Proactive Threat Detection for Connected Cars Using Recursive Bayesian Estimation

Haider Al-Khateeb, Gregory Epiphaniou, Adam Reviczky, Petros Karadimas, Hadi Heidari

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)4822 - 4831
Number of pages9
JournalIEEE Sensors Journal
Volume18
Issue number12
Early online date12 Dec 2017
DOIs
Publication statusPublished - 15 Jun 2018

Bibliographical note

© 2017 Crown Copyright. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing pubs-permissions@ieee.org.

Keywords

  • Connected cars
  • cyber physical systems
  • cyber threat
  • proactive detection
  • Bayesian estimation
  • Kalman filter

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