Adversarial Attack of ML-based Intrusion Detection System on In-vehicle System using GAN

Eun Seong Seo, Jeong Eun Kim, Wook Lee, Junhee Seok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, research has focused on developing intrusion detection systems (IDS) within vehicle networks to prevent automotive hacking from external cyberattacks. While machine learning (ML) techniques have shown promise in detecting known attacks, their vulnerability to adversarial examples remains a significant challenge. In this study, we introduce a Generative Adversarial Network (GAN)-based method for creating adversarial attacks capable of bypassing ML-based IDS in in-vehicle networks. Our approach involves preprocessing an automotive hacking dataset, training a GAN-based model, and evaluating the generated attacks using accuracy metrics. The results demonstrate that adversarial attacks effectively reduce the detection accuracy of various IDSs to less than 50%, emphasizing the importance of addressing adversarial cases when designing and evaluating ML-based IDSs for in-vehicle networks. Additionally, t-SNE visualization reveals the successful generation of new adversarial attacks, highlighting the need for ongoing research to strengthen the security of in-vehicle systems.

Original languageEnglish
Title of host publicationICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Pages700-703
Number of pages4
ISBN (Electronic)9798350335385
DOIs
Publication statusPublished - 2023
Event14th International Conference on Ubiquitous and Future Networks, ICUFN 2023 - Paris, France
Duration: 2023 Jul 42023 Jul 7

Publication series

NameInternational Conference on Ubiquitous and Future Networks, ICUFN
Volume2023-July
ISSN (Print)2165-8528
ISSN (Electronic)2165-8536

Conference

Conference14th International Conference on Ubiquitous and Future Networks, ICUFN 2023
Country/TerritoryFrance
CityParis
Period23/7/423/7/7

Bibliographical note

Funding Information:
This work was supported by a grant from the National Research Foundation of Korea (NRF-2022R1A2C2004003)

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Adversarial attack
  • GAN
  • In-vehicle networks
  • Intrusion detection system
  • Machine Learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

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