Cloud Intrusion Detection Agent for In-Vehicle Network Security
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Solution Overview
Problem
Existing intrusion detection systems for in-vehicle networks face challenges in efficiently detecting and responding to security threats with limited system resources and network bandwidth, while also determining the severity and reliability of detected events effectively.
Innovation Solution
An intrusion detection system comprising a message queue module, a rule engine, and an interface manager that stores network messages, applies detection techniques, determines severity and reliability scores for security events, and prioritizes report transmission to a remote backend server based on these scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If intrusion detection functions are integrated into individual ECUs, then each ECU can independently detect intrusions, but the system complexity and cost increase significantly
Solution Approach 1:
The patent consolidates intrusion detection resources by deploying a single intrusion detection agent in the cloud server that serves multiple ECUs. Instead of each ECU having its own detection function, the cloud-based agent centralizes the detection capability, reducing per-ECU complexity while maintaining comprehensive monitoring of the entire in-vehicle network.
Solution Approach 2:
The patent introduces a cloud server as an intermediary between ECUs and intrusion detection analysis. ECUs communicate their status and data to the cloud server, which then performs centralized analysis using the intrusion detection agent. This intermediary approach allows detection functionality to be added without modifying individual ECU architectures.
2Measurement precision
If comprehensive monitoring of all ECUs is implemented, then intrusion detection accuracy improves, but communication overhead and processing time increase
Solution Approach 1:
The patent extracts only the necessary monitoring data from ECUs and transmits it to the cloud server for analysis. The intrusion detection agent selectively processes relevant information rather than analyzing all possible data streams, reducing communication overhead and processing time while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary filtering and preprocessing of ECU data before transmission to the cloud server. By preparing and organizing data in advance at the ECU level, the system reduces the processing burden on the cloud server and enables faster intrusion detection response times.
3Ease of manufacture
If traditional signature-based detection methods are used, then implementation is straightforward, but detection of zero-day attacks and sophisticated intrusions is ineffective
Solution Approach 1:
The patent transitions from traditional signature-based detection parameters to machine learning-based detection parameters. The intrusion detection agent uses trained models that analyze patterns and anomalies in ECU communication data, enabling detection of unknown and sophisticated attacks while maintaining ease of deployment through automated model training and updating.
Solution Approach 2:
The patent replaces manual signature-based detection mechanisms with automated machine learning systems. The intrusion detection agent uses trained neural networks and anomaly detection algorithms that automatically adapt to new attack patterns, eliminating the need for manual signature updates while improving detection effectiveness against zero-day threats.
Data Source
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AI summary
The present disclosure relates to detecting of intrusions into an in-vehicle network. The present disclosure provides methods for efficiently operating a plurality of detection techniques, wherein the method can reduce required system resources while maintaining the architecture of an intrusion detection system (IDS) suitable for mounting on a vehicle and robustness against attack message or security event detection. The present disclosure also provides methods for determining a severity and a reliability of a detected security event, and determining an action for the detection security event based on the severity and the reliability.