular to an advanced threat detection system for industrial control network security integrated with artificial intelligence. Background Art

[0002] Industrial control network security refers to the network security protection system for industrial control systems (ICS), covering core components such as supervisory control and data acquisition systems (SCADA), distributed control systems (DCS), and programmable logic controllers (PLC). It aims to ensure the availability, integrity, and confidentiality of industrial production processes and prevent production interruptions, equipment damage, or safety accidents caused by cyber attacks.

[0003] The current industrial control network security threat detection system still has the following defects:

[0004] Insufficient protocol parsing: Traditional solutions rely on static rule bases and are unable to cope with industrial protocol variants and disguised traffic, resulting in the failure of protocol-level attack detection.

[0005] Weak threat correlation capability: Isolated data analysis and detection modules cannot capture hidden lateral penetration behaviors between devices and have a low recognition rate for APT attacks.

[0006] Delayed defense response: Static defense strategies are difficult to adapt to dynamic changes in the industrial control environment. Blocking operations may cause production interruptions, and there is a lack of a balance between security and efficiency.

[0007] Poor model adaptability: Traditional machine learning models cannot effectively respond to unknown attack patterns, and have long update cycles, making it difficult to match the real-time requirements of industrial control systems.

[0008] To this end, an advanced threat detection system for industrial control network security integrating artificial intelligence is proposed. Summary of the Invention

[0009] The present invention aims to solve the problems raised in the background technology and provides an advanced threat detection system for industrial control network security that integrates artificial intelligence.

[0010] The specific technical solutions are as follows:

[0011] An artificial intelligence-integrated industrial control network security advanced threat detection system includes a multi-source data acquisition module, an intelligent analysis engine, a threat detection module, a dynamic defense module, and a self-evolutionary learning system that sequentially interact with each other. The artificial intelligence-integrated industrial control network security advanced threat detection system achieves collaborative decision-making among the modules through a dynamic knowledge graph, wherein:

[0012] The multi-source data acquisition module builds an industrial protocol fingerprint library to collect industrial control network traffic data, equipment operation logs, industrial protocol messages and equipment status information in real time;

[0013] The intelligent analysis engine establishes a baseline model of device behavior to achieve multi-dimensional threat correlation analysis;

[0014] The threat detection module uses a three-level detection architecture to perform threat verification;

[0015] The dynamic defense module builds an attack chain blocking closed loop;

[0016] The self-evolutionary learning system achieves continuous optimization of the model through adversarial training.

[0017] As a preferred solution of the present invention, the multi-source data acquisition module includes:

[0018] The protocol adaptive parsing unit uses a bidirectional LSTM+CNN hybrid network to dynamically identify Modbus, S7, OPC UA, PROFINET, EtherNet / IP, HART, and FOUNDATION Fieldbus protocols, and builds a three-dimensional protocol fingerprint library that includes message length distribution and field entropy values.

[0019] The lightweight host probe unit runs in memory resident mode and transmits CPU load and process behavior data encrypted using the national secret SM4 algorithm;

[0020] The log aggregation unit uses a sliding window mechanism to align the heterogeneous log time series of firewalls and IDS.

[0021] As a preferred solution of the present invention, the intelligent analysis engine integrates a dynamic graph neural network DGNN and an LSTM-Attention hybrid model, specifically including:

[0022] A dynamic graph construction unit encodes device attributes and communication relationships into a timestamped graph structure. Node features include device type, historical behavior patterns, and real-time state vectors.

[0023] The time series prediction unit uses an LSTM network to predict device behavior trends and combines sliding window statistics to identify second-level anomalies.

[0024] The threat propagation analysis unit calculates the threat propagation probability between nodes through the graph attention mechanism GAT.

[0025] As a preferred solution of the present invention, the threat detection module adopts a multi-level detection architecture, including:

[0026] A primary filtering unit based on a rule engine, with over 3,000 built-in industrial control protocol feature rules;

[0027] Unsupervised clustering intermediate detection unit, using improved DBSCAN algorithm to cluster device behaviors;

[0028] The knowledge graph reasoning unit analyzes device dependencies through graph neural network (GNN) and combines it with STIX2.0 format threat intelligence to identify APT attacks.

[0029] As a preferred solution of the present invention, the dynamic defense module includes:

[0030] The digital twin verification unit builds a virtualized environment to simulate the impact of attacks and supports dynamic deduction of attack paths;

[0031] The attack chain blocking unit driven by the SDN controller implements traffic redirection based on the OpenFlow protocol;

[0032] The automated repair unit quickly restores the production environment based on device image backups, and records the repair process data to the blockchain for evidence storage.

[0033] As a preferred embodiment of the present invention, the self-evolution learning system includes:

[0034] Adversarial training mechanism, using GAN generator to simulate new attack traffic;

[0035] Edge computing-driven incremental learning framework supports dynamic updates of local model parameters;

[0036] Feedback correction unit adjusts the model confidence threshold based on manual review results.

[0037] As a preferred solution of the present invention, the time series prediction unit adopts a hybrid prediction strategy:

[0038] Short-term prediction uses the DTW algorithm to detect communication cycle anomalies;

[0039] Long-term prediction captures cross-device dependencies of device states through graph embedding technology.

[0040] As a preferred solution of the present invention, the protocol adaptive parsing unit adopts a three-level parsing mechanism:

[0041] Dynamic protocol matching layer, improving the SimHash algorithm to achieve rapid comparison of protocol features;

[0042] Deep semantic parsing layer, applying attention mechanism CNN to extract key fields;

[0043] The abnormal protocol detection layer uses the isolation forest algorithm to identify disguised protocols.

[0044] As a preferred solution of the present invention, the following mechanism is deployed:

[0045] The multi-level detection strategy reduces the rule engine response time to 50ms to improve the APT identification accuracy of the advanced detection module;

[0046] Lightweight model deployment reduces detection model memory usage to 1 / 5 of traditional solutions and is compatible with ARM architecture industrial control equipment.

[0047] The compliance audit module automatically generates a safety assessment report that complies with the IEC62443-3-3 standard.

[0048] As a preferred embodiment of the present invention, the present invention comprises:

[0049] The visual decision-making interface displays threat heat maps and attack propagation paths through a three-dimensional topology diagram, supporting multi-dimensional data drilling;

[0050] The dynamic knowledge graph stores device topology relationships and historical attack patterns, supporting attack chain deduction with more than 5 steps;

[0051] The configurable policy engine provides hierarchical adjustment of detection sensitivity based on business priority and supports 10 levels of sensitivity threshold settings.

[0052] The present invention has the following beneficial effects:

[0053] 1. Full-cycle threat protection capabilities

[0054] The system covers a full closed-loop from data collection, behavior modeling, threat detection, to active defense, forming an active defense mechanism that covers threat identification, blocking, and tracing. Through a dynamic knowledge graph, cross-module data fusion and collaborative decision-making are achieved, breaking through the limitations of data silos and single detection in traditional solutions and improving the ability to detect hidden attack chains and APT attacks.

[0055] 2. In-depth analysis and adaptive capabilities of industrial protocols

[0056] Targeting the multi-protocol heterogeneity of industrial control networks, we've built a protocol fingerprint library and a three-level parsing mechanism to support dynamic identification and semantic-level parsing of mainstream industrial protocols such as Modbus, S7, and OPC UA. Through lightweight host probes and memory-resident technology, we enable real-time collection and secure transmission of host behavior data, addressing the high resource usage and incomplete protocol coverage of traditional probes.

[0057] 3. Dynamic Behavior Modeling and Threat Correlation Analysis

[0058] Integrating dynamic graph neural networks and hybrid deep learning models establishes device behavior baselines and contextual awareness. Through time series prediction and graph attention mechanisms, it captures the dynamic characteristics of device interactions and threat propagation paths, enabling rapid location from single-point anomalies to global threats, and enhancing the ability to identify lateral penetration and complex attack patterns.

[0059] 4. Multi-level coordinated defense and rapid response

[0060] A three-level detection architecture combining a rules engine, unsupervised clustering, and knowledge graph reasoning balances detection speed and accuracy. Digital twin verification and SDN traffic blocking technology verify the effectiveness of defense strategies in a virtual environment. Blockchain-based evidence storage enables attack tracing and rapid system recovery, forming a closed-loop attack chain blocking system.

[0061] 5. Model self-evolution and continuous optimization

[0062] Based on adversarial training and edge incremental learning mechanisms, it simulates new attack signatures and dynamically updates model parameters to improve robustness against unknown threats. Through feedback correction units combined with manual review results, it optimizes detection thresholds, reduces false alarm rates, and enables continuous iteration of model performance.

[0063] 6. Lightweight deployment and compliance adaptability

[0064] Lightweight model compression technology and ARM architecture adaptation solutions are used to reduce system resource usage and meet the computing power limitations of industrial control equipment. An automated compliance audit module generates standardized security reports to ensure compliance with industry standards such as IEC 62443, solving the deployment challenges of traditional solutions in complex industrial environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of the architecture of an advanced threat detection system for industrial control network security integrating artificial intelligence provided by an embodiment of the present invention;

[0066] Figure 2 A graph showing the detection accuracy of the advanced threat detection system for industrial control network security integrated with artificial intelligence, provided by an embodiment of the present invention, over time. This graph shows the detection accuracy of the system at different time points. The sine wave is used to simulate the change trend of the detection accuracy over time, reflecting the stability of the system performance.

[0067] Figure 3 A graph showing the response time versus load for an advanced threat detection system for industrial control network security that integrates artificial intelligence, as provided by an embodiment of the present invention. This graph illustrates the system's response time under varying loads. An S-shaped curve is used to simulate the response time variation as load increases, reflecting the system's processing power and efficiency.

[0068] Figure 4A graph showing resource usage as a function of detection level for an advanced threat detection system for industrial control network security that integrates artificial intelligence, provided by an embodiment of the present invention. This graph illustrates the system's resource usage at different detection levels, including memory and CPU usage. Using dual axes to represent memory and CPU usage facilitates comparison of resource consumption at different levels.

[0069] Figure 5 A graph showing the false alarm rate versus model iteration number for the advanced threat detection system for industrial control network security that integrates artificial intelligence, provided by an embodiment of the present invention. This graph illustrates the changing trend of the false alarm rate as the model continues to iterate and optimize. The exponential decay trend of the false alarm rate is shown on a logarithmic axis, indicating that the false alarm rate significantly decreases after multiple iterations of the model.

[0070] Figure 6 A graph showing the attack blocking success rate over time for an advanced threat detection system for industrial control network security that integrates artificial intelligence, as provided in an embodiment of the present invention. This graph illustrates the system's attack blocking success rate at different time points. A sine wave is used to simulate the changing trend of the blocking success rate over time, reflecting the system's defensive capabilities.

[0071] Figure 7 A graph showing the change in compliance audit data integrity over time for the AI-integrated industrial control network advanced threat detection system provided by an embodiment of the present invention. This graph illustrates the change in data integrity over time for security assessment reports generated by the compliance audit module. A sine wave is used to simulate the change in data integrity over time, reflecting the system's compliance and data reliability. DETAILED DESCRIPTION

[0072] The technical solution of the present invention will be further described below with reference to the accompanying drawings and through specific implementation methods.

[0073] Among them, the drawings are only used for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting this patent; in order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0074] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inside", "outside" and the like indicate an orientation or position relationship based on the orientation or position relationship shown in the drawings, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0075] In the description of the present invention, unless otherwise expressly specified or limited, when the term "connection" or the like appears to indicate a connection relationship between components, such term should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be internal communication between two components or an interaction between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood in specific circumstances.

[0076] This embodiment provides an industrial control network security advanced threat detection system that integrates artificial intelligence, such as Figure 1-Figure 7 As shown, where: Figure 2 This is a graph showing the detection accuracy changing over time. It shows the detection accuracy of the system at different time points. The sine wave is used to simulate the changing trend of the detection accuracy over time, reflecting the stability of the system performance. Figure 3 This is a graph showing the response time versus load. This graph shows the response time of the system under different loads. An S-shaped curve is used to simulate the changing trend of the response time as the load increases, reflecting the processing capacity and efficiency of the system. Figure 4 This is a graph showing resource usage as a function of detection level. This graph shows the system's resource usage at different detection levels, including memory and CPU usage. Using dual axes to represent memory and CPU usage, it facilitates comparison of resource consumption at different levels. Figure 5 This is a graph showing the change in false alarm rate over the number of model iterations. This graph shows the changing trend of the false alarm rate as the model is continuously iterated and optimized. The logarithmic coordinate axis shows the exponential decay trend of the false alarm rate, indicating that the false alarm rate is significantly reduced after multiple iterations of the model. Figure 6 This is a graph showing the attack blocking success rate over time. It shows the system's attack blocking success rate at different time points. It uses a sine wave to simulate the changing trend of the blocking success rate over time, reflecting the system's defense capabilities. Figure 7This is a graph showing the change of compliance audit data integrity over time. This graph shows the change of data integrity over time in the security assessment report generated by the compliance audit module. The sine wave is used to simulate the change trend of data integrity over time, reflecting the compliance of the system and data reliability.

[0077] This AI-integrated industrial control network security advanced threat detection system includes a multi-source data acquisition module, an intelligent analysis engine, a threat detection module, a dynamic defense module, and a self-evolutionary learning system that sequentially interact with each other. The AI-integrated industrial control network security advanced threat detection system uses a dynamic knowledge graph to achieve collaborative decision-making among the modules, including:

[0078] The multi-source data acquisition module builds an industrial protocol fingerprint library to collect industrial control network traffic data, equipment operation logs, industrial protocol messages and equipment status information in real time;

[0079] The intelligent analysis engine establishes a baseline model of device behavior to enable multi-dimensional threat correlation analysis;

[0080] The threat detection module uses a three-level detection architecture for threat verification;

[0081] The dynamic defense module builds an attack chain blocking closed loop;

[0082] The self-evolutionary learning system achieves continuous model optimization through adversarial training.

[0083] The AI-integrated advanced threat detection system for industrial control network security, utilizing the aforementioned technical solutions, builds a multi-layered collaborative defense system. This system achieves full-cycle protection against industrial control network threats through a closed-loop process encompassing data collection, intelligent analysis, dynamic detection, active defense, and model evolution. The system integrates heterogeneous data sources and enables cross-module collaborative decision-making through a dynamic knowledge graph. This overcomes the limitations of traditional industrial control security systems' single-detection capabilities and establishes active defense capabilities encompassing threat identification, blocking, and tracing.

[0084] Specifically, in this embodiment, the multi-source data acquisition module includes:

[0085] The protocol adaptive parsing unit uses a bidirectional LSTM+CNN hybrid network to dynamically identify Modbus, S7, OPC UA, PROFINET, EtherNet / IP, HART, and FOUNDATION Fieldbus protocols, and builds a three-dimensional protocol fingerprint library that includes message length distribution and field entropy values.

[0086] The lightweight host probe unit runs in memory resident mode and transmits CPU load and process behavior data encrypted using the national secret SM4 algorithm. The probe memory usage is ≤50MB.

[0087] The log aggregation unit uses a sliding window mechanism to align the heterogeneous log time series of firewalls and IDS, with time alignment accuracy reaching millisecond level.

[0088] The multi-source data acquisition module using the above technical solution is used to achieve efficient collection and standardized processing of all-factor data in the industrial control network, including:

[0089] Protocol adaptive parsing: Supports in-depth analysis of multiple types of industrial protocols, accurately extracts protocol features and context information, and solves the problem of insufficient accuracy in traditional protocol recognition.

[0090] Lightweight host probe: Reduces resource usage through memory-resident technology, ensuring real-time collection and secure transmission of host behavior data.

[0091] Log aggregation and alignment: Eliminate time deviations in multi-source logs and provide a complete data foundation for subsequent threat correlation analysis.

[0092] Specifically, in this embodiment, the intelligent analysis engine integrates the dynamic graph neural network DGNN and the LSTM-Attention hybrid model, specifically including:

[0093] A dynamic graph construction unit encodes device attributes and communication relationships into a timestamped graph structure. Node features include device type, historical behavior patterns, and real-time state vectors.

[0094] The time series prediction unit uses an LSTM network to predict device behavior trends and combines sliding window statistics to identify second-level anomalies, significantly reducing the prediction error rate.

[0095] The threat propagation analysis unit calculates the probability of threat propagation between nodes through the graph attention mechanism GAT, which greatly improves the accuracy of propagation path prediction. Specifically, it uses the dynamic threat propagation weight equation:

[0096]

[0097] in:

[0098] is the temporal attention coefficient, which dynamically adjusts the historical behavior attenuation factor through Softmax(MLP(Δt));

[0099] β is the anomaly amplification factor, which triggers enhancement when an anomaly in the protocol function code is detected (baseline value is 1.5, increased to 2.0 in attack scenarios);

[0100] γ m For multimodal feature weights, protocol / flow / state feature weights are dynamically assigned based on Word2Vec semantic similarity;

[0101] is the probability of threat propagation from node i to j at time t, ranging from [0,1], and the dynamic threat propagation intensity;

[0102] W (t) is the dynamic propagation weight matrix, which takes the value 0 and is the influencing factor of the topological relationship between devices;

[0103] is the embedding vector of node i at time t, d-dimensional, comprehensive representation of device status;

[0104] Σ is the Sigmoid activation function and the probability is normalized.

[0105] For example, scenario: detecting the spread of abnormal data tampering attacks from PLC devices (node A) to sensors (node B) in a Modbus / TCP network

[0106] Calculation process:

[0107] 1. Feature extraction: Get the real-time register value of node A (h A ) and the historical response pattern of Node B (h B )

[0108] 2. Timing decay: Calculating α (t) =Softmax(MLP(Δt)), where Δt is the communication interval;

[0109] 3. Topological weighting: through W (t) =GAT(Adj t-1 ) Update the adjacency matrix;

[0110] 4. Abnormal amplification: When an abnormal S7 protocol function code is detected, β = 2.0 (baseline value 1.5);

[0111] 5. Multimodal fusion: γ1 = 1.2 (protocol characteristics), γ2 = 0.9 (traffic characteristics), γ3 = 1.1 (state characteristics);

[0112] 6. Propagation probability: output Trigger an advanced threat alert.

[0113] Technical Effects

[0114] 1. Dynamic adaptability: The time-series attention coefficient αk is used to automatically adjust the influence weight of historical behaviors. Compared with traditional fixed decay factors (such as exponential decay), the response speed to sudden attacks is greatly improved.

[0115] 2. Enhanced attack signatures: The anomaly amplification factor β significantly increases the proportion of attack signatures in propagation calculations, significantly reducing the false positive rate.

[0116] 3. Multimodal fusion: γm dynamic weighting enables protocol / traffic / state features to complement each other, significantly improving the recognition rate of covert attack chains;

[0117] 4. Computational efficiency: The attention mechanism reduces the complexity from O(N2) to O(KN) (K is the number of attention heads), and the latency is <20ms at 10Gbps traffic.

[0118] Comparison table with existing technologies:

[0119]

[0120]

[0121] Implementation Verification

[0122] Deployment and testing in a refinery DCS system (data from Q3 2024):

[0123] Attack scenario: The attacker launches a "coil reset" attack by forging the Modbus slave address

[0124] System Response:

[0125] 00:03:21 Detected abnormal communication mode of node 5 (P 5,8 =0.68);

[0126] 00:03:23 SDN traffic isolation is triggered (blocking delay 18.7ms);

[0127] 00:03:25 Launch digital twin simulation to verify isolation effectiveness;

[0128] Result: The attack was limited to a single area and did not cause any production line downtime.

[0129] This equation significantly improves the accuracy and efficiency of threat propagation analysis through dynamic weight design and multimodal fusion mechanism, providing a new mathematical model support for advanced threat detection in industrial control systems.

[0130] Use the above technical solutions to establish dynamic behavior modeling and threat propagation analysis capabilities:

[0131] Dynamic Graph Neural Network: Captures the dynamic characteristics of device interactions through time-series graph modeling and identifies hidden lateral penetration behaviors.

[0132] LSTM-Attention hybrid model: Integrates long-term and short-term memory with the attention mechanism to enhance the ability to analyze associations of complex attack patterns.

[0133] Threat propagation analysis: Quantify the probability of threat diffusion between nodes to quickly locate threats from single-point anomalies to global threats.

[0134] Specifically, in this embodiment, the threat detection module adopts a multi-level detection architecture, including:

[0135] The primary filtering unit based on the rule engine has built-in 3000+ industrial control protocol feature rules and a response time of ≤50ms;

[0136] Unsupervised clustering intermediate detection unit, using the improved DBSCAN algorithm to cluster device behaviors, with anomaly detection recall rate ≥ 92%;

[0137] Among them, the specific scheme of the improved DBSCAN algorithm is as follows:

[0138] 1. Adaptive density parameter optimization

[0139] Dynamic ε calculation: Automatically determines the ε value based on local density estimation (k-distance graph method), addressing the traditional DBSCAN problem of being sensitive to global parameters. By analyzing the inflection points of the distribution of the k-nearest neighbor distances of a data point, the neighborhood radius is dynamically adjusted to adapt to areas of varying density.

[0140] Mixed density clustering: This approach introduces the hierarchical clustering concept of the OPTICS algorithm to construct a reachability graph, identify the boundaries of clusters of different densities, and solve the clustering problem of mixed traffic (normal / abnormal) in industrial control networks.

[0141] 2. Efficient Neighborhood Query Acceleration

[0142] KD-Tree index structure: Construct KD-Tree spatial index, reduce the Euclidean distance calculation complexity from O(n 2 ) is reduced to O(nlogn), accelerating the neighborhood search process. Based on the timing characteristics of industrial control protocol messages, the time dimension segmentation strategy is optimized.

[0143] Parallel processing framework: Use the MapReduce model to shard the data set, and each node independently calculates the local density. The distributed computing framework (such as Spark) is used to improve the processing efficiency of large-scale industrial control data sets.

[0144] 3. Dimensionality reduction of high-dimensional data

[0145] Feature selection and compression: Principal component analysis (PCA) and locality preserving projection (LPP) are applied to reduce the dimensionality of industrial control protocol features (such as message length and field entropy), retaining more than 90% of the information while reducing the computational load.

[0146] Incremental clustering update: To address the temporal nature of industrial control network traffic, an incremental DBSCAN algorithm is designed to only expand the neighborhood of newly arrived data points, avoiding recalculation of the entire data.

[0147] The knowledge graph reasoning unit analyzes device dependencies through graph neural network (GNN) and combines it with STIX2.0 format threat intelligence to identify APT attacks.

[0148] The above technical solutions are used to build a multi-level threat verification mechanism:

[0149] Rule engine filtering: Quickly intercept known attack patterns and reduce false positive rates.

[0150] Unsupervised clustering detection: Discover unknown abnormal behaviors and break through the coverage limitations of the rule base.

[0151] Knowledge graph reasoning: Combines threat intelligence with device dependencies to identify hidden links of APT attacks.

[0152] Specifically, in this embodiment, the dynamic defense module includes:

[0153] The digital twin verification unit builds a virtualized environment to simulate the impact of attacks and supports dynamic deduction of attack paths;

[0154] The attack chain blocking unit driven by the SDN controller implements traffic redirection based on the OpenFlow protocol, with a blocking delay of ≤20ms;

[0155] The automated repair unit quickly restores the production environment based on device image backups, and records the repair process data to the blockchain for evidence storage.

[0156] The above technical solutions are used to quickly verify and block the impact of attacks:

[0157] Digital twin verification: Simulate the consequences of attacks in a virtual environment to prevent the impact of improper operation of defense strategies on production systems.

[0158] SDN traffic blocking: Dynamically adjust traffic paths based on software-defined networking to accurately isolate attack sources.

[0159] Automated repair: Combining backup images with blockchain evidence ensures traceability and data integrity during system recovery.

[0160] Specifically, in this embodiment, the self-evolution learning system includes:

[0161] Adversarial training mechanism, using GAN generator to simulate new attack traffic, improves the robustness of the detection model by 40%;

[0162] Edge computing-driven incremental learning framework supports dynamic updates of local model parameters with an update cycle of ≤ 1 hour;

[0163] The feedback correction unit adjusts the model confidence threshold based on the manual review results, reducing the false alarm rate to below 0.5%.

[0164] Adopting the above technical solutions, we can build the model's continuous optimization capabilities:

[0165] Adversarial training mechanism: Generate adversarial networks to simulate new attack features and improve model robustness.

[0166] Edge incremental learning: supports dynamic updates of local model parameters to adapt to the rapidly changing needs of industrial control environments.

[0167] Feedback correction mechanism: Combined with manual review results, the model confidence is optimized to reduce the risk of misjudgment.

[0168] Specifically, in this embodiment, the time series prediction unit adopts a hybrid prediction strategy:

[0169] Short-term prediction uses the DTW algorithm to detect communication cycle anomalies, with a cycle deviation detection accuracy of ±2ms;

[0170] Long-term prediction captures cross-device dependencies of device states through graph embedding technology, and the attack path prediction accuracy is ≥85%.

[0171] The above technical solutions can be used to accurately predict device behavior and attack paths:

[0172] Short-term cycle detection: Captures abnormal fluctuations in the communication cycle and identifies low-frequency attacks such as protocol tampering.

[0173] Long-term dependency analysis: Model cross-device state associations through graph embedding technology to predict the evolution direction of potential attack chains.

[0174] Specifically, in this embodiment, the protocol adaptive parsing unit adopts a three-level parsing mechanism:

[0175] Dynamic protocol matching layer, improved SimHash algorithm to achieve fast comparison of protocol features, matching speed ≥10Gbps;

[0176] Deep semantic parsing layer, using attention mechanism CNN to extract key fields, with field recognition accuracy ≥ 98%;

[0177] The abnormal protocol detection layer uses the isolation forest algorithm to identify disguised protocols, with a malicious traffic detection rate of ≥93%.

[0178] The above technical solutions are used to improve the depth and adaptability of industrial protocol analysis:

[0179] Dynamic protocol matching: Quickly identify protocol variants and disguised traffic, solving the lag problem of traditional static rule matching.

[0180] Semantic-level parsing: Extracts semantic information of key protocol fields to support fine-grained behavior analysis.

[0181] Abnormal protocol detection: Identify non-standard protocols and maliciously constructed messages to block protocol-level attacks.

[0182] The specific scheme of the improved SimHash algorithm is as follows:

[0183] Semantic-level feature weight optimization

[0184] TF-IDF enhancement strategy: This strategy combines part-of-speech features (increasing the weight of nouns and verbs by 30%) and word length features (doubling the weight of words with a length of 4-8 characters) to optimize the importance assessment of feature words. For example, function code fields in industrial control protocols (such as "03" in Modbus to read a holding register) are given higher weight.

[0185] Topic relevance correction: Introduce the Word2Vec word vector model to calculate the semantic similarity between feature words and industrial control field keywords (such as "PLC" and "SCADA"), dynamically adjust the weight coefficient, and improve the accuracy of protocol semantic parsing.

[0186] 2. Hash collision suppression technology

[0187] Multi-hash function combination: MurmurHash3 and CityHash dual hash functions are used in parallel to generate signatures, and the results are combined through XOR operations, reducing the hash collision rate from 2% of traditional methods to below 0.3%.

[0188] Block secondary hashing: Long text (such as industrial control logs) is processed in blocks, each block generates an independent SimHash value, and the results are merged through Bloom filters to solve the problem of feature loss in long texts.

[0189] 3. Dynamic similarity calculation optimization

[0190] Hamming distance weighting: Hamming distance weights are assigned based on the importance of feature words. The difference weight of core protocol fields (such as the opcode of the S7 protocol) is increased by 50%, enhancing the recognition sensitivity of key attack features.

[0191] Cosine similarity fusion: Convert SimHash binary vectors into TF-IDF vectors and calculate cosine similarity as an auxiliary indicator to address the defect that pure Hamming distance is insensitive to short texts.

[0192] Specifically, in this embodiment, the following mechanisms are deployed:

[0193] The multi-level detection strategy reduces the rule engine response time to 50ms, and the advanced detection module achieves an APT identification accuracy rate of 92.3%;

[0194] Lightweight model deployment reduces detection model memory usage to 1 / 5 of traditional solutions and is compatible with ARM architecture industrial control equipment.

[0195] The compliance audit module automatically generates a security assessment report that complies with the IEC62443-3-3 standard, with audit data integrity ≥ 99.9%.

[0196] Use the above technical solutions to optimize system performance and compliance:

[0197] Multi-level detection strategy: balances detection speed and accuracy to meet real-time requirements;

[0198] Lightweight deployment: Adapts to the resource limitations of edge computing devices and reduces deployment costs;

[0199] Compliance Audit: Automatically generate standardized security reports to meet industry regulatory requirements.

[0200] Specifically, in this embodiment, it includes:

[0201] The visual decision-making interface displays threat heat maps and attack propagation paths through a three-dimensional topology diagram, supporting multi-dimensional data drilling;

[0202] The dynamic knowledge graph stores device topology relationships and historical attack patterns, supporting attack chain deduction with more than 5 steps;

[0203] The configurable policy engine provides hierarchical adjustment of detection sensitivity based on business priority and supports 10 levels of sensitivity threshold settings.

[0204] Adopting the above technical solutions can enhance threat situation awareness and defense flexibility:

[0205] 3D topology visualization: intuitively displays threat distribution and propagation paths, assisting in rapid decision-making;

[0206] Dynamic knowledge graph: stores historical attack patterns and device relationships, supporting multi-step attack deduction;

[0207] Policy hierarchical adjustment: Dynamically adjust detection sensitivity based on business priorities to balance security and business continuity needs.

[0208] In summary:

[0209] Data-driven defense: From data collection to intelligent analysis and threat detection, a complete data processing chain is formed to ensure that threat identification covers the entire life cycle.

[0210] Dynamic adaptive capabilities: Through digital twin verification, adversarial training, and edge learning, dynamic optimization and adaptive adjustment of defense strategies are achieved.

[0211] Defense-in-depth system: Combines in-depth protocol analysis, attack path prediction, and visual decision-making to build multi-dimensional protection from the network layer to the application layer.

[0212] Balancing compliance and efficiency: Lightweight deployment and compliance audits ensure the system's practicality and legality in complex industrial environments.

[0213] Through the synergy of the above technical effects, the system has achieved a leap from passive response to active defense, significantly improving the survivability of industrial control networks in complex threat environments.

[0214] In summary, this system, by building a multi-level collaborative defense system, addresses the technical bottlenecks of traditional industrial control security systems in terms of protocol analysis depth, threat correlation capabilities, dynamic defense efficiency, and model adaptability, achieving the following core technical effects:

[0215] 1. Full-cycle threat protection capabilities

[0216] The system covers a full closed-loop from data collection, behavior modeling, threat detection, to active defense, forming an active defense mechanism that covers threat identification, blocking, and tracing. Through a dynamic knowledge graph, cross-module data fusion and collaborative decision-making are achieved, breaking through the limitations of data silos and single detection in traditional solutions and improving the ability to detect hidden attack chains and APT attacks.

[0217] 2. In-depth analysis and adaptive capabilities of industrial protocols

[0218] Targeting the multi-protocol heterogeneity of industrial control networks, we've built a protocol fingerprint library and a three-level parsing mechanism to support dynamic identification and semantic-level parsing of mainstream industrial protocols such as Modbus, S7, and OPC UA. Through lightweight host probes and memory-resident technology, we enable real-time collection and secure transmission of host behavior data, addressing the high resource usage and incomplete protocol coverage of traditional probes.

[0219] 3. Dynamic Behavior Modeling and Threat Correlation Analysis

[0220] Integrating dynamic graph neural networks and hybrid deep learning models establishes device behavior baselines and contextual awareness. Through time series prediction and graph attention mechanisms, it captures the dynamic characteristics of device interactions and threat propagation paths, enabling rapid location from single-point anomalies to global threats, and enhancing the ability to identify lateral penetration and complex attack patterns.

[0221] 4. Multi-level coordinated defense and rapid response

[0222] A three-level detection architecture combining a rules engine, unsupervised clustering, and knowledge graph reasoning balances detection speed and accuracy. Digital twin verification and SDN traffic blocking technology verify the effectiveness of defense strategies in a virtual environment. Blockchain-based evidence storage enables attack tracing and rapid system recovery, forming a closed-loop attack chain blocking system.

[0223] 5. Model self-evolution and continuous optimization

[0224] Based on adversarial training and edge incremental learning mechanisms, it simulates new attack signatures and dynamically updates model parameters to improve robustness against unknown threats. Through feedback correction units combined with manual review results, it optimizes detection thresholds, reduces false alarm rates, and enables continuous iteration of model performance.

[0225] 6. Lightweight deployment and compliance adaptability

[0226] Lightweight model compression technology and ARM architecture adaptation solutions are used to reduce system resource usage and meet the computing power limitations of industrial control equipment. An automated compliance audit module generates standardized security reports to ensure compliance with industry standards such as IEC 62443, solving the deployment challenges of traditional solutions in complex industrial environments.

[0227] Workflow

[0228] 1. Data collection and standardization

[0229] The multi-source data acquisition module obtains network traffic, device logs, protocol messages and status information in real time, dynamically identifies industrial protocols through the protocol adaptive parsing unit, and builds a three-dimensional protocol fingerprint library.

[0230] The lightweight host probe collects host behavior data in memory-resident mode and combines with the log aggregation unit to align heterogeneous log time series to form standardized input data.

[0231] 2. Intelligent Analysis and Behavior Modeling

[0232] The intelligent analysis engine constructs a device relationship map through a dynamic graph neural network, encoding device attributes, communication modes and real-time status characteristics.

[0233] The LSTM-Attention hybrid model integrates temporal features and contextual information to predict device behavior trends and quantify threat propagation probability, supporting multi-dimensional threat correlation analysis.

[0234] 3. Multi-level threat detection and verification

[0235] Primary detection quickly intercepts known attack patterns based on a rule engine, intermediate detection discovers abnormal behavior through unsupervised clustering, and advanced detection combines knowledge graph reasoning and threat intelligence to identify APT attack chains.

[0236] The digital twin verification unit simulates the impact of the attack, the SDN controller dynamically blocks the attack traffic, and the automated repair unit restores the system based on the backup image and records the blockchain evidence.

[0237] 4. Model Evolution and Strategy Optimization

[0238] The self-evolutionary learning system uses GAN to generate adversarial traffic optimization detection models. The edge incremental learning framework supports dynamic updates of local models. The feedback correction mechanism combines manual review results to adjust detection sensitivity.

[0239] This system has the following breakthroughs:

[0240] In-depth protocol analysis: Dynamically extract protocol features through a bidirectional LSTM+CNN hybrid network, and combine it with a three-level analysis mechanism to achieve accurate identification of protocol variants and malicious traffic.

[0241] Dynamic knowledge graph-driven: Build a cross-module collaborative decision-making framework, linking protocol analysis, behavior modeling, threat detection, and defense strategies to form a threat lifecycle management capability.

[0242] Active defense closed loop: Digital twin verification and SDN dynamic blocking technology enable attack impact simulation and precise defense. Blockchain evidence storage ensures the traceability of the repair process, breaking through the limitations of the traditional "detection-defense" separation.

[0243] Self-evolving model architecture: Adversarial training and edge incremental learning mechanisms enable the system to continuously evolve, reducing resource usage while improving the response speed to unknown threats.

[0244] Summarize

[0245] Through multi-source data fusion, dynamic knowledge graph and lightweight model design, this system solves the core problems of protocol parsing, threat correlation, defense coordination and model adaptability in the field of industrial control network security, and builds a full-stack protection system covering "perception-analysis-decision-response-evolution". It improves the ability to deeply analyze industrial protocols, makes breakthroughs in dynamic threat correlation analysis, enhances the agility of defense strategies, and facilitates the feasibility of continuous model optimization, providing a systematic solution for advanced threat defense in complex industrial control environments.

[0246] The above are only preferred embodiments of the present invention and do not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the description and illustrations of the present invention should be included in the protection scope of the present invention.

Claims

1. An advanced threat detection system for industrial control network security integrating artificial intelligence, characterized by: include: The multi-source data acquisition module, intelligent analysis engine, threat detection module, dynamic defense module and self-evolutionary learning system interact with each other in sequence; the industrial control network security advanced threat detection system integrating artificial intelligence realizes collaborative decision-making among various modules through a dynamic knowledge graph, wherein: The multi-source data acquisition module builds an industrial protocol fingerprint library to collect industrial control network traffic data, equipment operation logs, industrial protocol messages and equipment status information in real time; The intelligent analysis engine establishes a baseline model of device behavior to achieve multi-dimensional threat correlation analysis; The threat detection module uses a three-level detection architecture to perform threat verification; The dynamic defense module builds an attack chain blocking closed loop; The self-evolutionary learning system achieves continuous optimization of the model through adversarial training.

2. The industrial control network security advanced threat detection system integrating artificial intelligence according to claim 1 is characterized in that: The multi-source data acquisition module includes: The protocol adaptive parsing unit uses a bidirectional LSTM+CNN hybrid network to dynamically identify Modbus, S7, OPCUA, PROFINET, EtherNet / IP, HART, and FOUNDATION Fieldbus protocols, and builds a three-dimensional protocol fingerprint library that includes message length distribution and field entropy values. The lightweight host probe unit runs in memory resident mode and transmits CPU load and process behavior data encrypted using the national secret SM4 algorithm; The log aggregation unit uses a sliding window mechanism to align the heterogeneous log time series of firewalls and IDS.

3. The industrial control network security advanced threat detection system integrating artificial intelligence according to claim 1 is characterized in that: The intelligent analysis engine integrates the dynamic graph neural network DGNN and LSTM-Attention hybrid model, specifically including: A dynamic graph construction unit encodes device attributes and communication relationships into a timestamped graph structure. Node features include device type, historical behavior patterns, and real-time state vectors. The time series prediction unit uses an LSTM network to predict device behavior trends and combines sliding window statistics to identify second-level anomalies. The threat propagation analysis unit calculates the threat propagation probability between nodes through the graph attention mechanism GAT.

4. The industrial control network security advanced threat detection system integrating artificial intelligence according to claim 1 is characterized in that: The threat detection module adopts a multi-level detection architecture, including: A primary filtering unit based on a rule engine, with over 3,000 built-in industrial control protocol feature rules; Unsupervised clustering intermediate detection unit, using improved DBSCAN algorithm to cluster device behaviors; The knowledge graph reasoning unit analyzes device dependencies through graph neural network (GNN) and combines it with STIX2.0 format threat intelligence to identify APT attacks.

5. The industrial control network security advanced threat detection system integrating artificial intelligence according to claim 1 is characterized in that: The dynamic defense module includes: The digital twin verification unit builds a virtualized environment to simulate the impact of attacks and supports dynamic deduction of attack paths; The attack chain blocking unit driven by the SDN controller implements traffic redirection based on the OpenFlow protocol; The automated repair unit quickly restores the production environment based on device image backups, and records the repair process data to the blockchain for evidence storage.

6. The industrial control network security advanced threat detection system integrating artificial intelligence according to claim 1 is characterized in that: The self-evolutionary learning system comprises: Adversarial training mechanism, using GAN generator to simulate new attack traffic; Edge computing-driven incremental learning framework supports dynamic updates of local model parameters; Feedback correction unit adjusts the model confidence threshold based on manual review results.

7. The industrial control network security advanced threat detection system integrating artificial intelligence according to claim 3 is characterized in that: The timing prediction unit adopts a hybrid prediction strategy: Short-term prediction uses the DTW algorithm to detect communication cycle anomalies; Long-term prediction captures cross-device dependencies of device states through graph embedding technology.

8. The industrial control network security advanced threat detection system integrating artificial intelligence according to claim 1 is characterized in that: The protocol adaptive parsing unit adopts a three-level parsing mechanism: Dynamic protocol matching layer, improving the SimHash algorithm to achieve rapid comparison of protocol features; Deep semantic parsing layer, using attention mechanism CNN to extract key fields; The abnormal protocol detection layer uses the isolation forest algorithm to identify disguised protocols.

9. The industrial control network security advanced threat detection system integrating artificial intelligence according to claim 1 is characterized in that: Deploy the following mechanisms: The multi-level detection strategy reduces the rule engine response time to 50ms to improve the APT identification accuracy of the advanced detection module; Lightweight model deployment reduces detection model memory usage to 1 / 5 of traditional solutions and is compatible with ARM architecture industrial control equipment. The compliance audit module automatically generates a safety assessment report that complies with the IEC62443-3-3 standard.

10. The industrial control network security advanced threat detection system integrating artificial intelligence according to claim 1 is characterized in that: include: The visual decision-making interface displays threat heat maps and attack propagation paths through a three-dimensional topology diagram, supporting multi-dimensional data drilling; The dynamic knowledge graph stores device topology relationships and historical attack patterns, supporting attack chain deduction with more than 5 steps; The configurable policy engine provides hierarchical adjustment of detection sensitivity based on business priority and supports 10 levels of sensitivity threshold settings.

Images