A Content Poisoning Attack Detection and Prevention System in Vehicular Named Data Networking

Arif Hussain Magsi, Leanna Vidya Yovita, Ali Ghulam, Ghulam Muhammad () and Zulfiqar Ali
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Arif Hussain Magsi: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Leanna Vidya Yovita: School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia
Ali Ghulam: Information Technology Center, Sindh Agriculture University, Tandojam 70060, Pakistan
Ghulam Muhammad: Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Zulfiqar Ali: School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK

Sustainability, 2023, vol. 15, issue 14, 1-18

Abstract: Named data networking (NDN) is gaining momentum in vehicular ad hoc networks (VANETs) thanks to its robust network architecture. However, vehicular NDN (VNDN) faces numerous challenges, including security, privacy, routing, and caching. Specifically, the attackers can jeopardize vehicles’ cache memory with a Content Poisoning Attack (CPA). The CPA is the most difficult to identify because the attacker disseminates malicious content with a valid name. In addition, NDN employs request–response-based content dissemination, which is inefficient in supporting push-based content forwarding in VANET. Meanwhile, VNDN lacks a secure reputation management system. To this end, our contribution is three-fold. We initially propose a threshold-based content caching mechanism for CPA detection and prevention. This mechanism allows or rejects host vehicles to serve content based on their reputation. Secondly, we incorporate a blockchain system that ensures the privacy of every vehicle at roadside units (RSUs). Finally, we extend the scope of NDN from pull-based content retrieval to push-based content dissemination. The experimental evaluation results reveal that our proposed CPA detection mechanism achieves a 100% accuracy in identifying and preventing attackers. The attacker vehicles achieved a 0% cache hit ratio in our proposed mechanism. On the other hand, our blockchain results identified tempered blocks with 100% accuracy and prevented them from storing in the blockchain network. Thus, our proposed solution can identify and prevent CPA with 100% accuracy and effectively filters out tempered blocks. Our proposed research contribution enables the vehicles to store and serve trusted content in VNDN.

Keywords: vehicular network; named data networking; blockchain; content poisoning attack (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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