Information fusion-based cybersecurity threat detection for intelligent transportation system

Conference paper


Chowdhury, A., Naha, R., Kaisar, S., Khoshkholghi, A., Ali, K. and Galletta, A. 2023. Information fusion-based cybersecurity threat detection for intelligent transportation system. CCGridW: 4th Workshop on Secure IoT, Edge and Cloud Systems (SioTEC) 2023. Bangalore, India 01 - 04 May 2023 Bangalore, India IEEE. pp. 96-103 https://doi.org/10.1109/CCGridW59191.2023.00029
TypeConference paper
TitleInformation fusion-based cybersecurity threat detection for intelligent transportation system
AuthorsChowdhury, A., Naha, R., Kaisar, S., Khoshkholghi, A., Ali, K. and Galletta, A.
Abstract

Intelligent Transportation Systems (ITS) are sophisticated systems that leverage various technologies to increase the safety, efficiency, and sustainability of transportation. By relying on wireless communication and data collected from diverse sensors, ITS is vulnerable to cybersecurity threats. With the increasing number of attacks on ITS worldwide, detecting and addressing cybersecurity threats has become critically important. This need will only intensify with the impending arrival of autonomous vehicles. One of the primary challenges is identifying critical ITS assets that require protection and understanding the vulnerabilities that cyber attackers can exploit. Additionally, creating a standard profile for ITS is challenging due to the dynamic traffic pattern, which exhibits changes in the movement of vehicles over time. To address these challenges, this paper proposes an information fusion-based cybersecurity threat detection method. Specifically, we employ the Kalman filter for noise reduction, Dempster-Shafer decision theory and Shannon’s entropy for assessing the probabilities of traffic conditions being normal, intruded, and uncertain. We utilised Simulation of Urban Mobility (SUMO) to simulate the Melbourne CBD map and historical traffic data from the Victorian transport authority. Our simulation results reveal that information fusion with three sensor data is more effective in detecting normal traffic conditions. On the other hand, for detecting anomalies, information fusion with two sensor data is more efficient.

KeywordsInformation fusion; Cybersecurity; Intelligent Transport Systems; Threat Detection; Transportation; Sensor fusion; Data models; Threat assessment; Entropy; Kalman filters; Computer security
Middlesex University ThemeCreativity, Culture & Enterprise
LanguageEnglish
ConferenceCCGridW: 4th Workshop on Secure IoT, Edge and Cloud Systems (SioTEC) 2023
Page range96-103
Proceedings Title2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)
ISBN
Electronic9798350302080
Paperback9798350302097
PublisherIEEE
Place of publicationBangalore, India
Publication dates
Print01 May 2023
Online19 Jul 2023
Publication process dates
Deposited29 Mar 2023
Accepted07 Mar 2023
Output statusPublished
Accepted author manuscript
Copyright Statement

© 2023 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.

Digital Object Identifier (DOI)https://doi.org/10.1109/CCGridW59191.2023.00029
Web of Science identifierWOS:001037081200014
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/10171437/proceeding
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