Genetic Programming for Unsupervised Anomaly Detection Specialization
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Solution Overview
Problem
Existing anomaly detection systems in computer networks face challenges in effectively identifying specific anomalies, such as zero-day attacks, due to the lack of ground truth data, dynamic network behaviors, and the ability to differentiate between noise and relevant anomalies, especially in high-dimensional spaces.
Innovation Solution
The implementation of a genetic programming approach that specializes unsupervised anomaly detectors by ranking and generating new traffic flow features based on anomaly scores, using a fitness function to emphasize specific types of anomalies, thereby enhancing the detection of particular anomalies while maintaining the unsupervised nature of the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If unsupervised anomaly detection is used to detect unknown attacks, then the system can identify zero-day attacks, but the system cannot differentiate between noise and relevant anomalies
Solution Approach 1:
The system transforms the anomaly detection problem by changing parameters through genetic programming. It evolves new traffic flow features and modifies the anomaly score calculation parameters to better distinguish relevant anomalies from noise. The fitness function evolves parameters that optimize the detection of specific anomaly types while filtering out noise.
Solution Approach 2:
The system introduces an intermediary layer between raw traffic data and anomaly detection. Genetic programming evolves new traffic flow features that serve as intermediaries, transforming raw network traffic into optimized feature representations that enhance the signal-to-noise ratio and improve anomaly differentiation.
2Adaptability or versatility
If generic anomaly detection features are used, then the system can detect various types of anomalies, but the system cannot effectively detect specific types of anomalies
Solution Approach 1:
The system applies local quality by evolving specialized traffic flow features tailored to specific anomaly types. Instead of using uniform generic features for all anomaly detection, genetic programming creates localized feature sets optimized for detecting particular anomaly patterns, thereby improving precision for specific anomaly types while maintaining overall versatility.
Solution Approach 2:
The system implements dynamics by making the feature set adaptable through genetic programming. The traffic flow features are not static but can be dynamically evolved and adjusted based on the specific anomaly detection task, allowing the system to optimize its feature representation for different anomaly types as needed.
3Device complexity
If traditional feature selection methods are used, then the system can reduce dimensionality, but the system cannot optimize for specific anomaly types
Solution Approach 1:
The system implements feedback through the genetic programming fitness function, which evaluates evolved traffic flow features based on their effectiveness in detecting specific anomaly types. This feedback loop allows the system to iteratively optimize features for both dimensionality reduction and anomaly detection reliability, creating a self-improving feature selection mechanism.
Solution Approach 2:
The system applies self-service by using genetic programming to automatically evolve and optimize its own traffic flow features without manual intervention. The genetic programming algorithm autonomously performs feature selection and optimization, reducing dimensionality while maintaining or improving detection reliability for specific anomaly types.
Data Source
AI summary
In one embodiment, a device in a network receives sets of traffic flow features from an unsupervised machine learning-based anomaly detector. The sets of traffic flow features are associated with anomaly scores determined by the anomaly detector. The device ranks the sets of traffic flow features based in part on their anomaly scores. The device applies a genetic programming approach to the ranked sets of traffic flow features to generate new sets of traffic flow features. The genetic programming approach uses a fitness function that is based in part on the rankings of the sets of traffic flow features. The device specializes the anomaly detector to emphasize a particular type of anomaly using the new sets of traffic flow features.