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dc.contributor.authoral-Khateeb, Haider
dc.contributor.authorEpiphaniou, Gregory
dc.contributor.authorReviczky, Adam
dc.contributor.authorKaradimas, Petros
dc.contributor.authorHeidari, Hadi
dc.date.accessioned2018-02-01T09:02:37Z
dc.date.available2018-02-01T09:02:37Z
dc.date.issued2017-12-12
dc.identifier.citational-Khateeb, H., Epiphaniou, G., Reviczky, A., Karadimas, P. and Heidari, H. (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
dc.identifier.issn1530-437X
dc.identifier.doi10.1109/JSEN.2017.2782751
dc.identifier.urihttp://hdl.handle.net/2436/621062
dc.description.abstractUpcoming 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.
dc.language.isoen
dc.publisherIEEE
dc.relation.urlhttp://ieeexplore.ieee.org/document/8187601/
dc.subjectConnected Cars
dc.subjectCyber Physical Systems
dc.subjectCyber Threat
dc.subjectProactive Detection
dc.subjectBayesian Estimation
dc.subjectKalman Filter
dc.titleProactive threat detection for connected cars using recursive Bayesian estimation
dc.typeJournal article
dc.identifier.journalIEEE Sensors Journal
dc.date.accepted2017-12-01
rioxxterms.funderUniversity of Wolverhampton
rioxxterms.identifier.projectUoW01022018
rioxxterms.versionAM
rioxxterms.licenseref.urihttps://creativecommons.org/CC BY-NC-ND 4.0
rioxxterms.licenseref.startdate2019-12-12
dc.source.volume18
dc.source.issue12
dc.source.beginpage4822
dc.source.endpage4831
refterms.dateFCD2018-10-19T09:28:38Z
refterms.versionFCDAM
refterms.dateFOA2018-12-12T00:00:00Z
html.description.abstractUpcoming 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.


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