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Updated: Jul 8, 2026

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Deep learning-based HTTP TRACE flood detection in wireless sensor network using deep spectral multi-layer

S Tamilselvi1, Chin-Shiuh Shieh2, Mong-Fong Horng2

  • 1SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, 603203, India. tamilses3@srmist.edu.in.

Scientific Reports
|March 10, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces the Enhanced Deep Spectral Multi-Layer Convolutional Neural Network (EDSMCNN) to detect HTTP TRACE flood attacks in Wireless Sensor Networks (WSN). The EDSMCNN model enhances CPU performance and reduces computation time, improving WSN security against sophisticated threats.

Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Wireless Sensor Networks (WSN) are susceptible to Distributed Denial-of-Service (DDoS) attacks, specifically HTTP flood attacks targeting bandwidth and CPU processing.
  • HTTP TRACE flood attacks, which misuse the HTTP TRACE method with static URLs, degrade WSN performance and expose sensitive data.

Purpose of the Study:

  • To propose an Enhanced Deep Spectral Multi-Layer Convolutional Neural Network (EDSMCNN) for improved detection and mitigation of HTTP TRACE flood attacks in WSN.
  • To enhance CPU performance, manage multiple URL requests, and predict TRACE attack traffic using maximum-weighted features.

Main Methods:

  • Data preprocessing involved dimensionality reduction and feature selection using the spider algorithm based on Lattice Service Rate Access Values (LSRAV).
Keywords:
Distributed denial of service (DDoS)Enhanced deep spectral multi-layer convolutional neural network (EDSMCNN)HTTP-TRACE flooding attacksLattice service rate access values (LURAV)SoftMax logistic activation function (SLAF)Wireless sensor networks (WSN)

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  • The EDSMCNN model utilizes SoftMax and the Logistic Activation Function (SLAF) to generate logistic neurons for preventing trace attacks.
  • Feature selection considered parameters like URL, protocol, and IP address, with social spiders comparing feature patterns.

    • Main Results:

    • The EDSMCNN system demonstrated high efficiency in detecting HTTP TRACE flooding attacks compared to standard convolutional methods.
    • Experimental results confirmed improvements in CPU performance and reductions in computation time, effectively mitigating traffic from HTTP requests.
    • The study highlights the vulnerability of WSNs to TRACE attackers via backdoor injection, emphasizing the need for robust security measures.

    Conclusions:

    • The EDSMCNN model offers a robust solution for detecting and mitigating HTTP TRACE flood attacks in WSNs.
    • The proposed method significantly enhances WSN security by improving performance and reducing detection time.
    • Effective countermeasures are crucial for safeguarding WSN integrity and confidentiality against emerging cyber threats.