Industrial control network security advanced threat detection system fusing artificial intelligence

Abstract

The application provides an industrial control network security advanced threat detection system fusing artificial intelligence, comprising 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 sequentially carry out data interaction; the industrial control network security advanced threat detection system fusing artificial intelligence realizes collaborative decision among modules through a dynamic knowledge graph. Through multi-source data fusion, a dynamic knowledge graph and lightweight model design, the application solves the core problems of protocol analysis, threat correlation, defense collaboration and model adaptability in the field of industrial control network security, builds a full-stack protection system covering "perception-analysis-decision-response-evolution", improves the deep analysis capability of industrial protocols, breaks through dynamic threat correlation analysis, enhances the agility of defense strategies, and makes the model continuously optimized, thereby providing a systematic solution for advanced threat defense in complex industrial control environments.

Description

Technical Field

[0001] This invention relates to the field of network security technology, specifically to an advanced threat detection system for industrial control network security that integrates artificial intelligence. Background Technology

[0002] Industrial control network security refers to the network security protection system for industrial control systems (ICS), covering core components such as monitoring 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 network attacks.

[0003] Current industrial control network security threat detection systems still have the following shortcomings:

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

[0005] Weak threat correlation capabilities: Isolated data analysis and detection modules cannot capture covert lateral movement between devices, resulting in a low APT attack identification rate.

[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 mechanism between safety and efficiency.

[0007] Poor model adaptability: Traditional machine learning models cannot effectively cope with unknown attack patterns, and their long update cycle makes it difficult to match the real-time requirements of industrial control systems.

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

[0009] The present invention aims to solve the problems mentioned in the background art by providing an advanced threat detection system for industrial control network security that integrates artificial intelligence.

[0010] The specific technical solution is as follows:

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

[0012] The multi-source data acquisition module constructs an industrial protocol fingerprint library, and collects industrial control network flow data, device operation logs, industrial protocol messages and device state information in real time.

[0013] The intelligent analysis engine establishes a device behavior baseline model, and realizes multi-dimensional threat correlation analysis.

[0014] The threat detection module adopts a three-level detection architecture for threat verification.

[0015] The dynamic defense module constructs an attack chain blocking closed loop.

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

[0017] As a preferred scheme of the application, the multi-source data acquisition module comprises:

[0018] The protocol adaptive analysis unit adopts a bidirectional LSTM+CNN hybrid network to dynamically identify Modbus protocol, S7 protocol, OPC UA protocol, PROFINET protocol, EtherNet / IP protocol, HART protocol and Foundation Fieldbus FF protocol, and constructs a three-dimensional protocol fingerprint library containing message length distribution and field entropy value.

[0019] The lightweight host probe unit runs in a memory resident mode, and transmits CPU load and process behavior data through a national encryption SM4 algorithm.

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

[0021] As a preferred scheme of the application, the intelligent analysis engine integrates a dynamic graph neural network DGNN and an LSTM-Attention hybrid model, and specifically comprises:

[0022] The dynamic graph construction unit encodes device attributes and communication relationships into a graph structure with timestamps, and node features include device types, historical behavior patterns and real-time state vectors.

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

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

[0025] As a preferred scheme of the application, the threat detection module adopts a multi-level detection architecture, comprising:

[0026] The primary filtering unit based on a rule engine internally stores 3000+ industrial control protocol feature rules.

[0027] The unsupervised clustering intermediate detection unit adopts an improved DBSCAN algorithm to cluster device behaviors.

[0028] The knowledge graph reasoning unit analyzes device dependency relationships through a graph neural network (GNN) and realizes APT attack identification in combination with STIX2.0 format threat intelligence.

[0029] As a preferred scheme of the present application, the dynamic defense module comprises:

[0030] The digital twin verification unit constructs a virtualized environment to simulate attack influence and supports dynamic deduction of attack paths.

[0031] The SDN controller-driven attack chain blocking unit realizes traffic redirection based on an OpenFlow protocol.

[0032] The automated repair unit quickly recovers a production environment according to a device image backup, and records repair process data to a blockchain for storage.

[0033] As a preferred scheme of the present application, the self-evolution learning system comprises:

[0034] The adversarial training mechanism uses a GAN generator to simulate new attack traffic.

[0035] The edge computing-driven incremental learning framework supports dynamic updating of local model parameters.

[0036] The feedback correction unit adjusts a model confidence threshold according to an artificial review result.

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

[0038] The short-term prediction adopts a DTW algorithm to detect communication cycle abnormalities.

[0039] The long-term prediction captures device state cross-device dependency relationships through a graph embedding technique.

[0040] As a preferred scheme of the present application, the protocol adaptive analysis unit adopts a three-level analysis mechanism:

[0041] The dynamic protocol matching layer improves a SimHash algorithm to realize rapid comparison of protocol features.

[0042] The deep semantic analysis layer applies an attention mechanism CNN to extract key fields.

[0043] The abnormal protocol detection layer identifies disguised protocols through an isolation forest algorithm.

[0044] As a preferred scheme of the present application, the following mechanisms are deployed:

[0045] Multi-level detection strategy shortens the response time of the rule engine to 50ms to improve the APT identification accuracy of the high-level detection module;

[0046] Lightweight model deployment makes the memory occupation of the detection model 1 / 5 of the traditional scheme, and adapts to ARM architecture industrial control equipment;

[0047] The compliance audit module automatically generates a security evaluation report in accordance with the IEC62443-3-3 standard.

[0048] As a preferred scheme of the application, comprising:

[0049] The visual decision interface displays a threat heat map and an attack propagation path through a three-dimensional topology map, and supports multi-dimensional data drilling;

[0050] The dynamic knowledge graph storage device topology relationship and historical attack mode, support more than 5-step attack chain deduction;

[0051] The configurable policy engine provides detection sensitivity grading adjustment based on business priority, and supports 10-level sensitivity threshold setting.

[0052] The application has the following beneficial effects:

[0053] 1. Full-cycle threat protection capability

[0054] The system covers the full-link closed loop from data collection, behavior modeling, threat detection to active defense, forms an active defense mechanism covering threat identification, blocking and tracing, realizes cross-module data fusion and collaborative decision-making through a dynamic knowledge graph, breaks through the limitations of data silos and single detection in traditional schemes, and improves the discovery capability of hidden attack chains and APT attacks.

[0055] 2. Industrial protocol deep analysis and adaptive capability

[0056] In view of the multi-protocol heterogeneity of industrial control networks, a protocol fingerprint library and a three-level analysis mechanism are constructed to support dynamic identification and semantic-level analysis of mainstream industrial protocols such as Modbus, S7 and OPC UA. Through lightweight host probes and memory resident technology, real-time collection and secure transmission of host behavior data are realized, solving the problems of high resource occupation 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, device behavior baselines and context awareness capabilities are established. Through time series prediction and graph attention mechanisms, dynamic features of device interactions and threat propagation paths are captured to quickly locate from single-point anomalies to global threats, enhancing the recognition capability of horizontal penetration and complex attack patterns.

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

[0060] A three-level detection architecture combining rule engine, unsupervised clustering and knowledge graph reasoning is adopted to balance detection speed and accuracy. Through digital twin verification and SDN traffic blocking technology, the effectiveness of defense strategies is verified in a virtual environment. Combined with blockchain storage, attack tracing and system rapid recovery are realized, forming an attack chain blocking closed loop.

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

[0062] Based on the mechanism of adversarial training and edge incremental learning, the model parameters are dynamically updated to simulate new attack features and improve the robustness to unknown threats. Through the feedback correction unit combined with artificial review results, the detection threshold is optimized to reduce the false positive rate and realize the continuous iteration of model performance.

[0063] 6. Lightweight deployment and compliance adaptability

[0064] Lightweight model compression technology and ARM architecture adaptation scheme are adopted to reduce system resource occupation and meet the power limit of industrial control equipment. Through the automatic compliance audit module, standardized security reports are generated to ensure compliance with industry standards such as IEC 62443, solving the deployment problems of traditional solutions in complex industrial environments. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 The architecture diagram of the fusion artificial intelligence industrial control network security advanced threat detection system provided by the embodiment of the present application is provided.

[0066] Figure 2 The detection accuracy rate curve of the fusion artificial intelligence industrial control network security advanced threat detection system provided by the embodiment of the present application changes with time. The graph shows the detection accuracy of the system at different time points. The change trend of detection accuracy with time is simulated by a sine wave, reflecting the stability of system performance

[0067] Figure 3 The response time curve of the fusion artificial intelligence industrial control network security advanced threat detection system provided by the embodiment of the present application changes with load. This graph shows the response time of the system under different loads. The change trend of response time with increasing load is simulated by an S-shaped curve, reflecting the processing capacity and efficiency of the system.

[0068] Figure 4The resource occupation change curve diagram of the fusion artificial intelligence industrial control network security advanced threat detection system provided by the embodiment of the present application shows the resource occupation of the system under different detection levels, including memory and CPU usage. By double coordinate axes representing the usage of memory and CPU, it is convenient to compare the resource consumption of different levels;

[0069] Figure 5 The false positive rate change curve diagram of the fusion artificial intelligence industrial control network security advanced threat detection system provided by the embodiment of the present application shows the change trend of the false positive rate with the continuous iteration and optimization of the model. The logarithmic coordinate axis shows the exponential decay trend of the false positive rate, indicating that the false positive rate is significantly reduced after multiple iterations of the model;

[0070] Figure 6 The attack blocking success rate change curve diagram of the fusion artificial intelligence industrial control network security advanced threat detection system provided by the embodiment of the present application shows the attack blocking success rate of the system at different time points. The sine wave simulates the change trend of the blocking success rate with time, reflecting the defense capability of the system;

[0071] Figure 7 The compliance audit data integrity change curve diagram of the fusion artificial intelligence industrial control network security advanced threat detection system provided by the embodiment of the present application shows the change of the data integrity of the security evaluation report generated by the compliance audit module with time. The sine wave simulates the change trend of the data integrity with time, reflecting the compliance and data reliability of the system. DETAILED DESCRIPTION

[0072] The technical solutions of the present application will be further illustrated by specific embodiments in combination with the accompanying drawings.

[0073] Among them, the drawings are only used for example explanation, and the representation is only a schematic diagram, not a physical diagram, and cannot be understood as a limitation of the present patent; in order to better illustrate the embodiments of the present application, some components of the drawings will 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 known structures and their descriptions in the drawings can be omitted.

[0074] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it should be understood that if the terms "upper", "lower", "left", "right", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the positional relationship in the drawings are only used for exemplary illustration, and cannot be understood as a limitation on the present patent, for those of ordinary skill in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0075] In the description of the present application, unless otherwise explicitly specified and limited, if the terms "connection" and the like appear to indicate the connection relationship between components, the term should be broadly understood, for example, it can be a fixed connection, or a detachable connection, or an integral; it can be a mechanical connection, or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium; it can be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0076] The fusion artificial intelligence industrial control network security advanced threat detection system provided by the embodiment, as shown in Figures 1-7 , wherein: Figure 2 is a detection accuracy over time curve graph, which shows the detection accuracy of the system at different time points, and simulates the change trend of the detection accuracy over time by a sine wave, reflecting the stability of the system performance; Figure 3 is a response time over load curve graph, which shows the response time of the system under different loads, and simulates the change trend of the response time with the increase of the load by an S-shaped curve, embodying the processing capacity and efficiency of the system; Figure 4 is a resource occupation over detection level curve graph; the graph shows the resource occupation of the system under different detection levels, including memory and CPU usage, and the usage of memory and CPU is represented by double coordinate axes respectively, which is convenient for comparing the resource consumption of different levels; Figure 5 is a false positive rate over model iteration number curve graph, which shows the change trend of the false positive rate with the continuous iteration and optimization of the model, and the logarithmic coordinate axis shows the exponential decay trend of the false positive rate, indicating that the false positive rate is significantly reduced after the model is iterated for many times; Figure 6 is an attack blocking success rate over time curve graph, which shows the attack blocking success rate of the system at different time points, and simulates the change trend of the blocking success rate over time by a sine wave, reflecting the defense capability of the system; Figure 7For compliance audit data integrity over time graph, the graph shows the data integrity of the security assessment report generated by the compliance audit module over time, and the trend of data integrity over time is simulated by a sine wave, reflecting the compliance and data reliability of the system.

[0077] The fusion artificial intelligence industrial control network security advanced threat detection 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 sequentially interact with each other.

[0078] The multi-source data acquisition module constructs an industrial protocol fingerprint library, and collects industrial control network flow data, device operation logs, industrial protocol messages and device state information in real time.

[0079] The intelligent analysis engine establishes a device behavior baseline model to realize multi-dimensional threat correlation analysis.

[0080] The threat detection module adopts a three-level detection architecture for threat verification.

[0081] The dynamic defense module constructs an attack chain blocking closed loop.

[0082] The self-evolution learning system realizes continuous optimization of the model through adversarial training.

[0083] The fusion artificial intelligence industrial control network security advanced threat detection system adopting the above technical solution constructs a multi-level cooperative defense system, realizes whole-cycle protection of industrial control network threats through the whole-link closed loop of data acquisition, intelligent analysis, dynamic detection, active defense and model evolution, and has the integration capability of heterogeneous data sources, realizes cross-module collaborative decision-making through dynamic knowledge graph, breaks through the limitation of single detection of traditional industrial control security system, and forms active defense capability covering threat identification, blocking and tracing.

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

[0085] The protocol adaptive analysis unit adopts a bidirectional LSTM+CNN hybrid network to dynamically identify Modbus protocol, S7 protocol, OPC UA protocol, PROFINET protocol, EtherNet / IP protocol, HART protocol and Foundation Fieldbus FF protocol, and constructs a three-dimensional protocol fingerprint library containing message length distribution and field entropy value.

[0086] The lightweight host probe unit runs in memory resident mode, encrypts CPU load and process behavior data through the SM4 algorithm, and the probe memory occupancy is ≤50MB.

[0087] The log aggregation unit adopts a sliding window mechanism to align heterogeneous log time series of the firewall and the IDS, and the time alignment accuracy reaches the millisecond level.

[0088] The multi-source data acquisition module adopting the technical solution is used for realizing efficient acquisition and standardized processing of all-factor data of the industrial control network, and wherein:

[0089] Protocol adaptive analysis: supporting deep analysis of multiple types of industrial protocols, accurately extracting protocol features and context information, and solving the problem of insufficient accuracy of traditional protocol identification.

[0090] Lightweight host probe: through the memory resident technology, the resource occupation is reduced, and the real-time acquisition and safe transmission of host behavior data are guaranteed.

[0091] Log aggregation alignment: eliminating the time deviation of multi-source logs, and providing a complete data basis for subsequent threat correlation analysis.

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

[0093] The dynamic graph construction unit encodes device attributes and communication relationships into a timestamped graph structure, and the node features include device types, historical behavior patterns and real-time state vectors;

[0094] The time series prediction unit predicts device behavior trends using an LSTM network, identifies second-level anomalies by combining a sliding window, and significantly reduces prediction error rates.

[0095] The threat propagation analysis unit calculates the threat propagation probability between nodes through a graph attention mechanism GAT, significantly improves the prediction accuracy of propagation paths, and specifically uses a dynamic threat propagation weight equation:

[0096]

[0097] Among them:

[0098] is a time series attention coefficient, which dynamically adjusts the historical behavior decay factor through Softmax(MLP(Δt));

[0099] β is an abnormal amplification coefficient, which triggers enhancement when protocol function code abnormalities are detected (baseline value 1.5, attack scene increased to 2.0);

[0100] γ m is a multi-modal feature weight, which dynamically allocates protocol / traffic / state feature weights based on Word2Vec semantic similarity;

[0101] is the threat propagation probability from node i to j at time t, with value range [0, 1], dynamic threat propagation strength;

[0102] W (t) is the dynamic propagation weight matrix, with value 0, device inter-topology relationship influence factor;

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

[0104] is the Sigmoid activation function, probability normalization.

[0105] For example, scenario: detecting abnormal data tampering attack propagation of PLC device (node A) to sensor (node B) in Modbus / TCP network

[0106] Calculation process:

[0107] 1. Feature extraction: obtain real-time register value (h A ) of node A and historical response mode (h B ) of node B

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

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

[0110] 4. Abnormal amplification: when detecting S7 protocol function code anomaly, β = 2.0 (baseline value 1.5);

[0111] 5. Multi-modal fusion: γ1 = 1.2 (protocol features), γ2 = 0.9 (traffic features), γ3 = 1.1 (state features);

[0112] 6. Propagation probability: output Trigger high-level threat alarm.

[0113] Technical effects

[0114] 1. Dynamic adaptability: automatically adjust the influence weight of historical behavior through time attention coefficient αk, compared with traditional fixed decay factor (such as exponential decay), the response speed to sudden attack is greatly improved;

[0115] 2. Attack feature enhancement: abnormal amplification coefficient β makes attack features significantly increase in propagation calculation, greatly reducing the false positive rate;

[0116] 3. Multi-modal fusion: dynamic weights complement protocol / traffic / state features, significantly improving covert attack chain identification rate;

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

[0118] Comparison with existing technologies:

[0119]

[0120]

[0121] Implementation verification

[0122] Deployment test in a certain refinery DCS system (Q3 2024 data):

[0123] Attack scenario: attacker initiates "coil reset" attack by forging Modbus slave address

[0124] System response:

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

[0126] 00:03:23 triggers SDN traffic isolation (block delay 18.7ms);

[0127] 00:03:25 starts digital twin deduction to verify isolation effectiveness;

[0128] Results: attack impact is limited to a single area, and production line downtime is avoided.

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

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

[0131] Dynamic graph neural network: captures dynamic features of device interaction through time series graph modeling, identifies hidden lateral penetration behavior.

[0132] LSTM-Attention hybrid model: combines long short-term memory and attention mechanism, enhances the correlation analysis capability for complex attack patterns.

[0133] Threat propagation analysis: quantifies the threat diffusion probability between nodes, realizes rapid positioning from single-point anomaly to global threat.

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

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

[0136] An unsupervised clustering middle-level detection unit, which uses an improved DBSCAN algorithm to cluster device behaviors, with an abnormality detection recall rate ≥ 92%;

[0137] The specific scheme of the improved DBSCAN algorithm is as follows:

[0138] 1. Adaptive density parameter optimization

[0139] Dynamic ε value calculation: based on local density estimation (k-distance graph method) to automatically determine the ε value, solving the problem of sensitivity to global parameters of traditional DBSCAN. By analyzing the distribution inflection point of k-neighbor distance of data points, dynamically adjusting the neighborhood radius, adapting to different density regions.

[0140] Mixed density clustering: introduce the hierarchical clustering idea of OPTICS algorithm, construct the reachability graph, identify the boundary of different density clusters, and solve the clustering problem of mixed traffic (normal / abnormal) in industrial control network.

[0141] 2. Efficient neighbor query acceleration

[0142] KD-Tree index structure: construct KD-Tree spatial index, reduce the computational complexity of Euclidean distance from O(n 2 ) to O(nlogn), accelerate the neighborhood search process. For the timing characteristics of industrial control protocol packets, optimize the segmentation strategy in the time dimension.

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

[0144] 3. High-dimensional data dimensionality reduction processing

[0145] Feature selection and compression: apply principal component analysis (PCA) and local preserving projection (LPP) to reduce the dimensionality of industrial control protocol features (such as packet length, field entropy value), while retaining more than 90% of the information content and reducing the computational load.

[0146] Incremental clustering update: for the time sequence of industrial control network traffic, design an incremental DBSCAN algorithm, which only expands the neighborhood of newly arrived data points, avoiding full data recalculation.

[0147] The knowledge graph reasoning unit analyzes the device dependency relationship through a graph neural network (GNN) and identifies APT attacks in combination with STIX2.0 format threat intelligence.

[0148] The multi-level threat verification mechanism is constructed by using the above technical solutions.

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

[0150] Unsupervised clustering detection: discover unknown abnormal behavior and break through the limitations of rule library coverage.

[0151] Knowledge graph reasoning: combine threat intelligence and device dependency relationships to identify hidden links of APT attacks.

[0152] Specifically, in the present embodiment, the dynamic defense module comprises:

[0153] Digital twin verification unit: builds a virtualized environment to simulate attack impact and supports dynamic attack path deduction.

[0154] SDN controller-driven attack chain blocking unit: based on the OpenFlow protocol, it realizes traffic redirection and blocks with a delay of ≤20 ms.

[0155] Automatic repair unit: according to the device image backup, it quickly recovers the production environment, and the repair process is recorded to the blockchain for storage.

[0156] By using the above technical solutions, the rapid verification and closed-loop blocking of attack impact are realized.

[0157] Digital twin verification: simulate attack consequences in a virtual environment to avoid the impact of defense strategy misoperation on production systems.

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

[0159] Automatic repair: combined with backup images and blockchain storage, it ensures the traceability and data integrity of the system recovery process.

[0160] Specifically, in the present embodiment, the self-evolution learning system comprises:

[0161] Adversarial training mechanism: uses a GAN generator to simulate new attack traffic, and the detection model robustness is improved by 40%.

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

[0163] Feedback correction unit: adjusts the model confidence threshold according to the artificial review results, and the false positive rate is reduced to below 0.5%.

[0164] With the above technical solution, the model construction has continuous optimization capability:

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

[0166] Edge incremental learning: support dynamic updating of local model parameters to adapt to rapid changes in industrial control environment.

[0167] Feedback correction mechanism: combine artificial review results to optimize model confidence and reduce misjudgment risk.

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

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

[0170] Long-term prediction captures device state cross-device dependency through graph embedding technology, with attack path prediction accuracy ≥85%.

[0171] With the above technical solution, accurate prediction of device behavior and attack path is achieved:

[0172] Short-term cycle detection: capture communication cycle abnormal fluctuations and identify low-frequency attacks such as protocol tampering.

[0173] Long-term dependency analysis: model cross-device state association through graph embedding technology to predict potential attack chain evolution direction.

[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 realize fast comparison of protocol features, matching speed ≥10Gbps;

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

[0177] Abnormal protocol detection layer, identifies disguised protocols through Isolation Forest algorithm, malicious traffic detection rate ≥93%.

[0178] With the above technical solution, the depth and adaptability of industrial protocol parsing are improved:

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

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

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

[0182] The improved SimHash algorithm has the following specific scheme:

[0183] Semantic-level feature weight optimization

[0184] TF-IDF enhancement strategy: Combine part-of-speech features (noun / verb weight increase by 30%) and word length features (double the weight of words with length 4-8) to optimize feature word importance evaluation. For example, the function code field in industrial control protocols (such as Modbus's "03" read holding register) is given a higher weight.

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

[0186] 2. Hash collision suppression technology

[0187] Multi-hash function combination: Use MurmurHash3 and CityHash double hash functions to generate signatures in parallel, merge the results through XOR operation, and reduce the hash collision rate from 2% in traditional methods to below 0.3%.

[0188] Block-wise secondary hashing: Process long text (such as industrial logs) in blocks, generate SimHash values for each block independently, and merge the results through a Bloom filter to solve the problem of feature loss in long text.

[0189] 3. Dynamic similarity calculation optimization

[0190] Hamming distance weighting: Assign Hamming distance weights based on feature word importance, with a 50% increase in the difference weight of core protocol fields (such as S7 protocol operation codes) to enhance the recognition sensitivity of key attack features.

[0191] Cosine similarity fusion: Convert the SimHash binary vector to a TF-IDF vector and calculate the cosine similarity as an auxiliary index to solve the problem of pure Hamming distance insensitivity to short text.

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

[0193] The multi-level detection strategy shortens the response time of the rule engine to 50ms, and the APT recognition accuracy of the advanced detection module reaches 92.3%;

[0194] Lightweight model deployment makes the memory usage of the detection model 1 / 5 of the traditional scheme, and adapts to ARM architecture industrial control devices;

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

[0196] The above technical solutions optimize system performance and compliance:

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

[0198] Lightweight deployment: adapt to the resource limitations of edge computing devices to reduce deployment costs;

[0199] Compliance audit: automatically generate standardized security reports to meet industry regulatory requirements.

[0200] Specifically, in the present embodiment, it includes:

[0201] Visual decision interface displays threat heat map and attack propagation path through three-dimensional topology map, supports multi-dimensional data drilling;

[0202] Dynamic knowledge graph stores device topology relationship and historical attack patterns, supports 5-step or more attack chain deduction;

[0203] Configurable policy engine provides detection sensitivity grading adjustment based on business priority, supports 10-level sensitivity threshold setting.

[0204] The above technical solutions enhance threat situation awareness and defense flexibility:

[0205] Three-dimensional topology visualization: intuitively display threat distribution and propagation path to assist quick decision-making;

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

[0207] Policy grading adjustment: dynamically adjust detection sensitivity according to business priority to balance security and business continuity requirements.

[0208] In summary:

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

[0210] Dynamic adaptive capability: through digital twin verification, adversarial training and edge learning, realize dynamic optimization and adaptive adjustment of defense strategy.

[0211] In-depth defense system: combined with protocol deep analysis, attack path prediction and visual decision-making, build multi-dimensional protection from network layer to application layer.

[0212] Balancing compliance and efficiency: lightweight deployment and compliance audits ensure system practicality and legality in complex industrial environments.

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

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

[0215] 1. Full-cycle threat protection capability

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

[0217] 2. Industrial protocol deep analysis and adaptive ability

[0218] In view of the multi-protocol heterogeneity of industrial control networks, a protocol fingerprint library and a three-level analysis mechanism are constructed to support dynamic identification and semantic-level analysis of mainstream industrial protocols such as Modbus, S7, OPC UA, etc. Through lightweight host probes and memory resident technology, real-time collection and secure transmission of host behavior data are realized, solving the problem of high resource occupation 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, device behavior baselines and context awareness capabilities are established. Through time series prediction and graph attention mechanism, dynamic features and threat propagation paths of device interactions are captured, realizing rapid positioning from single-point anomaly to global threat, and enhancing the recognition ability of horizontal penetration and complex attack patterns.

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

[0222] A three-level detection architecture combining rule engine, unsupervised clustering, and knowledge graph reasoning is adopted to balance detection speed and accuracy. Through digital twin verification and SDN traffic blocking technology, the effectiveness of defense strategies is verified in a virtual environment, combined with blockchain storage to realize attack tracing and system rapid recovery, forming an attack chain blocking closed loop.

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

[0224] Based on the mechanism of adversarial training and edge incremental learning, simulate new attack features and dynamically update model parameters to improve the robustness to unknown threats. Through the feedback correction unit combined with artificial review results to optimize the detection threshold, reduce the false positive rate, and realize the continuous iteration of model performance.

[0225] 6. Lightweight deployment and compliance adaptability

[0226] Adopt lightweight model compression technology and ARM architecture adaptation scheme to reduce system resource occupation and meet the power limit of industrial control equipment. Through the automatic compliance audit module to generate standardized security reports to ensure compliance with industry standards such as IEC 62443, and solve the deployment problems of traditional solutions in complex industrial environments.

[0227] Workflow

[0228] 1. Data collection and standardization

[0229] The multi-source data collection module acquires network traffic, device logs, protocol messages and state information in real time, dynamically identifies industrial protocols through the protocol adaptive analysis unit, and constructs a three-dimensional protocol fingerprint library.

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

[0231] 2. Intelligent analysis and behavior modeling

[0232] The intelligent analysis engine constructs a device relationship graph through a dynamic graph neural network, encoding device attributes, communication patterns and real-time state features.

[0233] The LSTM-Attention hybrid model fuses time series features and context information to predict device behavior trends and quantify threat propagation probabilities, supporting multi-dimensional threat correlation analysis.

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

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

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

[0237] 4. Model evolution and strategy optimization

[0238] The self-evolution learning system generates an adversarial traffic optimization detection model through a GAN, an edge incremental learning framework supports dynamic updating of a local model, and a feedback correction mechanism adjusts detection sensitivity in combination with artificial review results.

[0239] The system has the following breakthroughs:

[0240] Protocol deep analysis: dynamically extract protocol features through a bidirectional LSTM+CNN hybrid network, and realize accurate identification of protocol variants and malicious traffic in combination with a three-level analysis mechanism.

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

[0242] Active defense closed loop: digital twin verification and SDN dynamic blocking technology realize attack impact simulation and accurate defense, and blockchain storage ensures traceability of the repair process, breaking through the limitations of traditional solutions "detection-defense".

[0243] Self-evolution model architecture: the system has continuous evolution capability through adversarial training and edge incremental learning mechanism, which reduces resource occupation and improves response speed to unknown threats.

[0244] Summary

[0245] The system solves the core problems of protocol analysis, threat correlation, defense collaboration, and model adaptability in the industrial control network security field through multi-source data fusion, dynamic knowledge graph, and lightweight model design, and builds a full-stack protection system covering "perception-analysis-decision-response-evolution". The improvement of industrial protocol deep analysis capability, the breakthrough of dynamic threat correlation analysis, the enhancement of defense strategy agility, and the feasibility of model continuous optimization provide a systematic solution for advanced threat defense in complex industrial control environments.

[0246] The above is only the preferred embodiment of the present application, and does not limit the implementation and protection scope of the present application. For those skilled in the art, it should be realized that any equivalent replacement and obvious change made by applying the contents of the present application specification and drawings should be included in the protection scope of the present application.

Claims

1. An industrial control network security advanced threat detection system integrated with artificial intelligence, characterized in that, Comprise: The multi-source data acquisition module, intelligent analysis engine, threat detection module, dynamic defense module and self-evolution learning system are sequentially carried out data interaction; the industrial control network security advanced threat detection system fusing artificial intelligence realizes collaborative decision of each module through dynamic knowledge graph, wherein: The multi-source data acquisition module constructs an industrial protocol fingerprint library, and collects industrial control network flow data, device operation logs, industrial protocol messages and device state information in real time; The intelligent analysis engine establishes a device behavior baseline model, realizes multi-dimensional threat correlation analysis, and integrates a dynamic graph neural network DGNN and an LSTM-Attention hybrid model, specifically comprising: A dynamic graph construction unit encodes device attributes and communication relationships into a time-stamped graph structure, and node features include device types, historical behavior patterns and real-time state vectors; A time series prediction unit predicts device behavior trends using an LSTM network and identifies second-level anomalies by combining sliding window statistics; A threat propagation analysis unit calculates the threat propagation probability between nodes through a graph attention mechanism GAT; The threat detection module adopts a three-level detection architecture for threat verification, which comprises: A primary filtering unit based on a rule engine; An unsupervised clustering middle-level detection unit that uses an improved DBSCAN algorithm to cluster device behaviors; A knowledge graph reasoning unit that analyzes device dependency relationships through a graph neural network GNN and realizes APT attack identification in combination with STIX2.0 format threat intelligence; The dynamic defense module constructs an attack chain blocking closed loop, which comprises: A digital twin verification unit that constructs a virtualized environment to simulate attack impact and supports dynamic deduction of attack paths; An attack chain blocking unit driven by an SDN controller that realizes traffic redirection based on the OpenFlow protocol; An automated repair unit that quickly recovers the production environment according to device image backups, and records the repair process data to the blockchain for storage; The self-evolution learning system realizes continuous optimization of the model through adversarial training.

2. The fusion industrial control network security advanced threat detection system of artificial intelligence according to claim 1, wherein, The multi-source data acquisition module comprises: A protocol adaptive parsing unit that uses a bidirectional LSTM+CNN hybrid network to dynamically identify Modbus protocol, S7 protocol, OPCUA protocol, PROFINET protocol, EtherNet / IP protocol, HART protocol and Foundation Fieldbus FF protocol, and constructs a three-dimensional protocol fingerprint library containing message length distribution and field entropy value; A lightweight host probe unit that runs in memory resident mode and transmits CPU load and process behavior data through the SM4 algorithm for encryption; A log aggregation unit that aligns heterogeneous log time series of firewalls and IDSs using a sliding window mechanism. 3.The industrial control network security advanced threat detection system integrated with artificial intelligence according to claim 1, wherein, The self-evolution learning system comprises: An adversarial training mechanism that uses a GAN generator to simulate new attack traffic; An incremental learning framework driven by edge computing, which supports dynamic updating of local model parameters; A feedback correction unit that adjusts the model confidence threshold according to the manual review results.

4. The fusion industrial AI of cyber security advanced threat detection system according to claim 1, wherein, The time series prediction unit adopts a hybrid prediction strategy: DTW algorithm is used for short-term prediction to detect communication cycle anomalies; Long-term prediction captures the cross-device dependencies of device states through graph embedding techniques.

5. The fusion industrial AI of cyber security advanced threat detection system according to claim 1, wherein, The protocol adaptive parsing unit adopts a three-level parsing mechanism: Dynamic protocol matching layer, improve SimHash algorithm to realize fast comparison of protocol characteristics; Deep semantic parsing layer, apply attention mechanism CNN to extract key fields; Abnormal protocol detection layer, identify disguised protocols through isolation forest algorithm.

6. The industrial control network security advanced threat detection system converged with artificial intelligence of claim 1, wherein, Deploy the following mechanisms: Multi-level detection strategy shortens the response time of the rule engine to 50ms to improve the APT identification accuracy of the advanced detection module; Lightweight model deployment makes the memory occupancy of the detection model 1 / 5 of the traditional scheme, adapting to ARM architecture industrial control devices; Compliance audit module automatically generates a security assessment report that meets the IEC62443-3-3 standard.

7. The fusion industrial AI of cyber security advanced threat detection system according to claim 1, wherein, Includes: Visual decision interface displays threat heat map and attack propagation path through three-dimensional topology map, supports multi-dimensional data drilling; Dynamic knowledge graph stores device topology relationship and historical attack patterns, supports 5-step attack chain deduction; Configurable policy engine provides detection sensitivity grading adjustment based on business priority, supports 10-level sensitivity threshold setting.

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