Domain-Specific Anomaly Detection for In-Vehicle Networks

Edy Kristianto*, Po Ching Lin, Ren Hung Hwang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Connecting components such as electronic control units (ECUs) via an in-vehicle network (IVN) is common in modern vehicles. However, if compromised, the components may send malicious messages to impact the operations of a vehicle and even hurt driving safety. Several in-vehicle intrusion detection system (IDS) solutions based on machine learning have been presented in the literature to detect unknown attacks. Such IDSs are deployed on a central gateway or within each ECU. We note that some vehicles have implemented domain gateways/controllers and automotive Ethernet to support the increasing bandwidth and complexity of the IVN. The domain gateways can take over the computation load from the ECUs. Therefore, each domain gateway can be a promising place to implement an IDS to detect and block malicious messages in its domain. We can optimize the domain-specific IDS model to classify malicious or normal messages in each domain and make it lightweight. In this work, we present two models of lightweight unsupervised IDS solutions for the domain gateway model. Our designs have only 2,708 and 49,454 parameters, fewer than the state-of-the-art designs. Their training and testing time are also shorter, achieving high accuracy from 0.90 to 1.00 in detecting the malicious messages on each domain gateway.

Original languageEnglish
Title of host publicationNew Trends in Computer Technologies and Applications - 25th International Computer Symposium, ICS 2022, Proceedings
EditorsSun-Yuan Hsieh, Ling-Ju Hung, Sheng-Lung Peng, Ralf Klasing, Chia-Wei Lee
PublisherSpringer Science and Business Media Deutschland GmbH
Pages637-648
Number of pages12
ISBN (Print)9789811995811
DOIs
StatePublished - 2022
Event25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022 - Taoyuan, Taiwan
Duration: 15 Dec 202217 Dec 2022

Publication series

NameCommunications in Computer and Information Science
Volume1723 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022
Country/TerritoryTaiwan
CityTaoyuan
Period15/12/2217/12/22

Keywords

  • CAN bus
  • Domain-specific
  • In-vehicle intrusion detection

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