A Real-time Method for CAN Bus Intrusion Detection by Means of Supervised Machine Learning

Francesco Mercaldo, Francesco Mercaldo, Rosangela Casolare, Giovanni Ciaramella, Giacomo Iadarola, Fabio Martinelli, Francesco Ranieri, Antonella Santone

2022

Abstract

Nowadays vehicles are not composed only of mechanical parts, exits a plethora of electronics components in our cars, able to exchange information. The protection devices such as the airbags are activated electronically. This happens because the braking or acceleration signal from the pedal to the actuator arrives through a packet. The latter is an electronic and not a mechanical signal. For packets transmission a bus, i.e., the Controller Area Network, was designed and implemented in vehicles. This bus was not designed to receive access from the outside world, which happened when info-entertainment systems were introduced, opening up the possibility of accessing bus information from devices external to the vehicle. To avoid the possibility of those attacks, in this research article, we propose a method aimed to detect intrusions targeting the CAN bus. In particular, we analyze packets transiting through the CAN bus, and we build a set of models by exploiting supervised machine learning. We experiment with the proposed method on three different attacks (i.e., speedometer attack, arrows attack, and doors attack), obtaining interesting performances.

Paper Citation

in Harvard Style

Mercaldo F., Casolare R., Ciaramella G., Iadarola G., Martinelli F., Ranieri F. and Santone A. (2022). A Real-time Method for CAN Bus Intrusion Detection by Means of Supervised Machine Learning. In Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT, ISBN 978-989-758-590-6, pages 534-539. DOI: 10.5220/0011267500003283

in Bibtex Style

@conference{secrypt22,
author={Francesco Mercaldo and Rosangela Casolare and Giovanni Ciaramella and Giacomo Iadarola and Fabio Martinelli and Francesco Ranieri and Antonella Santone},
title={A Real-time Method for CAN Bus Intrusion Detection by Means of Supervised Machine Learning},
booktitle={Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,},
year={2022},
pages={534-539},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011267500003283},
isbn={978-989-758-590-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,
TI - A Real-time Method for CAN Bus Intrusion Detection by Means of Supervised Machine Learning
SN - 978-989-758-590-6
AU - Mercaldo F.
AU - Casolare R.
AU - Ciaramella G.
AU - Iadarola G.
AU - Martinelli F.
AU - Ranieri F.
AU - Santone A.
PY - 2022
SP - 534
EP - 539
DO - 10.5220/0011267500003283