In-Situ Trainable Intrusion Detection via Semi-Supervised Learning
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Solution Overview
Problem
Existing network intrusion detection systems rely on rule-based or anomaly-based methods that require human analysis and are prone to high false positive alerts, struggling to detect unknown attacks effectively.
Innovation Solution
The development of an in-situ trainable intrusion detection system using semi-supervised learning with machine-learning sensors that can detect previously unknown attacks with low false positive rates, leveraging unlabelled network data and requiring minimal labelled samples for training, employing techniques like Laplacian Regularized Least Squares and Alternating Structure Optimization models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based tools or anomaly-based processes are used for intrusion detection, then human analysts can identify intrusions, but the systems render a high number of false positive alerts and require significant human analysis time
Solution Approach 1:
The system employs self-service through automated machine learning classification that operates without continuous human intervention. The semi-supervised learning algorithm automatically processes network traffic, classifies potential intrusions, and updates its model continuously, reducing reliance on human analysts while maintaining high detection accuracy and minimizing false positives through algorithmic decision-making
Solution Approach 2:
The system applies parameter changes by dynamically adjusting classification thresholds and model parameters based on learned patterns from training data. The machine learning model adapts its decision boundaries and sensitivity parameters to optimize the balance between detection accuracy and false positive reduction, automatically tuning system behavior based on observed network characteristics
2Reliability
If rule-based signatures are used to detect intrusions, then known attacks can be identified, but it is difficult to identify patterns of traffic common to unknown intrusions
Solution Approach 1:
The system performs preliminary action through an offline training phase where the machine learning model is trained on labeled network traffic data before deployment. This preliminary training establishes baseline patterns of normal and malicious traffic, enabling the system to detect both known and unknown attacks by comparing new traffic against these pre-established patterns without requiring real-time rule updates
Solution Approach 2:
The system implements dynamics through continuous model updating and adaptation. The machine learning classifier dynamically adjusts its decision boundaries and internal parameters based on new training data, allowing it to evolve its detection capabilities over time. This dynamic adaptation enables the system to recognize both established attack patterns and emerging threats without relying on static signature databases
3Reliability
If anomaly-based processes compare behavior to baseline behavior, then abnormal traffic can be detected, but some malicious traffic is not abnormal and some non-malicious traffic tends to be anomalous
Solution Approach 1:
The system substitutes mechanical anomaly detection methods with machine learning-based classification. Instead of using rigid threshold-based anomaly detection that flags any deviation from baseline behavior, the system employs trained classification models that understand the nuanced patterns of both normal and malicious traffic, replacing simple mechanical comparison with intelligent probabilistic classification that reduces false positives while maintaining detection effectiveness
Data Source
AI summary
A computer implemented method detects intrusions using a computer by analyzing network traffic. The method includes a semi-supervised learning module connected to a network node. The learning module uses labeled and unlabeled data to train a semi-supervised machine learning sensor. The method records events that include a feature set made up of unauthorized intrusions and benign computer requests. The method identifies at least some of the benign computer requests that occur during the recording of the events while treating the remainder of the data as unlabeled. The method trains the semi-supervised learning module at the network node in-situ, such that the semi-supervised learning modules may identify malicious traffic without relying on specific rules, signatures, or anomaly detection.