Industrial Plant Context Threat Scoring for Adaptive Anomaly Detection
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Solution Overview
Problem
Traditional intrusion detection systems in industrial plants face challenges with false positives and negatives due to static threat scoring and lack of dynamic adjustment to changes in the threat landscape, leading to ineffective continuous risk management.
Innovation Solution
A computer-implemented method that determines a context threat score by integrating various context factors, including time, historical data, user activities, and system health, using a weighted analysis and machine learning models to adaptively evaluate anomalies and provide a nuanced threat assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional threat scoring mechanisms are used to categorize threats into predefined levels, then the system provides a simple classification approach, but it results in high false alarm rates and fails to identify actual threats due to static scoring criteria
Solution Approach 1:
The patent implements dynamic threat scoring by continuously adjusting context factor weights based on changing threat landscapes and operational conditions. The system transitions from static predefined threat levels to dynamic contextual assessment, where scores are continuously updated based on real-time data from multiple context factors including environmental conditions, operational state, and threat intelligence, thereby improving detection accuracy while maintaining operational simplicity
Solution Approach 2:
The system changes the parameters of threat assessment by introducing multiple context factors (environmental, operational, security) with dynamically adjustable weights. Instead of using fixed threat categories, the system varies the importance of different parameters based on current conditions, allowing the same event to be scored differently depending on contextual parameters, thus reducing false alarms while preserving ease of use
2Stability of the object's composition
If static threat scores are provided without dynamic adjustment, then the system maintains consistent evaluation criteria, but it lacks effectiveness for continuous risk management as the threat landscape changes
Solution Approach 1:
The system implements feedback mechanisms where threat scores are continuously refined based on outcomes and changing conditions. Context factor weights are adjusted dynamically based on feedback from threat intelligence updates, operational changes, and incident outcomes, allowing the evaluation criteria to adapt to the evolving threat landscape while maintaining a structured assessment framework through the consistent application of contextual parameters
Solution Approach 2:
The patent transforms static threat scoring into a dynamic system where evaluation criteria adapt to changing conditions. The system maintains structural consistency through its multi-factor framework while dynamically adjusting the weight and relevance of different context factors based on current threat landscapes, operational states, and environmental conditions, enabling continuous risk management effectiveness
3Adaptability or versatility
If context factor weights are dynamically adjusted based on threat intelligence updates and operational changes, then the system achieves adaptive threat assessment, but it increases system complexity
Solution Approach 1:
The patent segments the threat assessment system into distinct context factors (environmental, operational, security) with specific weightings for each. This segmentation allows the complex adaptive assessment to be broken down into manageable components, where each factor can be independently monitored and adjusted, reducing overall system complexity while maintaining adaptive capability through modular design
Solution Approach 2:
The system manages complexity by changing parameters in a structured manner - adjusting the weights of predefined context factors based on threat intelligence and operational changes. Rather than creating entirely new assessment mechanisms, the system modifies parameters within an existing framework, enabling adaptive assessment while maintaining manageable complexity through consistent parameter adjustment protocols
Data Source
AI summary
A method for determining a context threat score in an industrial plant includes obtaining input data from the industrial plant, the input data comprising environmental data and/or operational data of at least one section of the plant; determining a context factor score for the at least one section of the industrial plant based on at least one pre-determined context factor and the input data, wherein the at least one context factor comprises a relation between the input data and context data of the at least one section, wherein the context data comprises at least one context dependent property of the at least one section; and determining, by the processing unit, a context threat score based on the at least one context factor score.