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<p><?tk 4?>The type of attack addressed in this proposed methodology is email phishing. Initially, preprocessing is done and text based features are extracted from emails using NLP techniques. TF-IDF vectorizer is used for reducing features. Multilayer Hybrid AdaBoost Classification Algorithm(MHACA) is implemented by adding several boosting layers for identifying phishing emails. The classification accuracy has been improved by adding more layers to the traditional AdaBoost algorithm. The performance of MHACA is measured using accuracy, precision, recall and F1-score and compared with the classification models of ANN, RNN and LSTM algorithms. The proposed MHACA has proved to be an efficient framework for email attack detection.</p>

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Attack Detection Framework of E-mail Phishing Using Multilayer Hybrid AdaBoost Classification Algorithm

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • S. Abiramasundari,
  • V. Ramaswamy

摘要

The type of attack addressed in this proposed methodology is email phishing. Initially, preprocessing is done and text based features are extracted from emails using NLP techniques. TF-IDF vectorizer is used for reducing features. Multilayer Hybrid AdaBoost Classification Algorithm(MHACA) is implemented by adding several boosting layers for identifying phishing emails. The classification accuracy has been improved by adding more layers to the traditional AdaBoost algorithm. The performance of MHACA is measured using accuracy, precision, recall and F1-score and compared with the classification models of ANN, RNN and LSTM algorithms. The proposed MHACA has proved to be an efficient framework for email attack detection.