ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder (ST-AAE: 시공간 적대적 오토인코더를 사용한 차량 내부 네트워크 침입 탐지)

Hyo-Eun Kang (강효은)

doi:10.29056/jsav.2024.06.07

ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder

Journal of Software Assessment and Valuation
Abbr : JSAV
2024, 20(2), pp.53~65
DOI : 10.29056/jsav.2024.06.07
Publisher : Korea Software Assessment and Valuation Society
Research Area : Engineering > Computer Science
Received : June 1, 2024
Accepted : June 20, 2024
Published : June 30, 2024

Hyo-Eun Kang ¹

¹스마트앰루앰

Accredited

ABSTRACT

The Controller Area Network (CAN), the in-vehicle network, is highly vulnerable to malicious physical and cyber attacks. To ensure the safety of both users and vehicle providers, appropriate security measures are necessary. This paper proposes a novel intrusion detection system (IDS) called Spatio-Temporal Adversarial Autoencoder (ST-AAE) for in-vehicle networks. ST-AAE utilizes the spatio-temporal characteristics of CAN traffic, a new approach in the field of in-vehicle IDS. The framework detects abnormal traffic and classifies attack types. Experimental results on real-world driving data show that ST-AAE outperforms existing models with high accuracy and low false positive rates across various attack types. Its performance is due to effective spatio-temporal feature learning, enhanced generalization through adversarial learning, and integration of CAN ID and payload information. These findings suggest that ST-AAE is an effective solution for automotive network security.

KEYWORDS

intrusion detection system, in-vehicle network security, control area network bus, autoencoder, deep learning

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* References for papers published after 2024 are currently being built.

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Journal of Software Assessment and Valuation 2024 KCI Impact Factor : 0.32

ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder

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* References for papers published after 2024 are currently being built.

Journal of Software Assessment and Valuation 2024 KCI Impact Factor : 0.32

ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder

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* References for papers published after 2024 are currently being built.