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The discovery of spam in IoT (Internet of Things) devices is the crucial issue of this study, which utilizes ML (machine learning) approaches. The RFC (Random Forest Classifier), the BC (Bagging Classifier), the VC (voting classifier), the GNB, the SGD Classifier, the GBC, and the XGB Classifier are a few of the classifiers that are deemed to be utilized from the sci-kit-learn and XGB libraries. Improving the accurateness and flexibility of the spam discovery system is carried out via the employ of ensemble methods. There is a system called hyperparameter tuning, which entails scientifically peripatetic transversely a range of values to maximize the values of hyperparameters. It is possible to extract appropriate possessions from the dataset via the utilization of feature engineering advances. An extremely expensive IoT spam improvement method that constantly recognizes spam in IoT devices is produced as an outcome of the mix of feature engineering, hyperparameter tuning, and ensemble methods.

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Spam Discovery in IoT Devices Using Time Series Data

  • 会议论文

  • 发表日期：

    2025年

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  • Ch. Sai Spoorthi,
  • Sunil Bhutada,
  • V. Kakulapati,
  • T. Deekshitha,
  • Ch. Rajyalakshmi

摘要

The discovery of spam in IoT (Internet of Things) devices is the crucial issue of this study, which utilizes ML (machine learning) approaches. The RFC (Random Forest Classifier), the BC (Bagging Classifier), the VC (voting classifier), the GNB, the SGD Classifier, the GBC, and the XGB Classifier are a few of the classifiers that are deemed to be utilized from the sci-kit-learn and XGB libraries. Improving the accurateness and flexibility of the spam discovery system is carried out via the employ of ensemble methods. There is a system called hyperparameter tuning, which entails scientifically peripatetic transversely a range of values to maximize the values of hyperparameters. It is possible to extract appropriate possessions from the dataset via the utilization of feature engineering advances. An extremely expensive IoT spam improvement method that constantly recognizes spam in IoT devices is produced as an outcome of the mix of feature engineering, hyperparameter tuning, and ensemble methods.