Industrial Sensor-Based Cyber Threat Detection With Pattern Recognition
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Solution Overview
Problem
Industrial control systems lack the necessary processing capability to detect and react to cybersecurity threats, posing a significant challenge in maintaining infrastructure integrity, especially in critical sectors like power grids and public water systems.
Innovation Solution
The system utilizes existing endpoint sensors in industrial control systems to detect cybersecurity threats by processing sensor data with pattern recognition algorithms, including machine learning algorithms, which can recognize suspicious patterns and execute responsive actions without interfering with existing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pattern recognition algorithms are applied to sensor data processing, then cybersecurity threat detection capability is improved, but processing complexity increases
Solution Approach 1:
The patent introduces pattern recognition algorithms as an intermediary layer between sensor data collection and cybersecurity threat detection. These algorithms process sensor data to identify suspicious patterns, acting as a mediator that enhances detection capability without requiring complete system redesign. The algorithms include both supervised learning models trained on labeled threat data and unsupervised learning models that detect anomalies without prior knowledge of specific threat patterns.
Solution Approach 2:
The cybersecurity detection system is segmented into multiple independent components: sensor data collection modules, pattern recognition processing modules, and response execution modules. This segmentation allows each component to be optimized independently and facilitates parallel processing of different sensor streams, reducing overall processing complexity while maintaining comprehensive threat detection across multiple industrial processes.
2Speed
If real-time sensor data processing is implemented, then response speed to cyber threats is improved, but computational resource consumption increases
Solution Approach 1:
The system applies partial processing by focusing computational resources on detecting specific suspicious patterns in sensor data rather than analyzing all data equally. The pattern recognition algorithms are configured to identify only the most critical threat indicators, performing sufficient processing to detect threats without the excessive computational overhead of complete data analysis. This selective processing maintains real-time response capability while conserving computational resources.
3Ease of manufacture
If existing industrial sensors are utilized for cybersecurity detection, then infrastructure cost is reduced, but detection versatility is limited
Solution Approach 1:
The patent makes existing industrial sensors serve multiple functions: their primary industrial process monitoring role and an additional cybersecurity threat detection role. The pattern recognition algorithms are designed to extract security-relevant information from the same sensor data streams used for process control, enabling a single sensor infrastructure to provide both industrial operation monitoring and cybersecurity protection without requiring separate dedicated security sensors.
Data Source
AI summary
Systems and methods of providing industrial system cybersecurity event detection and corresponding response are described. The systems and methods utilize various end point sensors already available in an industrial control system and an associated monitoring process to detect cybersecurity and other security threats based on data collected by the sensors. The cybersecurity monitoring process may be trained with sensor data patterns and behaviors for known threats to recognize potentially malicious activity. Such a process may also learn to recognize and be trained on new threats and may incorporate each new threat to stay current with evolving industrial threats. This allows an enterprise to utilize its existing industrial infrastructure to detect and act upon a variety of threats to an industrial system with little or no interference or interruption of existing industrial processes.