Decision Tree Anomaly Explainer for Intrusion Detection Systems
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Solution Overview
Problem
Intrusion detection systems face challenges in providing explanations for detected network anomalies due to their complex algorithms and multi-layered processing, making it difficult to determine the causes of suspicious patterns in network traffic.
Innovation Solution
A security analysis system that generates explanations for network anomalies by identifying a minimal set of features distinguishing anomalies from normal traffic, creating mappings between anomalies and features, and deriving explanation rules without knowing the IDS's algorithms or rules, allowing for the generation of explanations for detected anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If intrusion detection systems use complex algorithms and multi-layered processing to detect anomalies, then detection capability is improved, but explanation capability deteriorates
Solution Approach 1:
The patent introduces decision trees as an intermediary component between the complex intrusion detection algorithms and the explanation generation process. The decision trees are trained on the output of the intrusion detection system to learn the mapping between detected anomalies and their underlying causes, thereby enabling explanation generation without requiring modification of the original detection algorithms.
Solution Approach 2:
The patent segments the explanation generation task into distinct components: (1) extracting features from network traffic data, (2) training decision trees on intrusion detection outputs, (3) generating explanations based on decision tree predictions. This segmentation allows the complex detection system to remain unchanged while the explanation system processes its output separately.
2Ease of operation
If intrusion detection systems evaluate individual features separately, then feature analysis simplicity is improved, but anomaly detection accuracy deteriorates
Solution Approach 1:
The patent combines multiple individual feature evaluations into comprehensive feature sets that are processed together through decision trees. By merging features into structured combinations and evaluating them collectively, the system maintains the simplicity of individual feature analysis while achieving accurate anomaly detection through their integrated assessment.
3Reliability
If intrusion detection systems use multiple processing layers with aggregation, then detection robustness is improved, but output interpretability deteriorates
Solution Approach 1:
The patent creates a simplified copy of the intrusion detection system's decision-making logic through decision trees. These trees replicate the essential detection patterns learned by the complex multi-layer system but present them in an interpretable format that shows clear decision paths and feature importance, making the output understandable without sacrificing detection robustness.
Data Source
AI summary
In an embodiment, the method comprises receiving an identification of an anomaly associated with a false positive identification of a security threat by the intrusion detection system, wherein a first set of feature data identifies features of the anomaly; creating a plurality of training sets each comprising identifications of a plurality of samples of network communications; for the anomaly and each training set of the plurality of training sets, training a decision tree that is stored in digital memory of the security analysis computer; based at least in part on the plurality of trained decision trees, extracting a set of features that distinguish the anomaly from the plurality of samples; generating one or more rules associated with the anomaly from the extracted set of features and causing programming the security analysis computer with the one or more rules.