Edge Computing-Based DDoS Attack Detection for Intelligent Transportation Systems

Akshat Gaurav, B. B. Gupta, Kwok Tai Chui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Vehicular ad hoc networks (VANETs) are a critical component of intelligent transportation systems (ITS). Because VANET allows the transmission of critical and life-saving information between vehicle nodes, any effort to compromise the network should be recognized immediately, if at all feasible. The distributed denial-of-service (DDoS) assault is one kind of cyber-attack that affects VANET systems’ availability. As a consequence of the DDoS assault, vehicle nodes are unable to transmit vital information. In this context, this experiment proposed edge computing-based DDoS detection techniques. The proposed technique uses packet entropy to distinguish DDoS attack traffic from normal communication. To determine the entropy values, we performed an in-depth study of five different machine learning methods.

Original languageEnglish
Title of host publicationCyber Security, Privacy and Networking - Proceedings of ICSPN 2021
EditorsDharma P. Agrawal, Nadia Nedjah, B. B. Gupta, Gregorio Martinez Perez
Pages175-184
Number of pages10
DOIs
Publication statusPublished - 2022
EventInternational Conference on Cyber Security, Privacy and Networking, ICSPN 2021 - Virtual, Online
Duration: 17 Sept 202119 Sept 2021

Publication series

NameLecture Notes in Networks and Systems
Volume370
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceInternational Conference on Cyber Security, Privacy and Networking, ICSPN 2021
CityVirtual, Online
Period17/09/2119/09/21

Keywords

  • Entropy
  • Machine learning
  • Side channel attacks
  • VANET

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