Real-time Anomaly Detection Using State Transitions
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Solution Overview
Problem
Anomaly detection in Industrial Control Systems (ICS) networks is challenging due to the complexity, high memory, high computational power, and slowness of existing technologies, making it difficult to accurately distinguish between normal and abnormal message sequences.
Innovation Solution
A computer-implemented method for real-time anomaly detection using state transitions, which identifies variable-length candidate patterns in message sequences, compares them to a baseline pattern store, and alerts security actions upon detecting anomalies, with iterative adaptation and refinement of patterns to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anomaly detection technologies are used in ICS networks, then detection accuracy may be maintained, but system complexity, memory requirements, computational power requirements increase and detection speed decreases
Solution Approach 1:
The patent segments the anomaly detection process into distinct phases: message interception, state extraction, pattern matching against baseline patterns, and anomaly classification. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining detection accuracy through specialized processing at each stage.
Solution Approach 2:
The patent creates simplified copies of message sequences by extracting only the essential state information needed for anomaly detection, rather than processing complete raw messages. This copying approach reduces computational requirements and memory usage while preserving the critical patterns needed for accurate anomaly identification.
2Reliability
If traditional anomaly detection technologies are used in ICS networks, then detection capability is maintained, but memory requirements and computational power requirements increase
Solution Approach 1:
The patent extracts only the essential state information from complete messages, removing unnecessary data while retaining the critical elements needed for anomaly detection. This extraction process significantly reduces memory requirements by storing only relevant state transitions and patterns rather than complete message histories.
Solution Approach 2:
The patent changes the parameters of message representation by transforming raw messages into discrete state values and transitions. This parameter transformation reduces the dimensional space required for storage and computation, enabling efficient anomaly detection with reduced memory requirements while maintaining detection reliability.
3Measurement precision
If traditional anomaly detection technologies are used in ICS networks, then detection thoroughness is maintained, but computational power requirements increase and detection speed decreases
Solution Approach 1:
The patent implements dynamic pattern matching where the baseline patterns are continuously updated and adapted based on observed message sequences. This dynamic approach allows the system to focus computational resources on matching against relevant, recently-observed patterns rather than static comprehensive pattern sets, reducing computational power requirements while maintaining detection thoroughness.
Solution Approach 2:
The patent performs preliminary actions by pre-processing messages to extract state information and pre-computing baseline patterns from historical data before real-time detection. This preliminary processing reduces the computational burden during actual anomaly detection, enabling faster processing while maintaining thoroughness through pre-established pattern references.
4Measurement precision
If traditional anomaly detection technologies are used in ICS networks, then detection accuracy is maintained, but detection speed decreases
Solution Approach 1:
The patent replaces complex mechanical-style pattern matching with a streamlined state transition-based matching system. By substituting traditional comprehensive message comparison with state-based pattern matching, the system achieves both high detection accuracy and fast processing speeds suitable for real-time ICS network monitoring.
Data Source
AI summary
Real-time anomaly detection in a network using state transitions. In one embodiment, a method may include identifying a sequence of messages sent between a first network node and a second network node over a network link. The method may further include identifying a sequence of message states for the sequence of messages. The method may also include identifying variable-length candidate patterns in the sequence of message states. The method may further include adding the candidate patterns to a baseline pattern store. The method may also include comparing a real-time sequence of messages to patterns in the baseline pattern store to detect anomalies in the real-time sequence of messages. The method may further include, in response to the detecting of the anomalies, alerting a security action on one or more of the first network node, the second network node, and the network link using the detected anomalies.