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<p>Phishing attacks are one of the significantly evolving cyber threats, where phishing websites serve as the key tool for the attackers to steal sensitive and private information of the users. Thus, there is a need for a robust method for detecting phishing websites because of the evolving nature of phishing attacks. Numerous approaches based on Machine Learning (ML) have been introduced to detect phishing websites, but those methods suffer from certain shortcomings, such as limited adaptability, lower performance, higher false positive rates, and so on. Therefore, the Mixture of Experts Learning based Bidirectional Boosted Recurrent Network (MoE-B<sup>2</sup>RNet) model is proposed to detect phishing websites by overcoming the limitations of conventional approaches. The utilization of the gradient boosted Gated Recurrent Unit (GRU) within the model facilitates achieving a lower false positive rate by sequentially training the model based on the errors made by the previous ones. Moreover, the employment of Mixture of Experts (MoE) in the dense layer leads to better performance, which activates all the experts for processing each input and improves the model’s generalization capability. The experimental results show the MoE-B<sup>2</sup>RNet model achieved better results in terms of accuracy of 98.71%, specificity of 99.11%, F1-score of 98.87%, sensitivity of 98.39% and precision of 99.35% with 90% of training data for the Phishing URL dataset, respectively.</p>

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MoE-B2RNet: Mixture of experts-based bidirectional boosted recurrent network for phishing attack detection

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Sangeeta Vhatkar,
  • Zahir Aalam,
  • Ranjita Akash Asati,
  • Rahul Neve

摘要

Phishing attacks are one of the significantly evolving cyber threats, where phishing websites serve as the key tool for the attackers to steal sensitive and private information of the users. Thus, there is a need for a robust method for detecting phishing websites because of the evolving nature of phishing attacks. Numerous approaches based on Machine Learning (ML) have been introduced to detect phishing websites, but those methods suffer from certain shortcomings, such as limited adaptability, lower performance, higher false positive rates, and so on. Therefore, the Mixture of Experts Learning based Bidirectional Boosted Recurrent Network (MoE-B2RNet) model is proposed to detect phishing websites by overcoming the limitations of conventional approaches. The utilization of the gradient boosted Gated Recurrent Unit (GRU) within the model facilitates achieving a lower false positive rate by sequentially training the model based on the errors made by the previous ones. Moreover, the employment of Mixture of Experts (MoE) in the dense layer leads to better performance, which activates all the experts for processing each input and improves the model’s generalization capability. The experimental results show the MoE-B2RNet model achieved better results in terms of accuracy of 98.71%, specificity of 99.11%, F1-score of 98.87%, sensitivity of 98.39% and precision of 99.35% with 90% of training data for the Phishing URL dataset, respectively.