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A Hybrid Meta-heuristics Algorithm: XGBoost-Based Approach for IDS in IoT

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Abstract

In recent years, the importance of the Internet of Things (IoT) and its applications has increased. IoT networks are diverse, allowing for various real-time applications and making them indispensable to daily life. Even though these IoT gadgets make everyday tasks more straightforward, these networks are open to several security risks. IoT networks are currently vulnerable to security assaults due to inadequate security measures, making them attractive targets for different attackers. In this study, the Whale optimization method (WOM) is used to identify the ideal parameters for an intrusion detection model, and XGBoost (Extreme Gradient Boosting) is applied to those parameters that WOM selects. The principal component analysis (PCA) initially performs pre-processing on the gathered network data. To improve the overall model’s capacity for detecting intrusions after training, pre-processed data are first loaded into the WOM-Boost algorithm. The empirical results are applied to two datasets, IoTID20 and UNSW-NB15, and it is demonstrated that the IDS model using this method has greater accuracy, up to 99%, when compared to the results obtained by parameter adjustment in the conventional manner.

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Data Availability

The data set generated and/or analyzed during the current study is available upon reasonable request from the corresponding author. However, data sets are available as open source.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity University Madhya Pradesh, Maharajpura Dang, Gwalior, India

    Soumya Bajpai & Kapil Sharma

  2. Department of Computer Science and Engineering, Pranveer Singh Institute of Technology, Kanpur, India

    Brijesh Kumar Chaurasia

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Bajpai, S., Sharma, K. & Chaurasia, B.K. A Hybrid Meta-heuristics Algorithm: XGBoost-Based Approach for IDS in IoT. SN COMPUT. SCI. 5, 537 (2024). https://doi.org/10.1007/s42979-024-02913-2

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