@article{ART003093039},
author={Hyo-eun Kang},
title={ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder},
journal={Journal of Software Assessment and Valuation},
issn={2092-8114},
year={2024},
volume={20},
number={2},
pages={53-65}
TY - JOUR
AU - Hyo-eun Kang
TI - ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder
JO - Journal of Software Assessment and Valuation
PY - 2024
VL - 20
IS - 2
PB - Korea Software Assessment and Valuation Society
SP - 53
EP - 65
SN - 2092-8114
AB - 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.
KW - intrusion detection system;in-vehicle network security;control area network bus;autoencoder;deep learning
DO -
UR -
ER -
Hyo-eun Kang. (2024). ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder. Journal of Software Assessment and Valuation, 20(2), 53-65.
Hyo-eun Kang. 2024, "ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder", Journal of Software Assessment and Valuation, vol.20, no.2 pp.53-65.
Hyo-eun Kang "ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder" Journal of Software Assessment and Valuation 20.2 pp.53-65 (2024) : 53.
Hyo-eun Kang. ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder. 2024; 20(2), 53-65.
Hyo-eun Kang. "ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder" Journal of Software Assessment and Valuation 20, no.2 (2024) : 53-65.
Hyo-eun Kang. ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder. Journal of Software Assessment and Valuation, 20(2), 53-65.
Hyo-eun Kang. ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder. Journal of Software Assessment and Valuation. 2024; 20(2) 53-65.
Hyo-eun Kang. ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder. 2024; 20(2), 53-65.
Hyo-eun Kang. "ST-AAE: Intrusion Detection in In-Vehicle Networks Using Spatio-Temporal Adversarial Autoencoder" Journal of Software Assessment and Valuation 20, no.2 (2024) : 53-65.
