Network Mapping Anomaly Detection for Path Drift and Protocol Camouflage
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Solution Overview
Problem
Existing network mapping behavior anomaly detection systems suffer from high false alarms, low adaptability, and incomplete situation coverage due to reliance on static rule bases and inability to detect path drift in multi-step attacks.
Innovation Solution
A network mapping behavior anomaly detection method using machine learning, which includes collecting dual-source traffic data, constructing anomaly detection models, performing subgraph matching, generating communication data with watermark identifiers, and updating the detection model based on verification results to enhance cognitive robustness and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static rule base is used for attack chain detection, then detection simplicity is maintained, but adaptability to new attack patterns deteriorates
Solution Approach 1:
The patent transforms the static rule base into a dynamic machine learning model that continuously learns from new attack patterns. The anomaly detection model is trained on historical attack data and automatically updates its detection capabilities, enabling the system to adapt to evolving attack methods without manual rule updates.
Solution Approach 2:
The patent replaces the mechanical rule-based detection system with a machine learning-based anomaly detection model. This substitution enables the system to process complex, multi-dimensional attack patterns and automatically identify anomalies without relying on pre-defined static rules.
2Reliability
If traditional threshold judgment is used for anomaly detection, then detection speed is maintained, but false alarm rate increases
Solution Approach 1:
The patent changes the detection parameters from simple threshold-based metrics to multi-dimensional behavior feature vectors that capture protocol interaction states, service access spatial distributions, and temporal patterns. This enables more accurate anomaly identification with reduced false alarms while maintaining detection efficiency through optimized feature extraction.
Solution Approach 2:
The patent combines multiple detection approaches into a composite anomaly detection system that integrates behavior pattern recognition, protocol state analysis, and spatial distribution features. This composite approach achieves high reliability by cross-validating multiple indicators while maintaining productivity through unified model inference.
3Adaptability or versatility
If comprehensive attack chain rules are implemented, then situation coverage is improved, but false alarm rate increases
Solution Approach 1:
The patent segments the attack chain detection into hierarchical levels: protocol interaction state transitions, service access spatial distributions, and overall attack pattern recognition. This segmentation allows the system to detect comprehensive attack scenarios while reducing false alarms by analyzing each segment independently and integrating results through the anomaly detection model.
Solution Approach 2:
The patent introduces behavior feature vectors as intermediaries between raw attack data and final detection results. These feature vectors serve as a mediator that transforms complex multi-dimensional attack patterns into standardized representations, enabling comprehensive situation coverage while maintaining reliable detection through consistent feature extraction and comparison.
4Adaptability or versatility
If machine learning model is used for anomaly detection, then adaptability to path drift is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-training the machine learning model on extensive attack data and pre-computing behavior feature extraction pipelines. This preliminary preparation enables the model to quickly adapt to new attack patterns during runtime without requiring complex real-time computations, thus achieving high cognitive robustness with manageable computational complexity.
Solution Approach 2:
The patent uses copying by creating simplified representations of attack patterns through behavior feature vectors and protocol state graphs. These copied representations capture essential attack characteristics without requiring full computational analysis of original attack data, enabling efficient anomaly detection with high adaptability to path drift.
Data Source
AI summary
A network mapping behavior anomaly detection method and system based on machine learning is provided. The method includes: collecting dual-source traffic data, generating a structured log data set through dual-source log fusion engine; performing subgraph matching calculation to obtain a mapping behavior deviation degree; generating communication data containing a watermark identifier in a session corresponding communication path; verifying whether attack events carry the watermark identifier; generating a network mapping behavior anomaly detection report. According to the disclosure, an adaptive attack behavior model is constructed through a multi-modal feature vector based on structured logs and a graph protocol mapping rule base, so that the cognitive robustness to protocol camouflage and path drift is fundamentally enhanced, a real-time verification chain of detection results is built, and traditional passive detection is transformed into self-proof active defense through cross verification of watermark carrying state and behavior trajectory.