Network security analysis early warning system based on artificial intelligence

Abstract

The invention discloses a network security analysis early warning system based on artificial intelligence, and the system comprises a data collection layer which captures full flow based on DPI, aggregates firewall logs, terminal behaviors and threat intelligence, and constructs a structured data pool; through TLS fingerprint identification of AI driving, the encrypted traffic is penetrated, and a sampling strategy is dynamically adjusted in combination with reinforcement learning. The intelligent analysis layer is used for carrying out cross validation on known threats and abnormal behaviors; the time sequence CNN extracts encrypted traffic features, and a novel threat detector is rapidly generated by using historical attack fragments in combination with a meta-learning framework; sHAP value driving dynamic feature selection and optimization feature vector input; the decision-making early warning layer is used for fusing multi-source features through a Bayesian network and generating 0-100 score risk scores; a self-adaptive threshold module is combined to adjust a score threshold in real time, and a high-risk event is pushed; the collaborative response layer is used for triggering a preset decision tree, deploying a GAN dynamic honeypot to trap an attacker and reversely tracing; the Neo4j visually restores the attack path, and blocking is executed after the threat is confirmed by a progressive response mechanism.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of network security, in particular to an artificial intelligence-based network security analysis and early warning system. BACKGROUND

[0002] With the acceleration of digital transformation, the dependence of enterprise business on the network is deepening, and the network attack surface is continuously expanding. Attackers are increasingly professional and covert, from zero-day exploit to supply chain poisoning, from APT long-term lurking to ransomware outbreak, and attack events are frequent and the destructive power is significantly enhanced. Under this background, the network security analysis and early warning system emerges as the times require and becomes the core infrastructure for organizations to build flexible security capabilities.

[0003] However, with the widespread application of the network security analysis and early warning system, the problem of high false positive and false negative rates is increasingly prominent. For example, a certain financial institution once deployed a traditional security information and event management system, and the rule engine of the system was based on fixed signatures. One day, the system triggered the "brute force cracking" rule due to the pressure test conducted by the operation and maintenance team, generating 23,000 alarms, 99.7% of which were false positives. More seriously, the system once misjudged the compliance SSL certificate rotation operation as a man-in-the-middle attack, resulting in a 47-minute business interruption. Such false positives not only consume analysis resources, but also may trigger the "The Wolf Is Coming" effect, causing real threats to be ignored. Therefore, an artificial intelligence-based network security analysis and early warning system is proposed. SUMMARY

[0004] The purpose of the present application is to solve the problems existing in the prior art, and an artificial intelligence-based network security analysis and early warning system is proposed.

[0005] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: The artificial intelligence-based network security analysis and early warning system comprises: A data acquisition layer: full-dimensional perception is realized through multi-source heterogeneous data access, encrypted traffic analysis and user behavior portrait construction. First, full traffic data is captured based on DPI technology, and firewall, terminal log and OpenCTI threat intelligence are aggregated through Syslog / API interface to form a structured data pool. Second, AI-driven TLS fingerprint identification technology is used to penetrate encrypted traffic, combined with UEBA behavior sequence collected by the agent, to construct a "network-terminal-user" three-dimensional data matrix. Finally, the sampling strategy is dynamically adjusted through reinforcement learning to reduce the redundant packet capture rate during peak hours while ensuring key protocol coverage, providing a low-noise, high-fidelity original data base for upper-layer analysis; Intelligent analysis layer: Deep analysis of collected data through multi-modal AI engine; first, hybrid detection engine runs Bi-LSTM (captures known attack timing features), Isolation Forest (identifies outlier log behavior), and GNN (constructs attack graph) in parallel to cross-verify known threats and abnormal behavior; second, time-series CNN model performs lightweight feature extraction on encrypted traffic, combined with meta-learning framework of small sample learning module, to quickly generate new threat detector using historical attack fragments; finally, through SHAP value driven dynamic feature selection mechanism, low contribution degree features are automatically filtered, and the optimized multi-dimensional feature vector is input to the next layer; Decision warning layer: Accurate alarm through multi-dimensional risk quantification engine; first, based on ATT&CK framework, attack stages are dynamically weighted, combined with Bayesian network to fuse traffic features, log outlier degree, and threat intelligence matching degree to generate 0-100 risk score; second, false alarm filtering engine identifies batch scanning events through LSTM model and calls third-party API to verify IP credibility to filter low confidence alarms; finally, adaptive threshold module adjusts scoring threshold in real time according to network baseline to push high-risk events to response layer; Collaborative response layer: Intelligent orchestration for automated defense closed loop; first, SOAR platform triggers preset decision tree according to risk score to automatically execute blocking and isolation standard operations for events greater than 80 points; second, generate adversarial network (GAN) to deploy dynamic honeypot to lure attackers and trace back; meanwhile, Neo4j graph database visualizes attack path to assist manual research and judgment; finally, gradual response mechanism first starts mirror traffic analysis on high-risk targets, and then executes blocking after confirming threats to avoid business interruption.

[0006] The above technical solution further includes: Further, the AI-driven TLS fingerprint identification technology penetrates encrypted traffic, including the following steps: Feature capture: Capture full flow data through DPI technology, focusing on raw data packets in the TLS handshake phase; in the TLS handshake process, the client and server exchange key parameters such as TLS version (e.g. TLS 1.2 / 1.3), encryption suite (e.g. ECDHE-RSA-AES128-GCM-SHA256), extension list (e.g. SNI, ALPN), elliptic curve parameters (e.g. supported_groups); extract the above parameters from handshake data to generate TLS fingerprint (e.g. JA3 fingerprint); JA3 fingerprint forms a unique identifier by concatenating specific fields (TLS version, encryption suite, extensions, etc.) and calculating MD5 hash value; Train AI model: Train AI models (such as CNN, LSTM, or unsupervised learning algorithms) using historical traffic data to learn the differences in TLS fingerprints between legitimate and malicious traffic; train classification models using labeled malicious samples (such as Cobalt Strike communications) to identify known threat fingerprint patterns; use Isolation Forest algorithms to detect abnormal fingerprints that deviate from the normal baseline, discovering unknown threats or zero-day attacks; periodically update AI models to adapt to evolving encryption protocols (such as TLS 1.3) and attack methods, ensuring continuous and effective detection capabilities.

[0007] Threat detection: Compare real-time captured TLS fingerprints with known malicious fingerprint libraries (such as APT organizations and malware families) to quickly identify threats, combine contextual information (such as traffic time, source IP reputation) and threat intelligence to assess the abnormality of TLS fingerprints; for example: malware may use specific encryption suite combinations (such as fixed use of old TLS versions); APT attacks may hide communications by disguising as legitimate service TLS fingerprints; even if the traffic content is encrypted, AI models can infer potential threats by analyzing handshake phase metadata (such as cipher suite order, extension behavior) without decrypting data; Decision response: Based on the abnormality of TLS fingerprints and the matching degree of threat intelligence factors, generate a risk score (such as 0-100 points), trigger preset strategies according to the risk score: high-risk events (such as scores ≥ 80 points), automatically block malicious IPs, isolate infected terminals, and deploy dynamic honeypots to trap attackers; medium and low-risk events start mirror traffic analysis to further verify threats before executing blocking; use Neo4j graph database to display the association of TLS fingerprints, assisting security personnel in analyzing attack chains (such as the entire process from TLS handshake to data leakage); Adaptive adjustment: Feedback actual detection results (such as false positives and false negatives) to AI models to optimize feature selection and classification thresholds; during peak periods, dynamically adjust sampling strategies through reinforcement learning to prioritize capturing TLS handshake data for high-risk protocols, balancing performance and coverage.

[0008] Further, the dynamic adjustment of sampling strategies through reinforcement learning reduces the capture rate of redundant packets during peak periods, and key protocol coverage includes the following steps: Set goals: During peak hours (e.g. when traffic surges, DDoS attacks occur), full-flow collection may cause hardware resources (e.g. network card bandwidth, CPU) to be overloaded, affecting real-time analysis capabilities; a large amount of repeated or irrelevant traffic (e.g. video streams, bulk scanning packets) occupies storage and computing resources; simply reducing sampling to reduce load may result in the loss of key data for important protocols (e.g. HTTPS, DNS); By dynamically adjusting the sampling strategy through reinforcement learning (RL), the capture rate of redundant packets is reduced, and low-value traffic (e.g. video streams, scanning packets) is preferentially discarded; key protocol coverage is ensured to ensure complete collection of high-risk protocols (e.g. TLS 1.3, DNS queries); adaptive network load, dynamically adjust the sampling rate (e.g. from 100% to 10%-50%) according to real-time traffic characteristics; Design reinforcement learning model: Monitor multiple indicators in real time through DPI technology; traffic characteristics, packet size distribution, protocol type proportion (e.g. HTTP / HTTPS / DNS / SSH), source / destination IP entropy; load status, network card bandwidth utilization, CPU usage, memory occupancy; threat intelligence, currently active APT organizations, C2 domain names, vulnerability exploitation characteristics; Quantify environmental indicators as input states (State) of the RL model, for example: traffic load level, low (0-30%), medium (30%-70%), high (70%-100%); protocol risk score, based on historical attack data, assign risk weights to protocols such as TLS and DNS; hardware resource margin, CPU idle rate, memory remaining; Define adjustable sampling parameters (Action), for example: global sampling rate, from 100% to 50%, 30%, 10%; protocol-level sampling rate, maintain 100% sampling for TLS 1.3, reduce to 10% for HTTP video streams; traffic filtering rules, discard scanning packets with source IP entropy higher than the threshold.

[0009] Design a multi-objective reward mechanism to balance performance and detection needs; negative reward, when hardware resources are overloaded (e.g. CPU > 90%), deduct 1% for each excess; positive reward, when an attack (e.g. SQL injection) is successfully detected, reward +10 to +50 according to threat level; penalty, deduct -100 when key protocol misses result in false negatives; Execute dynamic sampling strategy: Through DPI and system monitoring tools, traffic characteristics and hardware load data are continuously collected, and the quantified state (such as traffic load level, protocol risk score) is input into the RL model; the RL model selects the optimal action (such as reducing the global sampling rate from 100% to 30%, while maintaining the TLS 1.3 sampling rate at 100%) according to the current state, implements the sampling strategy through traffic mirroring or shunting equipment, and dynamically discards redundant packets; monitor the hardware load after sampling (such as CPU reduced to 60%), the reduction ratio of redundant packets (such as from 70% to 20%); statistics attack detection rate (such as SQL injection detection rate remains above 95%), key protocol coverage (such as 100% of TLS 1.3 traffic is collected), and feedback data (such as reward value, state transition) are used for RL model training to optimize strategy selection; Fast packet header analysis is performed at the hardware level to avoid performance loss caused by full packet decoding; based on historical attack data, risk weights are assigned to common protocols (such as TLS = 100, HTTP = 70, DNS = 80); automatically adjust the filtering rules based on the network baseline (such as the source IP entropy threshold from 3.5 to 4.0 to filter more scan packets); when the RL model decision leads to key protocol missing sampling, automatically fallback to conservative strategy (such as global sampling rate no less than 10%).

[0010] Further, the hybrid detection engine runs Bi-LSTM, Isolation Forest and GNN in parallel to cross-verify known threats and abnormal behaviors, including the following steps: Firstly, the engine receives structured data from the data collection layer, which covers network traffic metadata, log records, and external threat intelligence; then, feature engineering is performed for different model characteristics, extracting time series features, statistical features, and graph structure features for Bi-LSTM to identify pre-defined attack patterns (such as SQL injection feature sequences), IsolationForest to detect outliers (such as abnormal external connection or operation baseline deviation), and GNN to construct entity relationship graphs to correlate multi-stage attacks (such as APT organization's horizontal movement path); in the parallel inference stage, Bi-LSTM uses a bidirectional LSTM network to capture temporal dependencies, IsolationForest uses unsupervised learning to label potential anomalies, and GNN aggregates node information based on graph attention mechanisms to identify attack chains; cross-validation is performed through logical association and dynamic weighted voting, for example, if Bi-LSTM detects SQL injection but IsolationForest does not find corresponding session anomalies, the alarm confidence will be reduced, and the global attack perspective provided by GNN can further verify whether fragmented events belong to part of APT attacks; finally, the engine integrates model output to generate risk scores, and continuously optimizes model parameters through a feedback loop, such as updating Bi-LSTM's attention weights with new attack fragments, adjusting IsolationForest's baseline threshold, or optimizing GNN's graph embedding algorithm.

[0011] Further, the meta-learning framework combined with the small sample learning module uses historical attack fragments to quickly generate new threat detectors, including the following steps: Construct a historical attack fragment knowledge base: From the structured data pool of the data collection layer, extract 200+ types of attack event features labeled in the past three years, including: Traffic level: special character sequence of SQL injection, periodic heartbeat packet interval of C2 communication; Log level: abnormal process startup chain after Webshell upload, credential spraying behavior in horizontal movement stage; Intelligence level: domain name generation algorithm (DGA) mode specific to APT organizations, compilation timestamp features of exploit code; store the features by attack type (such as web attacks, internal penetration) to form a meta-knowledge base containing 120,000+ historical attack fragments; Initialize the meta-learning task: Adopting an architecture compatible with Model-Agnostic Meta-Learning (MAML), a double-layer optimization target is designed. For each type of historical attack (such as SQL injection), a support set is constructed with 5 samples to train the basic detector; the gradient of shared parameters is calculated across attack types, enabling the model to learn meta-knowledge about "how to quickly learn new attack patterns"; during initialization, the feature extractor is pre-trained through self-supervised learning, enabling it to automatically identify sensitive operation codes (such as exec, system) and abnormal encoding (such as double URL encoding) in traffic; Fast adaptation: When an unknown threat is detected (such as abnormal external connection of an IoT device), the SNI field anomaly (such as sudden increase in random subdomain length) and payload entropy anomaly (such as encrypted shellcode features) in the TLS handshake phase are extracted from the 5 suspicious traffic captured from the honeypot; input the new sample into the meta-trained feature extractor to generate a 128-dimensional feature vector; load the pre-stored SQL injection detector parameters as the initial point, and perform 3 gradient updates with the new sample to generate a detector specifically for this IoT threat; through SHAP value analysis, automatically filter low-contribution features (such as excluding normal DNS query common fields) and retain key features (such as specific SNI prefixes); Deploy and verify dynamic detector: Deploy the generated detector to the intelligent analysis layer and run it in parallel with the Bi-LSTM and GNN models; if the new detector labels certain traffic as a threat, but GNN does not find an associated attack path, the confidence is reduced to observation mode; for high-risk detection results (such as matching encrypted traffic features of LockBit ransomware), first start mirror traffic analysis and automatically block after confirming the threat; real-time feedback of detection results to the meta-knowledge base, if the same type of attack is successfully intercepted for 3 consecutive times, the detector is included in the normal model library, achieving knowledge solidification.

[0012] Further, the ATT&CK framework is used to dynamically weight the attack phase, and a Bayesian network is used to combine traffic features, log outliers, and threat intelligence matching degrees to generate a risk score of 0-100, including the following steps: Dynamic weighting: Pre-set ATT&CK knowledge graph covering 14 attack stages (e.g. initial access, persistence, privilege escalation), each stage associated with typical techniques (e.g. T1195 - supply chain compromise, T1059 - command line interaction) and industry benchmark scores (e.g. initial access 20 points, lateral movement 50 points); when detecting attack behavior in real time, locate the current stage through the attack chain graph constructed by GNN, and call the dynamic weighting engine; if the internal network lateral movement (T1210) is detected, and the target system is a database server (identified by the SQL service port in the log), the weight of this stage is increased from the benchmark 50 points to 75 points; if the same attack chain has previously performed initial access (T1193), the weight of the subsequent stage (e.g. discovery stage T1083) is automatically increased by 30%, reflecting that the attacker has established a foothold; if the TTPs of LockBit ransomware (e.g. T1486 - data encryption) are matched, the highest weight of 90 points is directly assigned, triggering the emergency response process; Multi-source data feature fusion: Receive three types of feature vectors from the intelligent analysis layer: Traffic features: 200+ dimensional metadata analyzed by DPI (e.g. TLS certificate hash, HTTP request frequency anomaly degree); Log outliers: user entity behavior baseline deviation value calculated by UEBA model (e.g. 95% login outlier of a certain account at midnight); Threat intelligence matching degree: IoC matching results provided by OpenCTI platform (e.g. 88% similarity between domain name and APT29 organization C2 server); Build a probability dependency graph through Bayesian network; define the conditional probability table (CPT) between nodes, such as the triggering probability of the "TLS certificate hash anomaly" node increasing by 40% when the "malware communication" parent node is true; use variational inference algorithm to accelerate probability calculation, so that the fusion reasoning delay of a single flow is controlled within 8ms; Calculate risk score: After the Bayesian network outputs the posterior probability of each evidence node, combine the ATT&CK weighting results for weighted summation: Where A is the risk value, M is the ATT&CK stage weight, B is the evidence support degree, and C is the intelligence confidence; for example, a lateral movement behavior (stage weight 75) in detecting abnormal credential spraying (evidence support degree 0.9) and matching FIN7 organization TTPs (intelligence confidence 0.85), contributes a risk value of 75x0.9x0.85=57.375 points.

[0013] According to the network baseline dynamic adjustment score curve, the risk threshold is reduced from 70 to 60 in the early morning period to avoid false positives; the score rationality is verified by historical data backtracking, such as an attack with a score of 85 actually causing data leakage, then the weight coefficient of the corresponding TTPs is increased.

[0014] Further, the adaptive threshold module adjusts the scoring threshold in real time according to the network baseline, including the following steps: Network behavior baseline modeling: Collect traffic metadata (such as packet length distribution, protocol type proportion) in the past 30 days, log records (such as user login frequency, file access pattern), and threat intelligence matching results to construct a multi-dimensional feature space; use the Isolation Forest algorithm to identify normal behavior patterns and automatically filter outliers (such as sudden DDoS attack data) to generate a dynamic baseline range (such as a HTTP traffic baseline value of 500-800Mbps in a certain period); Real-time evaluation of network status: Collect current traffic characteristics (such as TCP / UDP ratio, new connection establishment rate), log outliers (such as an abnormal score of a certain account trying to access a rarely used server), and intelligence matching degree (such as the similarity between the current domain name and the malicious domain name library) every 5 minutes; calculate the deviation of real-time features from the baseline through a sliding window algorithm, for example: Traffic anomaly degree = (current HTTP traffic - baseline mean) / baseline standard deviation; Log outlier degree = Isolation Forest output abnormal score (0-1 interval); Intelligence risk value = number of matched malicious IoCs / total query times; Dynamic adjustment of threshold: Rule engine matching: trigger different adjustment rules according to network status combination, for example: Low-risk period (such as 2-5am): if traffic anomaly degree <1.5 and log outlier degree <0.3, then threshold is lowered to 60 (original 70) to avoid false negatives; High-risk event (such as detection of C2 communication characteristics): immediately increase the threshold to 90 to ensure that only high-confidence events trigger blocking; Holiday mode: according to historical data, predict traffic surge (such as e-commerce promotion), and raise the threshold to 80 in advance to prevent false positives; Use XGBoost model to predict the optimal threshold, input features include current deviation, business type (such as higher sensitivity during financial transaction period), and historical adjustment effect (such as false positive rate change after last threshold reduction); Verify adjustment effect: After the threshold is adjusted, the alarm quality within 10 minutes is continuously monitored, if the false positive rate rises (such as normal business traffic is misjudged), the rollback mechanism is triggered; daily adjustment effect report is generated, including threshold change times, false positive / false negative change rate and other indicators, feedback to baseline model for iterative optimization; provide visual interface to show threshold adjustment log, security personnel can manually override automatic adjustment results (such as lock threshold to 85 points during major activities).

[0015] Further, the generative adversarial network deploys a dynamic honeypot to lure attackers by false services and trace back, including the following steps: Generating false services: Collecting enterprise real service data (such as Web application API interface, SSH login interface, database protocol characteristics), constructing training data set, training generator by improved WGAN-GP algorithm, so that it can output false service characteristics similar to real service, including HTTP response header forgery, generating Server, X-Powered-By fields completely consistent with real website; SSL certificate confusion, generating false certificate issued by trusted CA (such as imitating Let's Encrypt certificate chain); protocol timing simulation, adjusting service response delay (such as 2 seconds waiting after SSH login failure) through reinforcement learning, matching real device behavior baseline; Intelligent deployment of honeypot: Adopting graph attention network (GAT) to construct virtual network topology, so that the honeypot nodes present the real subnet structure of the enterprise (such as 192.168.1.0 / 24 segment contains false domain controller server, financial system database); through NetFlow analysis real-time network traffic, deploy honeypot on the path most likely to be probed by the attacker, deploy false Web server honeypot after detecting Nmap scan (such as-sS half-open scan) on the next hop of the scanning path; find Cobalt Strike beacon communication characteristics (such as / admin / get.php?data=abnormal request), then generate false OA system honeypot containing vulnerabilities; Attacker behavior luring: When the attacker sends SYN packet to probe the port, the honeypot returns SYN-ACK immediately, and records the fingerprint information (such as TTL value, window size); if the attacker attempts SQL injection (such as'OR 1=1--), the honeypot generates false response containing vulnerabilities (such as You have an error in your SQL syntax) through Seq2Seq model to induce further attacks; when detecting Metasploit's exploit / multi / handler, the honeypot returns a fake credential verification success interface, and records the attacker's input of malicious payload; Attack tracing: Capture honeypot interaction traffic using zero-copy technology (such as PF_RING) to extract attacker real information; penetrate NAT through STUN protocol to obtain attacker public IP (such as 203.0.113.5); analyze TLS client fingerprint (such as Chrome 119 JA3S value 771,49161-49171-52393-52394) to associate attacker identity; record the complete attack chain from webshell upload (such as ant.php) to privilege escalation operation (such as CVE-2021-4034 exploit); Automatic response: When the attacker downloads fake data (such as fake_customer_data.xlsx), trigger the collaborative response layer to immediately block the attacker IP (through firewall ACL rules), isolate the affected honeypot node, and prevent escape; submit the attacker fingerprint (such as the compilation timestamp of the custom trojan 2025-06-29 08:30:00) to the OpenCTI platform, and update the global attacker portrait library.

[0016] The present application has the following beneficial effects: In the present application, a low-noise data base is constructed by DPI full-flow capture, AI-driven TLS fingerprint recognition and reinforcement learning dynamic sampling strategy, combined with a hybrid detection engine of the intelligent analysis layer, a lightweight feature extraction of time series CNN and a small sample meta-learning framework, for accurate detection of known threats and APT attacks; through ATT&CK dynamic weighting, Bayesian network multi-source feature fusion, false alarm filtering engine and adaptive threshold module, low confidence alarms are filtered and risk thresholds are adjusted in real time; finally, a dynamic honeypot is deployed using a generative adversarial network to capture and trace the source, Neo4j visualizes the attack chain, and through a progressive response mechanism, high-risk targets are first analyzed and then blocked, realizing a full-process closed loop from data noise reduction, intelligent detection, dynamic decision-making to response verification, effectively solving the problems of high false positive and false negative rates. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The system block diagram of the artificial intelligence network security analysis and early warning system proposed in the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0019] Please refer to Figure 1As shown, the present application is an artificial intelligence network security analysis and early warning system, which comprises: Data acquisition layer: full-dimensional perception is realized through multi-source heterogeneous data access, encrypted traffic analysis and user behavior portrait construction; first, full traffic data is captured based on DPI technology, and firewall, terminal log and OpenCTI threat intelligence are aggregated through Syslog / API interface to form a structured data pool; second, AI-driven TLS fingerprint identification technology is used to penetrate encrypted traffic, combined with UEBA behavior sequence collected by Agent, to construct a "network-terminal-user" three-dimensional data matrix; finally, through reinforcement learning, the sampling strategy is dynamically adjusted to reduce the redundant packet capture rate during peak hours, while ensuring key protocol coverage, providing low-noise and high-fidelity original data base for upper layer analysis; Intelligent analysis layer: the collected data is deeply analyzed through multi-modal AI engine; first, the hybrid detection engine runs Bi-LSTM (captures known attack timing characteristics), Isolation Forest (identifies outlier log behavior) and GNN (constructs attack graph) in parallel, and cross- validates known threats and abnormal behaviors; second, a time series CNN model performs lightweight feature extraction on encrypted traffic, combined with a meta-learning framework of a small sample learning module, to quickly generate a new threat detector using historical attack fragments; finally, through a SHAP value-driven dynamic feature selection mechanism, low-contribution features are automatically filtered, and the optimized multi-dimensional feature vector is input into the next layer; Decision and early warning layer: accurate alarm is realized through a multi-dimensional risk quantification engine; first, based on the ATT&CK framework, the attack stage is dynamically weighted, combined with the Bayesian network to fuse traffic features, log outlier degree and threat intelligence matching degree, to generate a risk score of 0-100; second, the false alarm filtering engine identifies batch scanning events through an LSTM model, and calls a third-party API to verify IP credibility to filter low-confidence alarms; finally, the adaptive threshold module adjusts the scoring threshold in real time according to the network baseline, and pushes high-risk events to the response layer; Collaborative response layer: automatic defense closed loop is realized through intelligent scheduling; first, the SOAR platform triggers a preset decision tree according to the risk score, and automatically executes blocking and isolation standard operations for events greater than 80 points; second, a generative adversarial network (GAN) is deployed to deploy a dynamic honeypot, which can trap attackers and trace back through false services; at the same time, a Neo4j graph database visualizes the attack path to assist manual research and judgment; finally, the progressive response mechanism first starts mirror traffic analysis on high-risk targets, and then executes blocking after confirming the threat, to avoid business interruption.

[0020] In one embodiment, the AI-driven TLS fingerprint identification technology for penetrating encrypted traffic comprises the following steps: Feature capture: Capture full traffic data through DPI technology, focus on raw packets during TLS handshake phase. During TLS handshake, client and server exchange key parameters such as TLS version (e.g. TLS 1.2 / 1.3), cipher suites (e.g. ECDHE-RSA-AES128-GCM-SHA256), extension lists (e.g. SNI, ALPN), elliptic curve parameters (e.g. supported_groups). Extract these parameters from handshake data to generate TLS fingerprints (e.g. JA3 fingerprints). JA3 fingerprints are formed by concatenating specific fields (TLS version, cipher suites, extensions, etc.) and calculating MD5 hash value to form a unique identifier. Train AI model: Train AI model (e.g. CNN, LSTM or unsupervised learning algorithm) using historical traffic data to learn differences in TLS fingerprints between legitimate and malicious traffic. Train classification model using labeled malicious samples (e.g. Cobalt Strike communications) to identify known threat fingerprint patterns. Use Isolation Forest algorithm to detect abnormal fingerprints deviating from normal baseline, discover unknown threats or zero-day attacks. Regularly update AI model to adapt to evolving encryption protocols (e.g. TLS 1.3) and attack methods, ensuring continuous and effective detection capabilities.

[0021] Threat detection: Compare real-time captured TLS fingerprints with known malicious fingerprint library (e.g. APT organizations, malware families) to quickly identify threats. Combine contextual information (e.g. traffic time, source IP reputation) and threat intelligence to assess the abnormality of TLS fingerprints. For example, malware may use specific cipher suite combinations (e.g. always use old version of TLS); APT attacks may hide communications by disguising as legitimate service TLS fingerprints; even if traffic content is encrypted, AI model can infer potential threats by analyzing handshake phase metadata (e.g. cipher suite order, extension behavior) without decrypting data; Decision response: Based on the abnormality of TLS fingerprints and threat intelligence matching factors, generate risk score (e.g. 0-100 points). Trigger preset strategies according to risk score: high-risk events (e.g. score ≥ 80 points), automatically block malicious IP, isolate infected terminals, deploy dynamic honeypot to trap attackers; medium and low-risk events start mirror traffic analysis to further verify threats before executing blocking; use Neo4j graph database to display TLS fingerprint association, assist security personnel in analyzing attack chain (e.g. from TLS handshake to data leakage); Adaptive adjustment: The actual detection results (such as false positives and missed cases) are fed back to the AI model to optimize feature selection and classification thresholds. During peak periods, reinforcement learning is used to dynamically adjust the sampling strategy, giving priority to capturing TLS handshake data for high-risk protocols, and balancing performance and coverage.

[0022] In one embodiment, the reinforcement learning dynamically adjusts the sampling strategy to reduce the capture rate of redundant packets during peak periods, and the key protocol coverage includes the following steps: Set goals: During peak periods (such as when traffic surges or DDoS attacks occur), full-flow collection may cause hardware resources (such as network card bandwidth and CPU) to be overloaded, affecting real-time analysis capabilities. Large amounts of repeated or irrelevant traffic (such as video streams and bulk scanning packets) occupy storage and computing resources. Simply reducing the sampling rate to reduce the load may result in the loss of important protocol (such as HTTPS and DNS) key data. Use reinforcement learning (RL) to dynamically adjust the sampling strategy, reduce the capture rate of redundant packets, and prioritize discarding low-value traffic (such as video streams and scanning packets). Ensure key protocol coverage and ensure complete collection of high-risk protocols (such as TLS 1.3 and DNS queries). Adapt to network load and dynamically adjust the sampling rate (such as from 100% to 10%-50%) based on real-time traffic characteristics. Design reinforcement learning model: Use DPI technology to monitor multiple indicators in real time, such as traffic characteristics, packet size distribution, protocol type proportion (such as HTTP / HTTPS / DNS / SSH), source / destination IP entropy, load status, network card bandwidth utilization, CPU usage, and memory occupancy. Threat intelligence, current active APT organizations, C2 domain names, and vulnerability exploitation characteristics. Quantify environmental indicators as input states (State) for the RL model, such as: traffic load level, low (0-30%), medium (30%-70%), and high (70%-100%); protocol risk score, based on historical attack data, giving risk weights to protocols such as TLS and DNS; hardware resource margin, CPU idle rate, and memory remaining. Define adjustable sampling parameters (Action), such as: global sampling rate, from 100% to 50%, 30%, and 10%; protocol-level sampling rate, keep TLS 1.3 at 100% sampling, and reduce HTTP video stream to 10%; traffic filtering rules, discard scanning packets with source IP entropy higher than the threshold.

[0023] Design a multi-objective reward mechanism to balance performance and detection needs; negative reward, when hardware resources are overloaded (such as CPU > 90%), deduct 1% reward for each 1% overloading; positive reward, when successful detection of attacks (such as SQL injection), reward +10 to +50 according to threat level; punishment item, deduct reward -100 when key protocol is missed and leads to false negatives; Perform dynamic sampling strategy: Collect traffic features and hardware load data continuously through DPI and system monitoring tools, input the quantified state (such as traffic load level, protocol risk score) into the RL model; the RL model selects the optimal action (such as reducing the global sampling rate from 100% to 30%, while keeping the TLS 1.3 sampling rate at 100%) according to the current state, implements the sampling strategy through traffic mirroring or shunting equipment, and dynamically discards redundant packets; monitor the hardware load after sampling (such as CPU reduced to 60%), the reduction ratio of redundant packets (such as from 70% to 20%); statistics attack detection rate (such as SQL injection detection rate remains above 95%), key protocol coverage rate (such as 100% of TLS 1.3 traffic is collected), and feedback data (such as reward value, state transition) for RL model training to optimize strategy selection; Perform fast packet header analysis at the hardware level to avoid performance loss caused by full packet decoding; based on historical attack data, assign risk weights to common protocols (such as TLS = 100, HTTP = 70, DNS = 80); automatically adjust filtering rules based on network baseline (such as source IP entropy threshold from 3.5 to 4.0 to filter more scan packets); when RL model decision leads to key protocol missing, automatically fallback to conservative strategy (such as global sampling rate no less than 10%).

[0024] In one embodiment, the hybrid detection engine runs Bi-LSTM, Isolation Forest and GNN in parallel, cross-verify known threats and abnormal behaviors, including the following steps: First, the engine receives structured data from the data acquisition layer, which includes network traffic metadata, log records, and external threat intelligence. Then, feature engineering is performed on different model characteristics to extract temporal features, statistical features, and graph structure features. These are used by Bi-LSTM to identify predefined attack patterns (such as SQL injection feature sequences), Isolation Forest to detect outliers (such as abnormal external connections or deviations from operational baselines), and GNN to construct entity relationship graphs to correlate multi-stage attacks (such as the lateral movement paths of APT groups). In the parallel inference stage, Bi-LSTM uses a bidirectional LSTM network to capture temporal dependencies, Isolation Forest uses unsupervised learning to label potential anomalies, and GNN aggregates node information based on a graph attention mechanism to identify attack chains. Cross-validation is performed through logical association and dynamic weighted voting. For example, if Bi-LSTM detects SQL injection but Isolation Forest does not, the cross-validation process will fail. If the Forest algorithm does not detect any corresponding session anomalies, the confidence level of the alarm will be reduced. The global attack perspective provided by the GNN can further verify whether the fragmented event is part of an APT attack. Finally, the engine synthesizes the model output to generate a risk score and continuously optimizes the model parameters through a feedback loop, such as updating the attention weights of the Bi-LSTM with new attack fragments, adjusting the baseline threshold of the Isolation Forest, or optimizing the graph embedding algorithm of the GNN.

[0025] In one embodiment, the meta-learning framework combining a few-shot learning module to rapidly generate novel threat detectors using historical attack fragments includes the following steps: Building a knowledge base of historical attack fragments: From the structured data pool of the data acquisition layer, features of over 200 types of attack events labeled over the past three years were extracted, including: Traffic-level issues: Special character sequences used in SQL injection, and periodic heartbeat intervals in C2 communication; Log-level analysis: Abnormal process startup chain after Webshell upload, credential spraying behavior during lateral movement; Intelligence level: APT groups' proprietary Domain Name Generation Algorithm (DGA) mode and the compilation timestamp characteristics of exploit code; these characteristics are classified and stored according to attack type (such as web attacks and internal network penetration), forming a meta-knowledge base containing more than 120,000 historical attack fragments; Initialize the meta-learning task: Adopting an architecture compatible with Model-Agnostic Meta-Learning (MAML), a double-layer optimization target is designed. For each type of historical attack (such as SQL injection), a support set is constructed with 5 samples to train the basic detector; the gradient of shared parameters is calculated across attack types, enabling the model to learn meta-knowledge about "how to quickly learn new attack patterns"; during initialization, the feature extractor is pre-trained through self-supervised learning, enabling it to automatically identify sensitive operation codes (such as exec, system) and abnormal encoding (such as double URL encoding) in traffic; Fast adaptation: When an unknown threat is detected (such as abnormal external connection of an IoT device), the SNI field anomaly (such as sudden increase in random subdomain length) and payload entropy anomaly (such as encrypted shellcode features) in the TLS handshake phase are extracted from the 5 suspicious traffic captured from the honeypot; input the new sample into the meta-trained feature extractor to generate a 128-dimensional feature vector; load the pre-stored SQL injection detector parameters as the initial point, and perform 3 gradient updates with the new sample to generate a detector specifically for this IoT threat; through SHAP value analysis, automatically filter low-contribution features (such as excluding normal DNS query common fields) and retain key features (such as specific SNI prefixes); Deploy and verify dynamic detector: Deploy the generated detector to the intelligent analysis layer and run it in parallel with the Bi-LSTM and GNN models; if the new detector labels certain traffic as a threat, but GNN does not find an associated attack path, the confidence is reduced to observation mode; for high-risk detection results (such as matching encrypted traffic features of LockBit ransomware), first start mirror traffic analysis and automatically block after confirming the threat; real-time feedback of detection results to the meta-knowledge base, if the same type of attack is successfully intercepted for 3 consecutive times, the detector is included in the normal model library, achieving knowledge solidification.

[0026] In one embodiment, the ATT&CK framework is used to dynamically weight attack stages, and a Bayesian network is used to combine traffic features, log outliers, and threat intelligence matching degrees to generate a risk score of 0-100, including the following steps: Dynamic weighting: Pre-set ATT&CK knowledge graph covering 14 attack stages (e.g. initial access, persistence, privilege escalation), each stage associated with typical techniques (e.g. T1195 - supply chain compromise, T1059 - command line interaction) and industry benchmark scores (e.g. initial access 20 points, lateral movement 50 points); when detecting attack behavior in real time, locate the current stage through the attack chain graph constructed by GNN, and call the dynamic weighting engine; if the internal network lateral movement (T1210) is detected, and the target system is a database server (identified by the SQL service port in the log), the weight of this stage is increased from the benchmark 50 points to 75 points; if the same attack chain has previously performed initial access (T1193), the weight of the subsequent stage (e.g. discovery stage T1083) is automatically increased by 30%, reflecting that the attacker has established a foothold; if the TTPs of LockBit ransomware (e.g. T1486 - data encryption) are matched, the highest weight of 90 points is directly assigned, triggering the emergency response process; Multi-source data feature fusion: Receive three types of feature vectors from the intelligent analysis layer: Traffic features: 200+ dimensional metadata analyzed by DPI (e.g. TLS certificate hash, HTTP request frequency anomaly degree); Log outliers: user entity behavior baseline deviation value calculated by UEBA model (e.g. 95% login outlier of a certain account at midnight); Threat intelligence matching degree: IoC matching results provided by OpenCTI platform (e.g. 88% similarity between domain name and APT29 organization C2 server); Build a probability dependency graph through Bayesian network; define the conditional probability table (CPT) between nodes, such as the triggering probability of the "TLS certificate hash anomaly" node increasing by 40% when the "malware communication" parent node is true; use variational inference algorithm to accelerate probability calculation, so that the fusion reasoning delay of a single flow is controlled within 8ms; Calculate risk score: After the Bayesian network outputs the posterior probability of each evidence node, combine the ATT&CK weighting results for weighted summation: Where A is the risk value, M is the ATT&CK stage weight, B is the evidence support degree, and C is the intelligence confidence; for example, a lateral movement behavior (stage weight 75) in detecting abnormal credential spraying (evidence support degree 0.9) and matching FIN7 organization TTPs (intelligence confidence 0.85), contributes a risk value of 75x0.9x0.85=57.375 points.

[0027] According to the network baseline dynamic adjustment score curve, the risk threshold is reduced from 70 to 60 in the early morning period to avoid false positives; the score rationality is verified by historical data backtracking, such as an attack with a score of 85 actually causing data leakage, then the weight coefficient of the corresponding TTPs is increased.

[0028] In one embodiment, the adaptive threshold module adjusts the scoring threshold in real time according to the network baseline, including the following steps: Network behavior baseline modeling: Collect traffic metadata (such as packet length distribution, protocol type proportion) in the past 30 days, log records (such as user login frequency, file access pattern), and threat intelligence matching results to construct a multi-dimensional feature space; use the Isolation Forest algorithm to identify normal behavior patterns and automatically filter outliers (such as sudden DDoS attack data) to generate a dynamic baseline range (such as a HTTP traffic baseline value of 500-800Mbps in a certain period); Real-time assessment of network status: Collect current traffic characteristics (such as TCP / UDP ratio, new connection establishment rate), log outliers (such as an abnormal score of a certain account attempting to access a rarely used server), and intelligence matching degree (such as the similarity between the current domain name and the malicious domain name library) every 5 minutes; calculate the deviation of real-time features from the baseline through a sliding window algorithm, for example: Traffic anomaly degree = (current HTTP traffic - baseline mean) / baseline standard deviation; Log outlier degree = Isolation Forest output abnormal score (0-1 interval); Intelligence risk value = number of matched malicious IoCs / total query times; Dynamic adjustment of threshold: Rule engine matching: trigger different adjustment rules according to network status combination, for example: Low-risk period (such as 2-5am): if traffic anomaly degree <1.5 and log outlier degree <0.3, then threshold is lowered to 60 (original 70) to avoid false negatives; High-risk event (such as detection of C2 communication characteristics): immediately increase threshold to 90 to ensure that only high-confidence events trigger blocking; Holiday mode: according to historical data, predict traffic surge (such as e-commerce promotion), and raise threshold to 80 in advance to prevent false positives; Use XGBoost model to predict the optimal threshold, input features include current deviation, business type (such as higher sensitivity during financial transaction period), and historical adjustment effect (such as false positive rate change after last threshold reduction); Verify adjustment effect: After the threshold is adjusted, the alarm quality within 10 minutes is continuously monitored. If the false positive rate rises (such as normal business traffic is misjudged), a rollback mechanism is triggered. An adjustment effect report is generated daily, including threshold variation times, false positive / false negative change rate, and other indicators, which are fed back to the baseline model for iterative optimization. A visual interface is provided to display the threshold adjustment log, and security personnel can manually override the automatic adjustment results (such as locking the threshold at 85 points during major events).

[0029] In one embodiment, the generative adversarial network deploys a dynamic honeypot to lure attackers through false services and trace back, including the following steps: Generating false services: Collecting enterprise real service data (such as Web application API interface, SSH login interface, database protocol features), building a training data set, and training the generator through an improved WGAN-GP algorithm to make it output false service features similar to real services, including HTTP response header forgery, generating Server, X-Powered-By fields consistent with real websites; SSL certificate confusion, generating false certificates issued by trusted CAs (such as simulating Let's Encrypt certificate chain); protocol timing simulation, adjusting service response delay through reinforcement learning (such as 2-second waiting after SSH login failure), matching real device behavior baseline; Intelligent deployment of honeypot: Using graph attention network (GAT) to build virtual network topology, making honeypot nodes present enterprise real subnet structure (such as 192.168.1.0 / 24 segment containing false domain controller server, financial system database); through NetFlow analysis real-time network traffic, deploying honeypot on the path most likely to be probed by attackers, deploying false Web server honeypot after detecting Nmap scan (such as -sS half-open scan) on the next hop of the scan path; finding Cobalt Strike beacon communication features (such as / admin / get.php?data=abnormal request), generating false OA system honeypot with vulnerabilities; Attacker behavior luring: When the attacker sends a SYN packet to probe the port, the honeypot immediately returns SYN-ACK and records the fingerprint information (such as TTL value, window size); if the attacker attempts SQL injection (such as'OR 1=1--), the honeypot generates a false response containing vulnerabilities (such as You have an error in your SQL syntax) through a Seq2Seq model to induce further attacks; when detecting Metasploit's exploit / multi / handler, the honeypot returns a fake credential verification success interface, recording the attacker's input of malicious payload; Attack tracing: Honeypot interaction traffic is captured using zero-copy technology (e.g., PF_RING) to extract attacker real information; NAT is penetrated through STUN protocol to obtain attacker public IP (e.g., 203.0.113.5); TLS client fingerprint (e.g., Chrome 119 JA3S value 771, 49161-49171-52393-52394) is analyzed to associate attacker identity; a complete attack chain is recorded from webshell upload (e.g., ant.php) to privilege escalation operation (e.g., CVE-2021-4034 exploit); Automated response: When the attacker downloads fake data (e.g., fake_customer_data.xlsx), a coordinated response layer is triggered to immediately block the attacker IP (through firewall ACL rules), isolate the affected honeypot node, and prevent escape; the attacker fingerprint (e.g., custom malware compilation timestamp 2025-06-29 08:30:00) is submitted to the OpenCTI platform to update the global attacker profile library.

[0030] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based network security analysis and early warning system, characterized in that, include: Data Acquisition Layer: Captures full traffic data based on DPI technology, and simultaneously aggregates firewall, endpoint logs, and OpenCTI threat intelligence through Syslog / API interfaces to form a structured data pool; Employs AI-driven TLS fingerprinting technology to penetrate encrypted traffic, and combines it with UEBA behavior sequences collected by the Agent to construct a three-dimensional data matrix; Dynamically adjusts sampling strategies through reinforcement learning to reduce redundant packet capture rate during peak periods, ensuring key protocol coverage, and providing a low-noise, high-fidelity raw data foundation for upper-layer analysis; Intelligent Analysis Layer: The hybrid detection engine runs Bi-LSTM, Isolation Forest, and GNN in parallel to perform cross-validation of known threats and anomalous behaviors; the temporal CNN model performs lightweight feature extraction on encrypted traffic, and combined with the meta-learning framework of the few-shot learning module, it quickly generates new threat detectors using historical attack fragments; finally, through the dynamic feature selection mechanism driven by SHAP value, low-contribution features are automatically filtered out, and the optimized multi-dimensional feature vector is input into the decision warning layer. Decision-making and early warning layer: Based on the ATT&CK framework, dynamic weighting is applied to the attack phase, and a risk score of 0-100 is generated by combining Bayesian network fusion traffic characteristics, log outlier rate and threat intelligence matching degree. The false alarm filtering engine identifies batch scanning events using an LSTM model and calls a third-party API to verify IP credibility, filtering low-confidence alarms. The adaptive threshold module adjusts the scoring threshold in real time based on the network baseline and pushes high-risk events to the collaborative response layer; Collaborative Response Layer: Based on the risk score, a preset decision tree is triggered to automatically execute standardized blocking and isolation operations for events with a score greater than 80; dynamic honeypots are deployed using adversarial networks to lure attackers through fake services and trace their origins; at the same time, the Neo4j graph database visualizes and reconstructs the attack path to assist manual analysis; a progressive response mechanism first initiates mirror traffic analysis for high-risk targets, and then executes blocking after confirming the threat.

2. The artificial intelligence-based network security analysis and early warning system according to claim 1, characterized in that, The AI-driven TLS fingerprinting technology for penetrating encrypted traffic includes the following steps: Feature capture: The DPI technology captures full traffic data, focusing on the raw data packets during the TLS handshake phase. During the TLS handshake, the client and server exchange key parameters, including the TLS version, cipher suite, extension list, and elliptic curve parameters. These parameters are extracted from the handshake data to generate a TLS fingerprint. The JA3 fingerprint is formed by concatenating specific fields and calculating the MD5 hash value to create a unique identifier. Training AI models: The AI ​​model is trained using historical traffic data to learn the differences in TLS fingerprints between legitimate and malicious traffic; a classification model is trained using labeled malicious samples to identify fingerprint patterns of known threats; an isolated forest algorithm is used to detect abnormal fingerprints that deviate from the normal baseline, thus discovering unknown threats or zero-day attacks; the AI ​​model is updated regularly to adapt to the evolution of encryption protocols and attack methods, ensuring that the detection capability remains effective. Threat detection: By comparing real-time captured TLS fingerprints with a known malicious fingerprint database, threats can be quickly identified. Combined with contextual information and threat intelligence, the degree of anomaly of the TLS fingerprint can be assessed. Malware may use specific combinations of encryption suites; APT attacks may hide communication by spoofing the TLS fingerprints of legitimate services; even if the traffic content is encrypted, the AI ​​model can still infer potential threats by analyzing the metadata of the handshake phase without decrypting the data. Decision Response: Based on the anomaly level of TLS fingerprints and threat intelligence matching factors, a risk score is generated. Pre-set strategies are triggered according to the risk score: for high-risk events, malicious IPs are automatically blocked, infected terminals are isolated, and dynamic honeypots are deployed to lure attackers; for medium- and low-risk events, mirror traffic analysis is initiated to further verify the threat before blocking is performed; the correlation of TLS fingerprints is displayed through the Neo4j graph database to assist security personnel in analyzing the attack chain. Adaptive adjustment: The actual detection results are fed back to the AI ​​model to optimize feature selection and classification thresholds; during peak periods, the sampling strategy is dynamically adjusted through reinforcement learning to prioritize the capture of TLS handshake data of high-risk protocols.

3. The artificial intelligence-based network security analysis and early warning system according to claim 1, characterized in that, The method of dynamically adjusting the sampling strategy through reinforcement learning to reduce the redundant packet capture rate and cover key protocols during peak periods includes the following steps: Set goals: During peak hours, full traffic collection may overload hardware resources and affect real-time analysis capabilities; a large amount of repetitive or irrelevant traffic will consume storage and computing resources; and simple downsampling to reduce load may result in the loss of critical data for important protocols. By dynamically adjusting the sampling strategy through reinforcement learning, the rate of redundant packet capture is reduced, and low-value traffic is discarded first; key protocol coverage is improved to enhance the complete collection of high-risk protocols; and adaptive network load is used to dynamically adjust the sampling rate based on real-time traffic characteristics. Design reinforcement learning models: Real-time monitoring of multiple indicators through DPI technology; traffic characteristics, including size distribution, protocol type ratio, source and destination IP entropy values; load status, network card bandwidth utilization, CPU utilization, and memory usage; threat intelligence, currently active APT groups, C2 domains, and vulnerability exploitation characteristics; The environmental indicators are quantified into the input states of the RL model, including low, medium, and high traffic load levels; protocol risk scoring, which assigns risk weights to TLS and DNS protocols based on historical attack data; and hardware resource availability, such as CPU idle rate and memory remaining amount. Define adjustable sampling parameters: global sampling rate, decreasing from 100% to 50%, 30%, and 10%; protocol-level sampling rate, maintaining 100% sampling for TLS 1.3 and decreasing to 10% for HTTP video streams; traffic filtering rules, discarding scan packets whose source IP entropy value is higher than the threshold. Design a multi-objective reward mechanism to balance performance and detection needs; negative reward: when hardware resources are overloaded, the reward is reduced for every 1% exceeding the limit; positive reward: when an attack is successfully detected, the reward is increased by 10 to 50 according to the threat level; penalty: when a critical protocol is missed and a false report is caused by the omission of detection, the reward of 100 is deducted. Implement a dynamic sampling strategy: Traffic characteristics and hardware load data are continuously collected through DPI and monitoring tools, and the quantified state is input into the RL model. The RL model selects the optimal action based on the current state, implements sampling strategies through traffic mirroring and diversion devices, and dynamically discards redundant packets. The hardware load and the reduction ratio of redundant packets after sampling are monitored. The attack detection rate and key protocol coverage are statistically analyzed, and the feedback data is used for RL model training to optimize strategy selection. At the hardware level, fast packet header parsing is performed to reduce the performance loss caused by full packet decoding; risk weights are assigned to common protocols based on historical attack data; filtering rules are automatically adjusted according to the network baseline, and when the RL model decision causes key protocols to be missed, it automatically falls back to a conservative strategy.

4. The artificial intelligence-based network security analysis and early warning system according to claim 1, characterized in that, The hybrid detection engine runs Bi-LSTM, Isolation Forest, and GNN in parallel to perform cross-validation of known threats and anomalous behaviors, including the following steps: First, the engine receives structured data from the data acquisition layer, which includes network traffic metadata, log records, and external threat intelligence. Then, feature engineering is performed on different model characteristics to extract temporal features, statistical features, and graph structure features, which are respectively used by Bi-LSTM to identify predefined attack patterns, Isolation Forest to detect outliers, and GNN to construct entity relationship graphs to correlate multi-stage attacks. During the parallel inference stage, Bi-LSTM uses a bidirectional LSTM network to capture temporal dependencies, Isolation Forest uses unsupervised learning to label potential anomalies, and GNN aggregates node information based on a graph attention mechanism to identify attack chains. Cross-validation is performed through logical association and dynamic weighted voting. If Bi-LSTM detects SQL injection but Isolation Forest does not find a corresponding session anomaly, the confidence level of the alarm is reduced, while the global attack perspective provided by GNN further verifies whether fragmented events are part of an APT attack. Finally, the engine synthesizes the model output to generate a risk score and continuously optimizes model parameters through a feedback loop, updating the attention weights of Bi-LSTM with new attack fragments, adjusting the baseline threshold of Isolation Forest, and optimizing the graph embedding algorithm of GNN.

5. The artificial intelligence-based network security analysis and early warning system according to claim 1, characterized in that, The meta-learning framework, which combines a few-shot learning module, rapidly generates novel threat detectors using historical attack fragments, including the following steps: Building a knowledge base of historical attack fragments: From the structured data pool of the data acquisition layer, features of numerous types of attack events labeled over the past three years were extracted, including: Traffic-level issues: Special character sequences used in SQL injection, and periodic heartbeat intervals in C2 communication; Log-level analysis: Abnormal process startup chain after Webshell upload, credential spraying behavior during lateral movement; Intelligence level: APT groups' proprietary domain name generation algorithm patterns and the compilation timestamp characteristics of exploit code; these characteristics are categorized and stored according to attack type to form a meta-knowledge base containing numerous historical attack fragments; Initialize the meta-learning task: Adopting an architecture compatible with Model-Agnostic Meta-Learning, a two-layer optimization objective is designed. For each type of historical attack, a support set is built using 5 samples to train the basic detector. Shared parameter gradients are calculated across attack types, enabling the model to learn meta-knowledge of "how to quickly learn new attack patterns". During initialization, a feature extractor is pre-trained through self-supervised learning, enabling it to automatically identify sensitive opcodes and anomalous codes in the traffic. Quick adaptation: When an unknown threat is detected, the system extracts anomalies in the SNI field and payload entropy value during the TLS handshake phase from five suspicious traffic streams captured by the honeypot. New samples are then input into a pre-trained feature extractor to generate a 128-dimensional feature vector. Pre-stored SQL injection detector parameters are loaded as the initial point, and the system performs three gradient updates using the new samples to generate a detector targeting the IoT threat. SHAP value analysis automatically filters out low-contribution features while retaining key features. Deployment and validation of dynamic detectors: The generated detector is deployed to the intelligent analysis layer and runs in parallel with the Bi-LSTM and GNN models. If the new detector marks a certain traffic as a threat, but the GNN does not find a related attack path, the confidence level is reduced to observation mode. For high-risk detection results, mirror traffic analysis is initiated first, and the threat is automatically blocked after confirmation. The detection results are fed back to the meta-knowledge base in real time. If the same type of attack is successfully blocked 3 times in a row, the detector is included in the regular model library for knowledge solidification.

6. The artificial intelligence-based network security analysis and early warning system according to claim 1, characterized in that, The method of dynamically assigning weights to the attack phase based on the ATT&CK framework, and generating a risk score of 0-100 by combining Bayesian network fusion traffic characteristics, log outlier rate, and threat intelligence matching degree, includes the following steps: Dynamic weighting: The system includes a pre-built ATT&CK knowledge graph covering 14 attack phases, with each phase associated with typical technologies and industry benchmark scores. When an attack is detected in real time, the attack chain graph constructed by the GNN is used to locate the current phase, and a dynamic weighting engine is invoked. If lateral movement within the internal network is detected and the target is a database server, the weight of that phase is increased from the benchmark score of 50 to 75. If the same attack chain has previously performed an initial access, the weight of subsequent phases is automatically increased by 30%, reflecting that the attacker has established a foothold. If a TTP of LockBit ransomware is matched, the highest weight of 90 is directly assigned, triggering the emergency response process. Multi-source data feature fusion: Three types of feature vectors are received from the intelligent analysis layer: Traffic characteristics: Multidimensional metadata parsed by DPI; Log outlier: The deviation of user entity behavior from the baseline calculated by the UEBA model; Threat intelligence matching score: IoC matching results provided by the OpenCTI platform; A probabilistic dependency graph is constructed using a Bayesian network; a conditional probability table between nodes is defined; and a variational inference algorithm is used to accelerate probability calculation, so that the fusion inference latency of a single traffic flow is controlled within 8ms. Calculate the risk score: After the Bayesian network outputs the posterior probability of each evidence node, it is then weighted and summed by combining the ATT&CK weighting results: Where A is the risk value, M is the ATT&CK stage weight, B is the evidence support level, and C is the intelligence confidence level; The scoring curve is dynamically adjusted based on the network baseline. During the early morning hours, the risk threshold is lowered from 70 to 60 to avoid false alarms. The rationality of the scoring is verified by backtracking through historical data. If an attack with a score of 85 actually causes a data breach, the weight coefficient of the corresponding TTPs is increased.

7. The artificial intelligence-based network security analysis and early warning system according to claim 1, characterized in that, The adaptive threshold module adjusts the scoring threshold in real time based on the network baseline, including the following steps: Network behavior baseline modeling: Collect traffic metadata, log records, and threat intelligence matching results from the past 30 days to construct a multi-dimensional feature space; use the isolated forest algorithm to identify normal behavior patterns, automatically filter out outliers, and generate a dynamic baseline range. Real-time network status assessment: Every 5 minutes, current traffic characteristics, log outlier rate, and intelligence matching degree are collected; the deviation between real-time characteristics and the baseline is calculated using a sliding window algorithm. Dynamically adjust threshold: Different adjustment rules are triggered based on the combination of network states: During low-risk periods, if the traffic anomaly score is less than 1.5 and the log outlier score is less than 0.3, the threshold is lowered to 60 points to avoid missing minor scans. For high-risk events, the threshold is immediately raised to 90 points so that only high-confidence events trigger blocking. During holidays, traffic surges are predicted based on historical data, and the threshold is raised to 80 points in advance to prevent false alarms. The optimal threshold is predicted using the XGBoost model, with input features including the current deviation, business type, and historical adjustment effects. Verify the adjustment effect: After the threshold is adjusted, the alarm quality is continuously monitored for 10 minutes. If the false alarm rate increases, the rollback mechanism is triggered. A daily adjustment effect report is generated, which includes indicators such as the number of threshold changes, false alarm rate, and missed alarm rate. This report is fed back to the baseline model for iterative optimization. A visual interface is provided to display the threshold adjustment log, and security personnel can manually overwrite the automatic adjustment results.

8. The artificial intelligence-based network security analysis and early warning system according to claim 1, characterized in that, The generative adversarial network (GAN) deploys dynamic honeypots to lure attackers with fake services and trace their origins, including the following steps: Generate fake services: Collect real service data from enterprises, build a training dataset, train a generator using an improved WGAN-GP algorithm, and output fake service features that are highly similar to real services, including HTTP response header forgery to generate Server and X-Powered-By fields that are completely identical to real websites; SSL certificate obfuscation to generate fake certificates issued by trusted CAs; and protocol timing simulation to adjust service response latency through reinforcement learning to match the behavior baseline of real devices. Intelligent Deployment of Honeypots: A virtual network topology is constructed using graph attention networks, making the honeypot nodes resemble the enterprise's real subnet structure; real-time network traffic is analyzed using NetFlow, and honeypots are deployed on the paths most likely to be probed by attackers. After detecting an Nmap scan, a fake web server honeypot is deployed at the next hop of the scan path; if Cobalt Strike beacon communication characteristics are discovered, a fake OA honeypot containing vulnerabilities is generated. Attacker behavior trapping: When an attacker sends a SYN packet to probe the port, the honeypot immediately returns SYN-ACK and records fingerprint information; if the attacker attempts SQL injection, the honeypot generates a fake response containing vulnerabilities using the Seq2Seq model to induce further attacks; when Metasploit's exploit / multi / handler is detected, the honeypot returns a forged credential verification success interface and records the malicious payload entered by the attacker. Attack Origin: Zero-copy technology is used to capture honeypot interaction traffic and extract the attacker's real information; the STUN protocol is used to penetrate NAT and obtain the attacker's public IP address; the TLS client fingerprint is analyzed to identify the attacker; and the complete attack chain from Webshell upload to privilege escalation operation is recorded. Automated response: When an attacker downloads fake data, the collaborative response layer is triggered to immediately block the attacker's IP, isolate the affected honeypot nodes, and prevent escape; the attacker's fingerprint is submitted to the OpenCTI platform to update the global attacker profile database.

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