OT Network Anomaly Detection via Isolation Forest
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Solution Overview
Problem
Existing technologies lack scalability and effectiveness in detecting anomalous network behavior in operational technology (OT) protocols, particularly in identifying zero-day malware without human intervention.
Innovation Solution
A machine learning-based system that uses an isolation forest technique to detect anomalous network behavior in OT protocols, allowing for automated malware detection without labeled data and capable of identifying zero-day attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional malware detection methods are used, then detection effectiveness is limited, but system complexity and resource consumption increase
Solution Approach 1:
The patent replaces conventional mechanical rule-based detection systems with a machine learning-based detection system. The machine learning model automatically learns patterns from network traffic data and identifies malware without requiring manual rule configuration, thereby improving detection effectiveness while reducing system complexity and resource consumption.
Solution Approach 2:
The machine learning system performs self-training and self-improvement by automatically learning from network traffic data. The system continuously adapts to new malware patterns without human intervention, enabling it to detect zero-day attacks while maintaining low operational complexity.
2Measurement precision
If manual analysis methods are used, then detection accuracy is limited, but detection speed decreases
Solution Approach 1:
The patent replaces manual analysis methods with automated machine learning-based anomaly detection. The system processes network traffic data at high speed using computational algorithms, achieving both high detection accuracy and fast processing speeds simultaneously, unlike manual methods which are slow and less accurate.
Solution Approach 2:
The detection system dynamically adapts to changing network conditions and malware patterns through continuous learning. The machine learning model adjusts its detection parameters and patterns in real-time based on incoming data, maintaining high accuracy while processing traffic at network speed.
3Adaptability or versatility
If traditional signature-based detection is used, then zero-day malware cannot be detected, but false positive rates increase
Solution Approach 1:
Instead of detecting malware based on known signatures (traditional approach), the patent inverts the approach by detecting anomalies that deviate from normal behavior patterns. This unsupervised learning approach identifies zero-day malware by recognizing unusual patterns without requiring prior knowledge of specific malware signatures, thereby reducing false positives from signature mismatches.
Solution Approach 2:
The system performs preliminary learning of normal network behavior patterns during a training phase before actual detection begins. This preliminary action establishes a baseline of legitimate traffic, enabling the system to accurately identify deviations caused by zero-day malware while minimizing false positives from legitimate unusual traffic.
Data Source
AI summary
Techniques for detecting anomalous network behavior in operational technology (OT) protocols are disclosed. A system, process, and/or computer program product for detecting anomalous network behavior in OT protocols include monitoring network traffic to perform automated OT malware detection analysis of OT related network traffic, extracting one or more features from the OT related network traffic, inputting the one or more extracted features into a model for malware detection analysis, and performing an action based on a result of the model.