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A machine learning-based ensemble model for securing the IoT network

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Abstract

The rapidly growing number of Internet of Things (IoT) devices has led to a rise in data transfers, which has raised security concerns. Due to the devices’ limited processing capabilities and vulnerability to many cyber attacks, securing IoT communications is challenging. Security threats, especially Distributed Denial of Service (DDoS) attacks, take a toll on the network in the form of increased communication overhead. Hence, a centralized unit is required to detect DDoS attacks in IoT networks at the earliest. Software-Defined Networking (SDN) promises a potential solution for better network traffic management and data flow. This paper presents a machine learning-based ensemble model for the detection of DDoS attacks in IoT networks using SDN. The proposed model employs a multi-step approach utilizing various Machine Learning (ML) algorithms. The proposed Ensemble Model (EM) combines Logistic Regression (LR), k-Nearest Neighbors (KNN), Gradient Boosting (GB), Extra-tree (ET), AdaBoost, and XGBoost, with XGBoost as the final estimator classifier. Various metrics, including sensitivity, specificity, precision, accuracy, and others, derived from the confusion matrix, evaluate the proposed model’s performance. The EM demonstrates superior performance during comparative analysis with state-of-the-art schemes, with a classification accuracy of 99.8%. Furthermore, the paper evaluates the model based on Receiver Operator Characteristic (ROC) curves, showing its superiority in True Positive Rates (TPR) compared to False Positive Rates (FPR). The AUC analysis supports the EM’s effectiveness. Cross-validation results further validate the model’s robustness, with a mean accuracy of 97.92%.

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The data used in this study will be made available upon reasonable request.

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Author notes

  1. Krishna Pal Sharma and Lalit Kumar Awasthi have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Engineering, Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Rohit Singh & Krishna Pal Sharma

  2. Department of Computer Science and Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India

    Lalit Kumar Awasthi

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  1. Rohit Singh
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  2. Krishna Pal Sharma
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Contributions

Rohit Singh conceptualized the study, designed the research methodology, and supervised the overall project. Rohit Singh, Krishan Pal Sharma conducted the experiments, collected, and analysed the result, and contributed to the writing of the manuscript. Lalit Kumar Awasthi contributed to the literature review, provided critical feedback on the research design, and contributed significantly to drafting and revising the manuscript. All authors contributed to the theoretical framework, conducted statistical analysis, and contributed to the interpretation of the results. They also revised the manuscript critically for important intellectual content.

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Correspondence to Rohit Singh.

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Singh, R., Sharma, K.P. & Awasthi, L.K. A machine learning-based ensemble model for securing the IoT network. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04519-y

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