Anomaly Detection for In-Vehicle Communication Using Transformers

Victor Cobilean, Harindra S. Mavikumbure, Chathurika S. Wickramasinghe, Benny J. Varghese, Timothy Pennington, Milos Manic

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

4 Scopus citations

Abstract

With the advancements of modern vehicle infrastructures, vehicles are increasingly relying on the signals received from a vast number of sensors and electronic components. Wireless technologies enable communication between vehicles and infrastructure, but it also increase the vulnerability surface. Malicious actors can remotely disrupt the vehicle's normal behavior, causing vehicle damage or worse, putting human lives in danger. To address these challenges, this paper proposes a transformer neural network-based intrusion detection system (CAN-Former IDS) that predicts anomalous behavior within the CAN protocol communication. Previous work typically addresses the prediction over the sequence of the CAN IDs. In this paper, we will simultaneously analyze both the sequence of IDs and the message payload values. The advantages of our approach are: 1) fully self-supervised training, which does not require labeled data, 2) self learning interactions between input tokens without relying on hand-crafted features. The transformer neural network is trained to predict the next communication sequence and anomalous communication is identified by comparing the real sequence to the predicted expected sequence. We evaluated our approach using a publicly available data set known as survival analysis data set, containing CAN communication from three different cars.

Original languageEnglish
Title of host publicationIECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
Pages1-6
Number of pages6
ISBN (Electronic)9798350331820
ISBN (Print)9798350331820
DOIs
StatePublished - 2023
Event49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 - Singapore, Singapore
Duration: Oct 16 2023Oct 19 2023

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
ISSN (Print)2162-4704
ISSN (Electronic)2577-1647

Conference

Conference49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Country/TerritorySingapore
CitySingapore
Period10/16/2310/19/23

Keywords

  • Anomaly Detection
  • Deep Learning
  • In-Vehicle Communications
  • Transformer

INL Publication Number

  • INL/CON-23-74422
  • 161800

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