From Challenges to Opportunities: A Comprehensive Study of AI-based In-Vehicle Intrusion Detection Systems - IRT SystemX Access content directly
Conference Papers Year : 2024

From Challenges to Opportunities: A Comprehensive Study of AI-based In-Vehicle Intrusion Detection Systems

Elies Gherbi
  • Function : Author
  • PersonId : 1064617
Hamza Khemissa
  • Function : Author
Mohammed Bouchouia
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  • PersonId : 1308091
  • IdRef : 272872520
Maxime Ayrault
  • Function : Author
  • PersonId : 1361402
  • IdRef : 276288610

Abstract

While significant research has been conducted on ML-based In-Vehicle Intrusion Detection Systems (IV-IDS), the practical application of these systems needs further refinement. The safety-critical nature of IV-IDS calls for precise and objective evaluation and feasibility assessment metrics. This paper responds to this need by conducting a rigorous ML-based IVIDS analysis. We offer a thorough review of recent automotive forensics studies spotlighting the constraints relevant to Invehicles networks and the associated security/safety requirements to reveal the current gaps in the existing literature. By addressing the limitations of AI in IV-IDS, this paper contributes to the existing research corpus and defines pertinent baseline metrics for in-vehicle networked systems. Essentially, we reconcile the requirements of real-world autonomous vehicles with those of the security domain, enabling an assessment of the viability of AI-based intrusion detection systems.
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hal-04391947 , version 1 (12-01-2024)

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  • HAL Id : hal-04391947 , version 1

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Elies Gherbi, Hamza Khemissa, Mohammed Bouchouia, Maxime Ayrault. From Challenges to Opportunities: A Comprehensive Study of AI-based In-Vehicle Intrusion Detection Systems. IEEE Consumer Communications & Networking Conference (CCNC), Jan 2024, Las vegas, United States. ⟨hal-04391947⟩

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