Anomaly Forecasting for Industrial Control Systems
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Solution Overview
Problem
Current information technology and operational technology protection mechanisms are inadequate in preventing cyber-attacks on industrial control systems, as they fail to effectively identify and neutralize anomalies and threats, leading to potential system failures and disruptions.
Innovation Solution
A feature-based anomaly forecasting process is implemented, which uses a cloud platform to receive and analyze data from monitoring nodes, transforming input signals into feature values and forecasting potential anomalies, thereby providing early warning signals to control systems or operators, effectively protecting against unauthorized intrusions and faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional information technology and operational technology protection mechanisms are used, then basic security coverage is provided, but they fail to effectively identify and neutralize anomalies and threats
Solution Approach 1:
The system performs preliminary actions by continuously monitoring feature values and forecasting future anomalies before they occur. The anomaly forecaster predicts when feature values will cross decision boundaries, enabling early warning and preventive action before actual cyber attacks or failures happen, thus improving detection capability while maintaining protection effectiveness.
2Adaptability or versatility
If more software is made available through the cloud, then functionality and versatility are improved, but the situation for cyber security gets worse
Solution Approach 1:
The system implements feedback mechanisms where the anomaly forecaster continuously monitors feature values from cloud-based software and provides real-time warnings when anomalies are detected. This feedback loop enables the system to adapt to new software deployments while maintaining security protection by detecting anomalies in cloud software operations.
3Reliability
If existing protection mechanisms are used, then basic security is maintained, but residual faults and vulnerabilities remain exploitable
Solution Approach 1:
The patent replaces traditional mechanical security mechanisms with a data-driven forecasting approach. Instead of relying on conventional security software that reacts to known threats, the system uses anomaly forecasters that predict future anomalies by analyzing feature value patterns, substituting reactive security mechanisms with proactive prediction-based protection that can identify and neutralize residual faults before exploitation.
Data Source
AI summary
The example embodiments are directed to a system and method for forecasting anomalies in feature detection. In one example, the method includes storing feature behavior information of at least one monitoring node of an asset, including a normalcy boundary identifying normal feature behavior and abnormal feature behavior for the at least one monitoring node in feature space, receiving input signals from the at least one monitoring node of the asset and transforming the input signals into feature values in the feature space, wherein the feature values are located within the normalcy boundary, forecasting that a future feature value corresponding to a future input signal from the at least one monitoring node is going to be positioned outside the normalcy boundary based on the feature values within the normalcy boundary, and outputting information concerning the forecasted future feature value being outside the normalcy boundary for display.