A Real-time Explainable Anomaly Detection System for Connected Vehicles

Duc Cuong Nguyen, Kien Dang Nguyen, Simy Chacko

2022

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.

Paper Citation

in Harvard Style

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 - Volume 1: IoTBDS, ISBN 978-989-758-564-7, pages 17-25. DOI: 10.5220/0010968500003194

in Bibtex Style

@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 - Volume 1: IoTBDS,},
year={2022},
pages={17-25},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010968500003194},
isbn={978-989-758-564-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - A Real-time Explainable Anomaly Detection System for Connected Vehicles
SN - 978-989-758-564-7
AU - Nguyen D.
AU - Nguyen K.
AU - Chacko S.
PY - 2022
SP - 17
EP - 25
DO - 10.5220/0010968500003194