Stateful IDS Cold Start Detection with Dual Machine 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 lengthy period to learn normal behavior before providing anomaly detection, leaving the network vulnerable during this time.
Innovation Solution
Implement a user-generated initial profile with known behavioral patterns, using first machine-learning models for immediate anomaly detection, while simultaneously training second models to learn actual patterns, with user feedback enhancing the initial profile and accelerating the training of the second model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a stateful IDS learns normal behavior from scratch, then detection accuracy is improved, but the time required before detection can begin increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-collecting data during an initial period and pre-training the machine learning model before the IDS goes live. This allows the model to have head start in learning normal behavior patterns, reducing the cold start period while maintaining detection accuracy. The model is trained offline with historical data before deployment, so when the system starts, it already has a baseline of what constitutes normal behavior.
2Adaptability or versatility
If the IDS monitors diverse traffic patterns, then coverage of security scenarios is improved, but the learning period required increases
Solution Approach 1:
The system segments the learning process into distinct phases: an initial data collection phase where diverse traffic is gathered and stored, and a subsequent model training phase. This segmentation allows the system to handle diverse traffic patterns by collecting them all during the initial phase, then processing this comprehensive dataset to train a model that can detect anomalies across all traffic types simultaneously, rather than learning each pattern sequentially over time.
3Reliability
If no initial model is used, then false detections are reduced, but the system cannot provide detection during the learning period
Solution Approach 1:
The system performs preliminary model training using historical data before deployment, creating a pre-trained model that provides immediate detection capability from day one. This pre-trained model serves as a reliable baseline that can detect anomalies while maintaining low false detection rates, because it has already learned from extensive historical data what constitutes normal behavior across all traffic patterns.
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.