Embedded Anomaly Detector for Cyber-Physical Systems
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Solution Overview
Problem
Cyber-physical systems (CPS) such as control systems on vehicles, ships, and industrial plants are vulnerable to cyberattacks, with existing security measures failing to effectively detect and prevent both known and novel attack patterns, particularly in scenarios where attackers breach existing defense layers.
Innovation Solution
An embedded anomalies detector (HEAD) is integrated into CPS devices to monitor data input, output, and memory for anomalies, using customized detection algorithms with minimal computational footprint, capable of detecting known and novel attacks by recognizing patterns and deviations from normal behavior, and reporting security events to secure access servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing security measures (firewalls, access control, encryption) are implemented, then basic security protection is provided, but they fail to detect novel attack patterns and require significant computational resources
Solution Approach 1:
The patent replaces traditional mechanical security measures (firewalls, access control lists, encryption protocols) with an embedded anomaly detector that uses statistical pattern recognition and machine learning algorithms to automatically detect both known and novel cyberattacks, reducing the need for complex manual security configurations
Solution Approach 2:
The embedded anomaly detector continuously learns from normal system behavior patterns and automatically adapts to detect deviations, enabling the security system to self-improve its detection capabilities without requiring external intervention or complex manual updates for each new threat
2Measurement precision
If comprehensive monitoring of all data input, output, and memory is performed, then anomaly detection accuracy is improved, but processing overhead and computational resources increase
Solution Approach 1:
The patent extracts and monitors only the most critical data elements and behavioral patterns that are most indicative of cyberattacks, rather than processing all possible data, thereby maintaining high detection accuracy while minimizing computational overhead and energy consumption on resource-constrained embedded devices
Solution Approach 2:
The system performs monitoring at selective critical points in the data flow and uses efficient statistical algorithms that provide sufficient detection accuracy without requiring exhaustive analysis of every single data point, achieving a practical balance between accuracy and computational efficiency
3Reliability
If parallel monitoring is implemented alongside data processing, then real-time anomaly detection is achieved, but system processing speed may be reduced
Solution Approach 1:
The patent segments the monitoring function into separate embedded anomaly detection modules that operate independently from the main data processing pipeline, allowing parallel execution without interfering with the primary control system's performance while maintaining real-time detection capabilities
Data Source
AI summary
Disclosed are methods, systems, and non-transitory computer-readable medium for detecting data anomalies on a device. The method may include determining data patterns for data input to the device, data output from the device, and/or data stored in a memory of the device; monitoring the data input, data output, and the data stored in the memory at least based on the determined data patterns in parallel with processing of the data input, data output, and/or the data stored in the memory; and detecting whether an anomaly exists in the data input, data output, and/or the data stored in the memory of the device based on the monitoring.