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Arabian Journal for Science and Engineering

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08-07-2021 | Research Article-Computer Engineering and Computer Science

Analysis of Machine Learning Classifiers for Early Detection of DDoS Attacks on IoT Devices

Authors: Vimal Gaur, Rajneesh Kumar

Published in: Arabian Journal for Science and Engineering | Issue 2/2022

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Abstract

Distributed denial-of-service attacks are still difficult to handle as per current scenarios. The attack aim is a menace to network security and exhausting the target networks with malicious traffic from multiple sites. Although a plethora of conventional methods have been proposed to detect DDoS attacks, so far the rapid diagnosis of these attacks using feature selection algorithms is a daunting challenge. The proposed system uses a hybrid methodology for selecting features by applying feature selection methods on machine learning classifiers. Feature selections methods, namely chi-square, Extra Tree and ANOVA have been applied on four classifiers Random Forest, Decision Tree, k-Nearest Neighbors and XGBoost for early detection of DDoS attacks on IoT devices. We use the CICDDoS2019 dataset containing comprehensive DDoS attacks to train and assess the proposed methodology in a cloud-based environment (Google Colab). Based on the experimental results, the proposed hybrid methodology provides superior performance with a feature reduction ratio of 82.5% by achieving 98.34% accuracy with ANOVA for XGBoost and helps in early detection of DDoS attacks on IoT devices.

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Title: Analysis of Machine Learning Classifiers for Early Detection of DDoS Attacks on IoT Devices
Authors: Vimal Gaur
Rajneesh Kumar
Publication date: 08-07-2021
Publisher: Springer Berlin Heidelberg
Published in: Arabian Journal for Science and Engineering / Issue 2/2022
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-021-05947-3

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