al-Khateeb, Haider, Epiphaniou, Gregory, Reviczky, Adam, Karadimas, Petros and Heidari, Hadi (2018) Proactive threat detection for connected cars using recursive Bayesian estimation. IEEE Sensors Journal, 18 (12). pp. 4822-4831. doi:10.1109/JSEN.2017.2782751 ISSN 1530-437X.

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Abstract

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. First, we manifest the opportunity of behavioral profiling for connected cars from recent literature covering related underpinning technologies. Then, we design and utilize a new data set file for connected cars influenced by the automatic dependent surveillance-broadcast 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 modeling. Simulations show the applicability of a Bayesian estimation technique, namely, Kalman filter. With the analysis of future state predictions based on the previous behavior, 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 the predictive profiling and behavioral algorithms can be superior in effectiveness than the retrospective comparison of known-good/known-bad behavior. When quicker action can be taken while connected cars encounter cyberattacks, more effective engagement or interception of command and control will be achieved.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TE Highway engineering. Roads and pavements T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions:	Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Automated vehicles, Driver assistance systems, Cyber intelligence (Computer security), Intelligent transportation systems, Vehicular ad hoc networks (Computer networks), Embedded Internet devices, Automated vehicles -- Security measures
Journal or Publication Title:	IEEE Sensors Journal
Publisher:	Institute of Electrical and Electronic Engineers
ISSN:	1530-437X
Official Date:	12 December 2018
Dates:	Date Event 12 December 2018 Published 5 December 2018 Accepted
Volume:	18
Number:	12
Page Range:	pp. 4822-4831
DOI:	10.1109/JSEN.2017.2782751
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	30 April 2020
Date of first compliant Open Access:	30 April 2020

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