In-Vehicle Network Intrusion Detection via Segmented Modules
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Solution Overview
Problem
Current intrusion detection systems for in-vehicle networks lack effective mechanisms to detect anomalies and respond to potential threats, particularly in real-time, due to limitations in rule-based detection and remote attack vulnerability.
Innovation Solution
A system comprising an anomaly detection module, resident log generation module, transmitted log generation module, and remedial action module, which monitors network messages, generates logs, and takes actions based on predefined rules, including adjusting log transmission and storage, and restricting communication to mitigate intrusions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based detection is used to monitor network traffic, then detection capability is provided, but real-time response effectiveness is insufficient
Solution Approach 1:
The intrusion detection system is divided into multiple independent modules: anomaly detection module, resident log generation module, transmitted log generation module, and remedial action module. Each module operates independently and can respond to threats at different stages, enabling both reliable detection and real-time response.
Solution Approach 2:
The system pre-configures detection rules and log management strategies before intrusions occur. When anomalies are detected, pre-established response protocols are automatically executed, eliminating delays in real-time response while maintaining accurate detection through predefined detection criteria.
2Measurement precision
If comprehensive log monitoring is implemented, then detection accuracy is improved, but system vulnerability to remote attacks increases
Solution Approach 1:
The system extracts and processes only the necessary log data locally within the vehicle network before transmission. Sensitive information is filtered and processed at the source, reducing the amount of data exposed to remote systems and minimizing attack surface while maintaining detection accuracy through selective monitoring.
Solution Approach 2:
A gateway module acts as an intermediary between the vehicle network and remote computing systems. This gateway selectively transmits processed log data while filtering out sensitive information, enabling accurate detection through comprehensive monitoring while protecting against remote attacks by preventing direct access to raw data.
3Adaptability or versatility
If continuous log transmission is performed, then remote monitoring capability is enhanced, but data transmission security is reduced
Solution Approach 1:
The system implements feedback mechanisms where the gateway receives control signals from remote computing systems to adjust transmission parameters. Log transmission is activated, suspended, or modified based on real-time security assessments and operational context, enabling flexible remote monitoring while enhancing security through adaptive transmission control.
Solution Approach 2:
The log transmission process is made dynamic rather than static. The system can adjust transmission frequency, data volume, and content based on current operational conditions and security threats. This dynamic approach allows comprehensive remote monitoring when needed while reducing transmission exposure to attacks during high-risk periods.
4Measurement precision
If detailed incident logging is implemented, then detection precision is improved, but storage resource consumption increases
Solution Approach 1:
The system applies different logging depths and detail levels to different types of network events and communication buses. Critical events receive detailed logging for high detection precision, while routine events receive summarized logging to reduce storage consumption. This localized quality approach optimizes both detection precision and storage resource utilization.
Solution Approach 2:
The system dynamically adjusts log retention policies, compression ratios, and storage allocation parameters based on detected anomaly patterns and storage availability. When storage resources are constrained, the system automatically adjusts parameters to prioritize critical event logging while maintaining detection precision for security-relevant incidents.
Data Source
AI summary
A system for in-vehicle network intrusion detection includes: (i) an anomaly detection module configured to obtain one or more network messages from one or more communication buses of a vehicle describing one or more events associated with the vehicle and detect whether at least some of the one or more events constitute an anomaly based on predefined rules to provide detected anomaly event data; (ii) a resident log generation module configured to generate one or more resident incident logs based on the detected anomaly event data, wherein the one or more resident incident logs comprise metadata associated with one or more detected anomalous events; and (iii) a transmitted log generation module configured to generate one or more transmitted incident logs based on the one or more resident incident logs, wherein each of the one or more transmitted incident logs corresponds to a resident incident log.