Aurangzeb, Sana, Aleem, Muhammad, Khan, Muhammad Taimoor ORCID: 0000-0002-5752-6420 , Anwar, Haris and Siddique, Muhammad Shaoor (2023) Cybersecurity for autonomous vehicles against malware attacks in smart-cities. Cluster Computing. ISSN 1386-7857 (Print), 1573-7543 (Online) (doi:https://doi.org/10.1007/s10586-023-04114-7)

Preview

PDF (Publisher VoR) 44380_KHAN_Cybersecurity_for_autonomous_vehicles_against_malware_attacks_in_smart_cities.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

Abstract

Smart Autonomous Vehicles (AVSs) are networks of Cyber-Physical Systems (CPSs) in which they wirelessly communicate with other CPSs sub-systems (e.g., smart -vehicles and smart-devices) to efficiently and securely plan safe travel. Due to unreliable wireless communication among them, such vehicles are an easy target of malware attacks that may compromise vehicles’ autonomy, increase inter-vehicle communication latency, and drain vehicles’ power. Such compromises may result in traffic congestion, threaten the safety of passengers, and can result in financial loss. Therefore, real-time detection of such attacks is key to the safe smart transportation and Intelligent Transport Systems (ITSs). Current approaches either employ static analysis or dynamic analysis techniques to detect such attacks. However, these approaches may not detect malware in real-time because of zero-day attacks and huge computational resources. Therefore, we introduce a hybrid approach that combines the strength of both analyses to efficiently detect malware for the privacy of smart-cities.

Item Type:	Article
Uncontrolled Keywords:	malware detection; security; smart cities; autonomous systems
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	07 Feb 2024 10:13
URI:	http://gala.gre.ac.uk/id/eprint/44380

Actions (login required)

View Item

Downloads