Detection of blackhole and DDoS attack in 5g VANET using multiple machine learning algorithms

Shabbar, Burhanuddin (2024) Detection of blackhole and DDoS attack in 5g VANET using multiple machine learning algorithms. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (564kB) | Preview
Preview	PDF (Configuration Manual) Download (1MB) | Preview

Abstract

In an era of fast innovation of intelligent transportation systems, VANETs prove very crucial for road safety and efficiency by vehicle-to-vehicle and vehicle-to-infrastructure communication. However, the 5G integration exposes VANET to sophisticated cyber threats such as DDoS and blackhole attacks while offering high-speed, low-latency communication. This paper presents the detection and mitigation of such attacks in 5G-enabled VANETs through multiple machine learning algorithms. The simulation environment for a VANET is performed with OMNeT++ and the Veins framework, modelling real traffic conditions of the world to realize how robust the designed network could be against an attack. In this research, generated simulation data is used in training and evaluating machine learning models, some of which are Decision Trees, Logistic Regression, Support Vector Machines, and Neural Networks with the benchmark datasets NSL-KDD. This research was able to illustrate how these were very instrumental models in the identification and mitigation of network attacks and provided a comparative analysis to prior approaches. Results show the great potential of machine learning in improving VANET security by providing robust detection mechanisms that could integrate in real-world scenarios for protection against critical transportation infrastructures. Furthermore, mitigation strategies are foreseen, among them such as rate limiting and traffic filtering, to reduce the impact of the detected attacks and therefore provide reliability and safety in VANET communications.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Hafeez, Khadija UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	31 Jul 2025 09:02
Last Modified:	31 Jul 2025 09:02
URI:	https://norma.ncirl.ie/id/eprint/8370

Actions (login required)

View Item