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A New Modified MD5-224 Bits Hash Function and an Efficient Message Authentication Code Based on Quasigroups Read chapter Read first chapter

2022 | OriginalPaper | Chapter

Edge Computing-Based DDoS Attack Detection for Intelligent Transportation Systems

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

Published in: Cyber Security, Privacy and Networking

Publisher: Springer Nature Singapore

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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. precision, accuracy, f1 measure, and recall are used to assess the performance of different machine learning methods

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previous chapter Supervised Machine Learning Algorithms Based on Classification for Detection of Distributed Denial of Service Attacks in SDN-Enabled Cloud Computing
next chapter An Empirical Study of Secure and Complex Variants of RSA Scheme
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Metadata
Title
Edge Computing-Based DDoS Attack Detection for Intelligent Transportation Systems
Authors
Akshat Gaurav
B. B. Gupta
Kwok Tai Chui
Copyright Year
2022
Publisher
Springer Nature Singapore
Book
Cyber Security, Privacy and Networking

Print ISBN: 978-981-16-8663-4

Electronic ISBN: 978-981-16-8664-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-981-16-8664-1_16