IVI Intrusion Detection Using eBPF Kernel Behavior Tracing
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Solution Overview
Problem
Modern vehicles are vulnerable to cyber attacks due to their complex computing systems with diverse processors, sensors, and operating systems, which can jeopardize occupant safety and brand reputation, and existing detection methods are often ineffective against sophisticated attackers.
Innovation Solution
Implementing a synthetic sensor system that utilizes extended Berkeley Packet Filter (eBPF) technology to monitor kernel-level behaviors, generating security insights through machine learning and rule-based anomaly detection, and reporting malicious activities to a Security Operations Center.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a synthetic sensor system with eBPF technology is implemented to monitor kernel-level behaviors, then detection precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces a synthetic sensor as an intermediary component that sits between the kernel and the intrusion detection system. This synthetic sensor uses eBPF technology to monitor kernel behaviors and translate them into observable metrics, thereby improving detection precision without requiring direct modification of the kernel or intrusion detection system. The intermediary handles the complexity of kernel-level monitoring internally while presenting a simplified interface to higher-level systems.
Solution Approach 2:
The intrusion detection system is segmented into multiple independent components: the synthetic sensor for data collection, the eBPF tracing mechanism for kernel monitoring, the machine learning model for anomaly detection, and the reporting system for alert generation. This segmentation allows each component to be optimized independently and reduces overall system complexity by distributing functionality across specialized modules.
2Reliability
If kernel-level tracing is performed to detect malicious behaviors, then reliability is improved, but use of energy increases
Solution Approach 1:
The synthetic sensor implements partial monitoring by selectively tracing only specific kernel functions and behaviors that are most indicative of malicious activity. Instead of monitoring all kernel operations, the system focuses on critical security-relevant functions, thereby maintaining high reliability for detecting attacks while reducing the overall energy consumption of kernel-level tracing.
Solution Approach 2:
The system dynamically adjusts tracing parameters such as sampling rates, trace buffer sizes, and monitored function sets based on system conditions and threat levels. During normal operation, tracing is performed at lower intensity to conserve energy, while during suspected attack scenarios, the system increases tracing depth and frequency to maintain high reliability for detection.
Data Source
AI summary
A method at a computing device, the method including placing a trace on a plurality of behaviors within a kernel on the computing device; generating data from the trace; assembling the data into an event; and formatting the event into a security sensor output. Further, a computing device having a processor and communications subsystem, wherein the computing device is configured to place a trace on a plurality of behaviors within a kernel on the computing device; generate data from the trace; assemble the data into an event; and format the event into a security sensor output.