Open Access

ARTICLE

A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

Arif Hussain Magsi^1,*, Ali Ghulam², Saifullah Memon¹, Khalid Javeed³, Musaed Alhussein⁴, Imad Rida⁵

1 State Key Laboratory of Networking and Switching Technology, Beijing University of Post and Telecommunication, Beijing, 100876, China
2 Information Technology Center, Sindh Agriculture University, Tandojam, 70050, Pakistan
3 Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, 27272, United Arab Emirate
4 4Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P. O. Box 51178, Riyadh, 11543, Saudi Arabia
5 Laboratory Biomechanics and Bioengineering, University of Technology of Compiegne, Compiegne, 60200, France

* Corresponding Author: Arif Hussain Magsi. Email:

(This article belongs to the Special Issue: Innovations in Pervasive Computing and Communication Technologies)

Computers, Materials & Continua 2023, 77(2), 1445-1465. https://doi.org/10.32604/cmc.2023.040290

Received 13 March 2023; Accepted 13 June 2023; Issue published 29 November 2023

Abstract

Named Data Networking (NDN) is gaining a significant attention in Vehicular Ad-hoc Networks (VANET) due to its in-network content caching, name-based routing, and mobility-supporting characteristics. Nevertheless, existing NDN faces three significant challenges, including security, privacy, and routing. In particular, security attacks, such as Content Poisoning Attacks (CPA), can jeopardize legitimate vehicles with malicious content. For instance, attacker host vehicles can serve consumers with invalid information, which has dire consequences, including road accidents. In such a situation, trust in the content-providing vehicles brings a new challenge. On the other hand, ensuring privacy and preventing unauthorized access in vehicular (VNDN) is another challenge. Moreover, NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in VNDN. In this connection, our contribution is threefold. Unlike existing rule-based reputation evaluation, we propose a Machine Learning (ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate nodes. Based on ML evaluation results, vehicles accept or discard served content. Secondly, we exploit a decentralized blockchain system to ensure vehicles’ privacy by maintaining their information in a secure digital ledger. Finally, we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe (Pub-Sub) approach. We implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection (BurST-ADMA). We used five (05) hybrid ML classifiers, including Logistic Regression, Decision Tree, K-Nearest Neighbors, Random Forest, and Gaussian Naive Bayes. The qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%. Our proposed research offers the most accurate solution to detect CPA in VNDN for safe, secure, and reliable vehicle communication.

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Keywords

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