Instance-Based Learning Framework for Anomaly Intrusion Detection
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Solution Overview
Problem
Anomaly intrusion detection systems face challenges in efficiently analyzing discrete data for intrusion traces and managing growing behavioral profiles, which can consume significant memory, impairing real-time detection capabilities.
Innovation Solution
An instance-based learning framework that captures historical user data, profiles normal behavior, and uses a similarity metric to cluster data into representative instances with frequency and pointers, allowing for efficient comparison of test data to identify anomalies, thereby reducing memory usage and enabling real-time detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If behavioral profiles are maintained in main memory for real-time intrusion detection, then detection speed is improved, but memory consumption increases significantly
Solution Approach 1:
The behavioral profile data is segmented into clusters of similar user behaviors. Each cluster is represented by a representative instance (prototype) and a set of exemplar instances. This segmentation reduces the total data volume stored in memory while preserving the essential behavioral patterns needed for real-time detection.
Solution Approach 2:
Instead of storing complete behavioral profiles of all users, the system creates simplified copies in the form of representative instances that capture the essential characteristics of each behavior cluster. These representative copies are stored in main memory for fast comparison, while the full detailed data can be stored elsewhere.
2Measurement precision
If complete historical data is stored for accurate anomaly detection, then detection accuracy is improved, but data storage requirements increase
Solution Approach 1:
Multiple similar historical data points are merged into cluster groups, where each group is represented by a representative instance. This merging process reduces storage requirements by combining redundant information while preserving the essential patterns needed for accurate anomaly detection through similarity comparison.
Solution Approach 2:
The system discards redundant detailed historical data that can be reconstructed or approximated from representative instances. When needed, the essential behavioral patterns are recovered through similarity measurements against the stored representative instances, maintaining detection accuracy without storing complete historical records.
3Adaptability or versatility
If behavioral profiles grow to capture all user behaviors, then detection completeness is improved, but system performance deteriorates
Solution Approach 1:
The system dynamically organizes behavioral profiles into clusters that adapt to the diversity of user behaviors. As new behaviors are observed, they are integrated into existing clusters or form new clusters, maintaining detection completeness. The cluster-based structure allows efficient processing by comparing new behaviors against representative instances rather than exhaustive profile searches.
Data Source
AI summary
Intruders into a computer are detected by capturing historical data input into the computer by a user during a training mode, by profiling the historical data during the training mode to identify normal behavior, by capturing test data input by the user into the computer during an operational mode, by comparing the test data with the profiled historical data in accordance with a predetermined similarity metric during the operational mode to produce similarity results, and by evaluating the similarity results during the operational mode to identify abnormal data.