Proactive threat detection for connected cars using recursive Bayesian estimation

al-Khateeb, H., Epiphaniou, G., Reviczky, A., Karadimas, P. and Heidari, H. (2017) Proactive threat detection for connected cars using recursive Bayesian estimation. IEEE Sensors Journal, 18(12), pp. 4822-4831. (doi: 10.1109/JSEN.2017.2782751)

153599.pdf - Accepted Version



Upcoming disruptive technologies around autonomous driving of connected cars have not yet been matched with appropriate security by design principles and lack approaches to incorporate proactive preventative measures in the wake of increased cyber-threats against such systems. In this paper, we introduce proactive anomaly detection to a use-case of hijacked connected cars to improve cyber-resilience. Firstly, we manifest the opportunity of behavioural profiling for connected cars from recent literature covering related underpinning technologies. Then, we design and utilise a new dataset file for connected cars influenced by the Automatic Dependent Surveillance – Broadcast (ADS–B) surveillance technology used in the aerospace industry to facilitate data collection and sharing. Finally, we simulate the analysis of travel routes in real-time to predict anomalies using predictive modelling. Simulations show the applicability of a Bayesian estimation technique, namely Kalman Filter. With the analysis of future state predictions based on the previous behaviour, cyber-threats can be addressed with a vastly increased time-window for a reaction when encountering anomalies. We discuss that detecting real-time deviations for malicious intent with predictive profiling and behavioural algorithms can be superior in effectiveness than the retrospective comparison of known-good/known-bad behaviour. When quicker action can be taken while connected cars encounter cyber-attacks, more effective engagement or interception of command and control will be achieved.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Karadimas, Dr Petros and Heidari, Professor Hadi
Authors: al-Khateeb, H., Epiphaniou, G., Reviczky, A., Karadimas, P., and Heidari, H.
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Sensors Journal
ISSN (Online):1558-1748
Published Online:12 December 2017
Copyright Holders:Copyright © 2017 Crown Copyright
First Published:First published in IEEE Sensors Journal 2017 18:4822-4831
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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