A Real-time Explainable Anomaly Detection System for Connected Vehicles

Duc Cuong Nguyen; Kien Dang Nguyen; Simy Chacko

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Paper

A Real-time Explainable Anomaly Detection System for Connected Vehicles

Topics: Intrusion and detection techniques; Security, Privacy and Trust

In Proceedings of the 7th International Conference on Internet of Things, Big Data and Security IoTBDS - Volume 1, 17-25, 2022

Authors: Duc Cuong Nguyen ¹ ; Kien Dang Nguyen ¹ and Simy Chacko ²

Affiliations: ¹ HCL Vietnam, Vietnam ; ² HCL Technologies, India

Keyword(s): Automotive Security, Anomaly Detection, Explainable AI, Deep Learning, Connected Vehicle.

Abstract: Anomaly detection is one of the key factors to identify and prevent attacks on connected vehicles. It makes cars more secure and safer to use in the new era of connectivity. In this paper, we propose a real-time explainable deep learning-based anomaly detection system that effectively identifies anomalous activities in connected vehicles. Our approach provides real-time alerts for on-the-road connected vehicles with clear output that makes it easily comprehensible. By evaluating our approach on a simulated driving environment, we can showcase its effectiveness (AUC value of 0.95) and provide insights on different attack scenarios that would threaten the safety of car users.

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Paper citation in several formats:

Nguyen, D.; Nguyen, K. and Chacko, S. (2022). A Real-time Explainable Anomaly Detection System for Connected Vehicles. In Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - IoTBDS; ISBN 978-989-758-564-7; ISSN 2184-4976, SciTePress, pages 17-25. DOI: 10.5220/0010968500003194

@conference{iotbds22,
author={Duc Cuong Nguyen. and Kien Dang Nguyen. and Simy Chacko.},
title={A Real-time Explainable Anomaly Detection System for Connected Vehicles},
booktitle={Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - IoTBDS},
year={2022},
pages={17-25},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010968500003194},
isbn={978-989-758-564-7},
issn={2184-4976},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - IoTBDS
TI - A Real-time Explainable Anomaly Detection System for Connected Vehicles
SN - 978-989-758-564-7
IS - 2184-4976
AU - Nguyen, D.
AU - Nguyen, K.
AU - Chacko, S.
PY - 2022
SP - 17
EP - 25
DO - 10.5220/0010968500003194
PB - SciTePress