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A Multi-Layer Intrusion Detection System for SOME/IP-Based In-Vehicle Network.

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Summary

This study introduces a multi-layer intrusion detection system (IDS) for securing automotive networks using Scalable service-Oriented MiddlewarE over IP (SOME/IP). The system achieves high accuracy in detecting cyber threats within vehicle communication systems.

Keywords:
SOME/IPdeep learningintrusion detection system

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Area of Science:

  • Cybersecurity
  • Automotive Engineering
  • Network Protocols

Background:

  • Automotive Ethernet is replacing Controller Area Network (CAN) as the primary in-vehicle network (IVN).
  • Scalable service-Oriented MiddlewarE over IP (SOME/IP) is crucial for service-based communication in modern vehicles.
  • Vulnerabilities in SOME/IP and increased external interfaces pose significant cybersecurity risks to IVNs.

Purpose of the Study:

  • To propose a novel multi-layer Intrusion Detection System (IDS) architecture for SOME/IP-based IVNs.
  • To develop a method for establishing a SOME/IP dataset for IDS performance evaluation.
  • To assess the effectiveness and efficiency of the proposed IDS in detecting intrusions.

Main Methods:

  • A hybrid IDS architecture combining rule-based and Artificial Intelligence (AI)-based modules.
  • Rule-based detection focusing on SOME/IP headers, service discovery messages, message intervals, and communication flows.
  • AI-based detection targeting the payload of SOME/IP messages.
  • Creation of a dedicated SOME/IP dataset for empirical validation.

Main Results:

  • Rule-based detection achieved 100% accuracy, while AI-based detection reached 99.7761% accuracy.
  • Average packet detection times of 0.3958 ms with GPU acceleration and 0.6669 ms with CPU.
  • Demonstrated feasibility for deployment in distributed or centralized configurations for critical Advanced Driving Assistance System (ADAS) traffic.

Conclusions:

  • The proposed multi-layer IDS effectively detects intrusions in SOME/IP IVNs with high accuracy and low latency.
  • The system is suitable for real-time vehicle-level deployment, enhancing the security of connected vehicles.
  • The developed SOME/IP dataset aids future research in automotive network security.