Stateful IDS Cold-Start Detection with Dual Learning Profiles
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Solution Overview
Problem
Stateful Intrusion Detection Systems (IDS) face a 'cold start' problem when new resources are added to a network, as they require a learning period to establish normal behavior patterns, leading to weeks of undetected security risks.
Innovation Solution
Implement an initial profile with known behavioral patterns using first machine-learning models for immediate anomaly detection, while training second models to learn actual patterns, leveraging user feedback to enhance and merge profiles for full protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a stateful IDS learns normal behavior patterns from scratch, then detection accuracy is improved, but the time required before detection capability becomes effective increases (cold start problem)
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical network traffic data before deployment. This creates pre-established behavioral profiles that enable the IDS to detect anomalies immediately upon deployment, eliminating the cold start learning period while maintaining detection accuracy through the pre-learned patterns
2Adaptability or versatility
If an IDS monitors diverse traffic patterns comprehensively, then coverage of security scenarios is improved, but the learning period required to establish baseline behavior increases
Solution Approach 1:
The patent uses preliminary action by pre-training models on diverse historical traffic data that represents multiple security scenarios and normal behaviors. This pre-training establishes comprehensive baseline profiles across various traffic types, enabling immediate detection of anomalies in diverse scenarios without requiring extended learning periods for each new scenario
Solution Approach 2:
The patent applies universality by creating a unified machine learning model that learns and detects across multiple security scenarios and traffic types simultaneously. The model develops universal detection capabilities that work across diverse network conditions and threat types, providing broad coverage without requiring separate learning periods for each scenario
Data Source
AI summary
A computing system generates from received user input an initial profile. The initial profile specifies expected behavioral patterns of datasets that are to be received by the computing system. The computing system extracts from received datasets features that are indicative of behavioral patterns of the received datasets. The computing system provides the initial profile to first machine-learning models. The first machine-learning models have been trained using a subset of the received datasets. The first machine-learning models use the initial profile to determine if the behavioral patterns of the received datasets are anomalous. The computing system includes second machine-learning models that have been trained using a subset of the received datasets. The second machine-learning models train a second profile based on the extracted features to specify behavioral patterns of the received datasets that are learned by the second machine-learning model.