Industrial Plant Context Threat Scores from Environmental and Operational Data
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Solution Overview
Problem
Traditional intrusion detection systems in industrial plants face limitations such as high false alarm rates and static threat scoring that fail to dynamically adjust to changes in the threat landscape, leading to false negatives and ineffective risk management.
Innovation Solution
A computer-implemented method for determining a context threat score that integrates various context factors, including environmental and operational data, using machine learning models to provide a dynamic and nuanced assessment of potential security threats, enabling adaptive responses based on real-time adjustments and holistic contextual assessments.
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 simplified threat assessment, but it results in high false alarm rates and fails to detect new attack methods
Solution Approach 1:
The patent implements dynamic threat scoring that continuously adapts to changing threat landscapes by incorporating real-time context factors such as environmental data, operational data, and historical anomaly patterns. The system updates threat scores dynamically rather than relying on static predefined categories, enabling it to respond to new attack methods while maintaining operational simplicity.
Solution Approach 2:
The system changes the parameters used for threat assessment by incorporating multiple context factors including environmental conditions, operational parameters, and historical data patterns. This multi-parameter approach allows the system to differentiate between benign anomalies and actual threats more accurately, reducing false alarms while maintaining ease of operation through standardized scoring.
2Stability of the object's composition
If static threat scores are provided to assess security threats, then the system offers a stable assessment framework, but it lacks dynamic adjustment to changes in the threat landscape
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors contextual factors including environmental data, operational data, and anomaly detection results. This feedback loop enables dynamic adjustment of threat scores based on changing conditions while maintaining a stable assessment framework structure. The system learns from historical patterns and adapts its scoring criteria over time.
Solution Approach 2:
The threat scoring system transitions from static to dynamic by incorporating real-time context factors and historical anomaly data. The system continuously updates threat assessments based on changing environmental and operational conditions, enabling adaptability to new threats while maintaining framework stability through standardized scoring procedures.
3Measurement precision
If comprehensive context factors are integrated into threat scoring, then the system provides accurate threat assessment, but it increases system complexity
Solution Approach 1:
The patent segments the complex threat assessment process into distinct context factors including environmental data, operational data, historical anomaly patterns, and real-time system state. Each factor is evaluated separately and then integrated into a comprehensive threat score. This segmentation manages complexity by organizing multiple data sources into structured, manageable components while maintaining high assessment accuracy.
Solution Approach 2:
The system implements a universal threat scoring framework that can evaluate multiple different context factors through a standardized process. The same scoring mechanism handles diverse inputs including environmental conditions, operational parameters, and security events, reducing overall system complexity through multi-functionality while maintaining comprehensive and accurate threat assessment.
Data Source
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AI summary
This method marks a paradigm shift in our approach to evaluating and responding to security anomalies. At its core, this innovative approach involves assigning threat scores based on the unique context of our industrial environment and based on input data. It is about comprehending the criticality of affected systems, potential operational impacts, and the likelihood of a security violation. Context holds immense significance in the realm of cybersecurity. What might trigger concern in one environment could be entirely normal in another. Using this method ensuring that threat assessments are precisely tailored to the specific characteristics of the industrial plants. The enhanced anomaly detection system of the industrial plant based on this method employs advanced algorithms, such as machine learning models, to assign context threat scores to detected anomalies and deviations. These algorithms consider various factors, including the criticality of affected systems, the potential impact on plant operations and the likelihood of an actual security breach. The system utilizes historical data and contextual information to train the models and develop accurate scoring mechanisms. By providing context threat scores, the enhanced anomaly detection system enables operators to prioritize their response efforts based on the severity and potential consequences of each detected anomaly. The computer-implemented method for determining a context threat score in an industrial plant comprises the following steps. Obtaining, by an obtaining unit, input data from at least one section of the industrial plant, wherein the input data comprises environmental data and/or operational data of the at least one section. Determining, by a processing unit, 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. Determining, by the processing unit, a context threat score based on the at least one context factor score.