OT Cybersecurity Appliance Using ML Baselines for Anomaly Response
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Solution Overview
Problem
Operational Technology (OT) systems, such as Industrial Control Systems (ICS), are highly vulnerable to cyber threats due to their complex network topology and exposure to traditional cyber threats, with Programmable Logic Controllers (PLCs) being particularly susceptible, leading to costly remediation efforts.
Innovation Solution
A cyber security appliance equipped with modules that utilize machine-learning models to analyze normal patterns of life in OT networks, detect anomalies, and autonomously respond to cyber threats, integrating with both OT and Information Technology (IT) networks for comprehensive threat detection and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cyber security measures are used in OT networks, then security coverage is limited, but system complexity and disruption to normal operations increase
Solution Approach 1:
The patent replaces traditional mechanical cyber security measures (firewalls, intrusion detection systems) with an AI-based system that uses machine learning models to analyze network traffic and device behavior. The AI security appliance learns normal patterns of life for OT devices and automatically detects anomalies, substituting complex rule-based systems with adaptive intelligence that reduces overall system complexity while improving security effectiveness.
Solution Approach 2:
The AI-based security system performs self-learning and self-adjustment by continuously monitoring network traffic and updating its machine learning models. The system autonomously adapts to new threats and normalizes its understanding of device behavior without requiring manual configuration or intervention, enabling the system to serve itself and reduce operational complexity.
2Measurement precision
If AI-based threat detection is implemented, then threat detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary learning during a baseline period to establish normal patterns of life for OT devices before actual threat detection begins. This pre-training phase allows the AI model to develop its understanding of normal behavior, so that during operational phase, the system can quickly compare incoming data against established patterns without requiring extensive real-time processing, thereby improving detection accuracy while minimizing processing time.
Solution Approach 2:
The patent implements a two-phase approach where the system performs comprehensive learning during the baseline period (excessive action) and then uses the learned models for efficient real-time detection (partial action). This allows the system to invest computational resources upfront to build accurate models, then use those models for faster, more efficient ongoing threat detection with reduced processing requirements.
3Speed
If autonomous response actions are taken, then response speed to threats improves, but risk of false positives and operational disruption increases
Solution Approach 1:
The system implements feedback mechanisms where the results of autonomous response actions are continuously monitored and fed back into the machine learning models. When false positives occur or when response actions are taken, the system learns from these outcomes and adjusts its detection thresholds and response criteria. This feedback loop enables the system to improve its accuracy over time while maintaining fast response speeds, as the models become better at distinguishing true threats from normal anomalies.
Data Source
AI summary
A cyber security appliance has one or more modules to interact with entities in an operational technology network and potentially in an informational technology network. The operational technology module can reference various machine-learning models trained on a normal pattern of life of users, devices, and/or controllers of the operational technology network. A comparator module cooperates with the operational technology module to compare the received data on the operational technology network to the normal pattern of life of any of the users, devices, and controllers to detect anomalies in the normal pattern of life for these entities in order to detect a cyber threat. An autonomous response module can be programmed to respond to counter the detected cyber threat.