Isolation Forest Anomaly Interpretation for Mitigation
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Solution Overview
Problem
Existing anomaly detection methods struggle to provide sufficient contextual information for detected anomalies, making it challenging to determine effective mitigation actions in complex network environments.
Innovation Solution
The method employs an isolation forest with multiple decision tree estimators to extract contextual information from anomaly observations, generating a mapping object that provides detailed explanations of anomalous behavior, which is then used to determine mitigation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based rules are used to detect malicious activities, then detection accuracy for known anomalies is improved, but the system becomes cumbersome to implement due to large variations among connected devices and anomaly types
Solution Approach 1:
The system enables autonomous learning by training the isolation forest model to automatically identify anomaly patterns without requiring manual signature creation. The model self-adapts to new devices and anomaly types by learning from normal behavior baselines, eliminating the need for continuous manual rule updates while maintaining detection accuracy
Solution Approach 2:
The system transitions from static signature-based detection to dynamic behavior-based detection by continuously updating the baseline of normal behavior. The isolation forest model adapts to parameter variations across different devices by learning device-specific normal patterns, allowing accurate detection without cumbersome device-specific rule configuration
2Adaptability or versatility
If anomaly detection algorithms autonomously learn normal behavior, then detection of unknown anomalies is improved, but insufficient contextual information is provided for determining mitigation actions
Solution Approach 1:
The system segments the anomaly detection process into two distinct components: the isolation forest model that identifies whether an anomaly exists, and the decision tree interpreters that explain why the anomaly occurred. This segmentation allows the system to provide detailed contextual information about specific anomalous behaviors while maintaining the ability to detect unknown anomaly types
Solution Approach 2:
The decision tree estimators serve as intermediaries between the isolation forest anomaly detection and the mitigation action determination. These interpreters translate the complex internal state of the isolation forest into human-readable explanations about which specific behaviors deviated from the baseline, preserving contextual information needed for effective mitigation
3Reliability
If detailed contextual information is provided for each anomaly, then effectiveness of mitigation actions is improved, but processing complexity and computational resources increase
Solution Approach 1:
The system extracts only the most relevant contextual information needed for mitigation by using decision trees to identify and report specific deviating features. Rather than providing complete raw data, the interpreters extract and present only the key behaviors that caused the anomaly, reducing processing complexity while maintaining mitigation effectiveness
Data Source
AI summary
A system and method for anomaly interpretation and mitigation. A method includes extracting at least one input feature vector from observation data related to an observation; applying an isolation forest to the at least one input feature vector, wherein the isolation forest includes a plurality of estimators, wherein each estimator is a decision tree, wherein the output of each estimator is a split-path of a plurality of split-paths, each split-path having a path-length and including name and a corresponding value for a respective output feature of a plurality of output features; generating a mapping object based on the application of the isolation forest to the at least one feature vector, wherein the mapping object includes the plurality of split-paths; clipping the mapping object based on the path-length of each split-path; and determining at least one mitigation action based on the clipped mapping object.