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Context <p>Wireless sensor network (WSN) is susceptible to vampire attacks. It is a type of denial of service (DoS) attack that drains sensor nodes’ energy, leading to network failure. The study addresses the challenge by detecting these attacks to conserve energy and maintain network functionality.</p> Objective <p>The research aims to develop a fuzzy ranking-based ensemble deep network (EDN) for vampire attack detection in WSN, thereby extending the network’s lifespan and improving security.</p> Methods <p>The proposed model involves three phases: data collection, feature selection using the enhanced piranha foraging optimization algorithm (E-PFOA), and attack detection using EDN, which combines long short-term memory (LSTM), recurrent neural network (RNN), gated recurrent unit (GRU), and temporal convolution network (TCN). The final detection outcome is determined through fuzzy ranking.</p> Results <p>The sensitivity, specificity, and F1-score of the implemented vampire attack detection model were attained with 96.85%, 95.56%, and 96.10% for the k-fold value of 5, and these values are significantly higher than those of conventional approaches. The ROC curve and confusion matrix further validated the model’s effectiveness.</p> Conclusion <p>The study successfully implemented a fuzzy ranking-based vampire attack detection model that outperforms traditional methods, offering a promising solution for securing WSN against vampire attacks and ensuring the network’s lifetime.</p>

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Fuzzy computation on ensemble deep network for the performance of vampire attack detection model in WSN

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月29日

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  • M. Sudha,
  • Rajesh Arunachalam,
  • A. Karthikayen,
  • V. Sumanth

摘要

Context

Wireless sensor network (WSN) is susceptible to vampire attacks. It is a type of denial of service (DoS) attack that drains sensor nodes’ energy, leading to network failure. The study addresses the challenge by detecting these attacks to conserve energy and maintain network functionality.

Objective

The research aims to develop a fuzzy ranking-based ensemble deep network (EDN) for vampire attack detection in WSN, thereby extending the network’s lifespan and improving security.

Methods

The proposed model involves three phases: data collection, feature selection using the enhanced piranha foraging optimization algorithm (E-PFOA), and attack detection using EDN, which combines long short-term memory (LSTM), recurrent neural network (RNN), gated recurrent unit (GRU), and temporal convolution network (TCN). The final detection outcome is determined through fuzzy ranking.

Results

The sensitivity, specificity, and F1-score of the implemented vampire attack detection model were attained with 96.85%, 95.56%, and 96.10% for the k-fold value of 5, and these values are significantly higher than those of conventional approaches. The ROC curve and confusion matrix further validated the model’s effectiveness.

Conclusion

The study successfully implemented a fuzzy ranking-based vampire attack detection model that outperforms traditional methods, offering a promising solution for securing WSN against vampire attacks and ensuring the network’s lifetime.