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Erschienen in:

2024 | OriginalPaper | Buchkapitel

Syn Flood DDoS Attack Detection with Different Multilayer Perceptron Optimization Techniques Using Uncorrelated Feature Subsets Selected by Different Correlation Methods

verfasst von : Nagaraju Devarakonda, Kishorebabu Dasari

Erschienen in: High Performance Computing, Smart Devices and Networks

Verlag: Springer Nature Singapore

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Abstract

Cyber attackers widely used Distributed Denial of Service (DDoS) attacks to saturate servers with network traffic, preventing authorized clients to access network resources and ensuing massive losses in all aspects of the organizations. With the use of ADAM, SGD, and LBFGS optimization techniques, this paper evaluates a Multilayer Perceptron (MLP) classification algorithm for Syn flood DDoS attack detection using various uncorrelated features chosen with Pearson, Spearman, and Kendall correlation methods. Dataset for a Syn flood DDoS attack was taken from the CIC-DDoS2019 dataset. Experiment results conclude that among optimization techniques, ADAM optimization gives better results and among uncorrelation feature sets and Pearson uncorrelated feature subset produce the best results. Multilayer Perceptron produces the best classification results with ADAM optimization and Pearson uncorrelation subset on Syn flood DDoS attack.

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Titel: Syn Flood DDoS Attack Detection with Different Multilayer Perceptron Optimization Techniques Using Uncorrelated Feature Subsets Selected by Different Correlation Methods
verfasst von: Nagaraju Devarakonda
Kishorebabu Dasari
Verlag: Springer Nature Singapore
Buch: High Performance Computing, Smart Devices and Networks
Print ISBN: 978-981-9966-89-9

Electronic ISBN: 978-981-9966-90-5

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-6690-5_18

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