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Timely detection of DDoS attacks in IoT with dimensionality reduction

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Abstract

The exponential growth of IoT devices and their interdependency makes the technology more vulnerable to network attacks like Distributed Denial of Service (DDoS) that interrupt network resources. The prevalence of these attacks necessitates the development of robust and effective defense mechanisms. In recent years, many machine learning defense methodologies have been developed to address the ubiquitous growth of DDoS attacks on IoT, and the majority of them suffer from detection time delay issues. Thus, the paper presents an approach focusing on dimensionality reduction and feature selection techniques to minimize long-time detection without compromising accuracy. The proposed approach uses Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Factor Analysis and Recursive Feature Elimination with Cross Validation (RFECV) as the dimensionality reduction and feature selection techniques and Gaussian Naïve Bayes (GNB), Decision Tree (DT), Random Forest (RF), AdaBoost, and Logistic Regression (LR) as machine learning models to classify the malicious traffic. The approach provides a reliable DDoS detection model that effectively enhances the detection time delay with the combination of GNB with LDA and achieves 99.98% accuracy in 0.582 s of detection time.

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Data availability

The CICDDoS2019 dataset is used in this work for validating the proposed approach which is available at: “https://www.unb.ca/cic/datasets/ddos-2019.html”.

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  1. Department of Computer Engineering, National Institute of Technology, Kurukshetra, Kurukshetra, India

    Pooja Kumari & Ankit Kumar Jain

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  1. Pooja Kumari
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Pooja Kumari has implemented this study and wrote the manuscript. Dr. Ankit Kumar Jain has provided his guidance to conduct the study and helped in writing the manuscript. All authors reviewed the manuscript.

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Correspondence to Pooja Kumari.

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Kumari, P., Jain, A.K. Timely detection of DDoS attacks in IoT with dimensionality reduction. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04392-9

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