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The Internet of Things (IoT) has flourished and found numerous practical applications thanks to the advent of energy-aware sensor devices, as well as autonomous and brainy systems. But the Internet of Things gadgets are especially vulnerable to botnet assaults. To counteract this risk, it may be possible to design an anomaly-based detection method that is both lightweight and capable of creating profiles for both malicious and benign behaviors on IoT networks. Machine learning (ML) procedures may also be used for the mountain of data produced by IoT devices. Various tactics have been used to identify the Botnet's initial point of entry. However, the work remains challenging due to the limited sum of characteristics encompassed in Botnet datasets. Nine industrial-grade IoT devices were hacked in this study, and XGBoost and B models were constructed to identify and categorize common IoT botnet threats including Mirai and BASHLITE. Rigid hyperparameter tuning using a variant of the ant lion optimization algorithm (ALOA) formed the basis of the model development process.

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BotNet Attack Detection Using MALO-Based XGBoost Model in IoT Environment

  • 会议论文

  • 发表日期：

    2025年

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  • Omar A. Alzubi

摘要

The Internet of Things (IoT) has flourished and found numerous practical applications thanks to the advent of energy-aware sensor devices, as well as autonomous and brainy systems. But the Internet of Things gadgets are especially vulnerable to botnet assaults. To counteract this risk, it may be possible to design an anomaly-based detection method that is both lightweight and capable of creating profiles for both malicious and benign behaviors on IoT networks. Machine learning (ML) procedures may also be used for the mountain of data produced by IoT devices. Various tactics have been used to identify the Botnet's initial point of entry. However, the work remains challenging due to the limited sum of characteristics encompassed in Botnet datasets. Nine industrial-grade IoT devices were hacked in this study, and XGBoost and B models were constructed to identify and categorize common IoT botnet threats including Mirai and BASHLITE. Rigid hyperparameter tuning using a variant of the ant lion optimization algorithm (ALOA) formed the basis of the model development process.