Unsupervised Network Anomaly Detection for Industrial Security
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Solution Overview
Problem
Existing industrial security measures are inadequate in protecting against electronic and telecommunication-based attacks, particularly failing to detect new and unknown attack patterns, and are prone to false positives.
Innovation Solution
The implementation of unsupervised machine learning techniques that utilize predictive models of normal industrial network behavior to detect anomalies and identify potential attacks without relying on known attack patterns, using autoencoding and principal component analysis to generate reconstruction error thresholds for real-time attack detection and source identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing security techniques rely on knowledge of known attacks with predefined patterns (signatures), then they can protect against known attacks, but they are unable to detect new and unknown attacks with unknown patterns
Solution Approach 1:
Instead of training the system to recognize known attack patterns (supervised learning), the patent inverts the approach by training the system to learn normal network behavior patterns and then detecting deviations from this baseline. This unsupervised learning approach enables detection of unknown attacks without requiring prior knowledge of attack signatures.
Solution Approach 2:
The system performs self-learning by automatically establishing a baseline of normal network behavior through continuous monitoring and analysis. The machine learning model autonomously identifies patterns in normal traffic and uses this self-acquired knowledge to detect anomalies, eliminating the need for manual programming of attack signatures.
2Measurement precision
If security systems continuously monitor and analyze network traffic to detect attacks, then detection capability improves, but false positives increase due to short term fluctuations and long term changes
Solution Approach 1:
The system dynamically adapts to changing network conditions by continuously updating its understanding of normal behavior. The machine learning model evolves with the network, adjusting to long-term changes in traffic patterns while maintaining sensitivity to short-term anomalies that indicate attacks, thereby reducing false positives.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and ongoing traffic analysis continuously refine the baseline model. This feedback loop allows the system to distinguish between legitimate variations in network behavior and actual attack patterns, improving accuracy while minimizing false alarms.
Data Source
AI summary
Network-based, unsupervised classifiers are provided. The classifiers identify both known and unknown attacks aimed at industrial networks without the need to have a priori knowledge of known malicious attack patterns.