OT Security Analysis Using Adaptive Attack Detection Models
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Solution Overview
Problem
The increasing complexity and variability of operational technology (OT) systems pose challenges for effective security analysis and monitoring, particularly due to the integration of Information Technology (IT) networks, which makes them vulnerable to sophisticated security threats and novel attack methods.
Innovation Solution
An analysis device and system for OT systems that extracts features from security logs and network traffic, generates an algorithm model to identify abnormal behaviors, and integrates human intelligence with artificial intelligence for enhanced intrusion detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security monitoring methods are used in OT systems, then the system maintains simplicity and closed architecture, but the system becomes vulnerable to sophisticated security threats and novel attack methods
Solution Approach 1:
The patent segments the security analysis process into multiple independent modules: data collection module, feature extraction module, behavior analysis module, and algorithm model generation module. Each module handles specific tasks, allowing the complex security monitoring function to be built from simpler, manageable components that can be independently developed and maintained.
Solution Approach 2:
The patent implements dynamic algorithm models that continuously adapt to new attack patterns. The system automatically generates updated algorithm models based on analyzed attack behaviors, transforming the static security monitoring system into a dynamic one that evolves with emerging threats, thereby improving reliability without requiring complete system redesign.
2Measurement precision
If comprehensive security analysis is performed on complex OT systems, then the detection capability for known and novel attacks is improved, but the analysis time and computational resources increase
Solution Approach 1:
The patent performs preliminary feature extraction from collected data, identifying key characteristics and patterns before conducting full security analysis. By pre-processing data and extracting relevant features in advance, the system reduces the computational burden during actual attack detection, maintaining high accuracy while reducing analysis time.
Solution Approach 2:
The patent dynamically adjusts analysis parameters and algorithms based on the specific context and type of security event detected. The system selects and configures appropriate algorithm models according to the analyzed attack behavior, optimizing the balance between detection accuracy and analysis speed for different threat scenarios.
3Adaptability or versatility
If static algorithm models are used for intrusion detection, then the system is easy to implement and maintain, but the system cannot effectively identify novel attack behaviors
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously analyzes detected attack behaviors and uses this information to automatically generate updated algorithm models. The analysis results feed back into the model generation process, creating a closed-loop system that continuously improves its detection capabilities against novel attacks without requiring manual intervention.
Solution Approach 2:
The system performs self-updating of algorithm models based on analyzed attack patterns. Rather than requiring external experts to manually create and update detection models, the system automatically generates updated models from its own analysis data, reducing the complexity burden on operators while maintaining high adaptability to new threats.
Data Source
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AI summary
An analysis device (10), method and system for an operational technology system and a storage medium, relate to the technical field of industrial networks and information security. The device comprises a parsing module (101) configured to acquire first data related to the operational technology system from a data storage area, and parse out first features of the first data; an identifying module (102) configured to identify an abnormal feature from the first features; and a model generation module (103) configured to acquire second data related to the abnormal feature from the data storage area, and generate an algorithm model based on the second data, where the algorithm model is used for identifying an attack behavior related to the abnormal feature. The attack behavior can be automatically identified, and complementation of the advantages of human intelligence and the advantages of artificial intelligence is realized.