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Hybrid intrusion detection in connected self-driving vehicles

Alheeti, Khattab M Ali and McDonald-Maier, Klaus (2016) Hybrid intrusion detection in connected self-driving vehicles. In: 2016 22nd International Conference on Automation and Computing (ICAC), 2016-09-07 - 2016-09-08, Colchester.

Hybrid Intrusion Detection Accepted.pdf - Accepted Version

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Emerging self-driving vehicles are vulnerable to different attacks due to the principle and the type of communication systems that are used in these vehicles. These vehicles are increasingly relying on external communication via vehicular ad hoc networks (VANETs). VANETs add new threats to self-driving vehicles that contribute to substantial challenges in autonomous systems. These communication systems render self-driving vehicles vulnerable to many types of malicious attacks, such as Sybil attacks, Denial of Service (DoS), black hole, grey hole and wormhole attacks. In this paper, we propose an intelligent security system designed to secure external communications for self-driving and semi self-driving cars. The proposed scheme is based on Proportional Overlapping Score (POS) to decrease the number of features found in the Kyoto benchmark dataset. The hybrid detection system relies on the Back Propagation neural networks (BP), to detect a common type of attack in VANETs: Denial-of-Service (DoS). The experimental results show that the proposed BP-IDS is capable of identifying malicious vehicles in self-driving and semi self-driving vehicles.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2016 22nd International Conference on Automation and Computing (ICAC)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 27 Feb 2017 16:46
Last Modified: 22 Jul 2020 11:00

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