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Detecting application layer DDoS attack using an advanced signature detection algorithm.

Abdul Ghafar Jaafar1, Md Asri Ngadi2, Nazri Kama3

  • 1Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia. abdulghafar@utm.my.

Scientific Reports
|June 10, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a signature detection method to identify forged request headers in Application-layer Distributed Denial of Service (App-DDoS) attacks. The approach achieves high accuracy, offering a practical solution for cybersecurity defense.

Area of Science:

  • Cybersecurity
  • Network Security
  • Applied Computer Science

Background:

  • Application-layer Distributed Denial of Service (App-DDoS) attacks pose a significant threat by disrupting services through forged request headers.
  • Existing detection methods struggle with identifying these forged headers, creating a critical research gap.

Purpose of the Study:

  • To develop and evaluate a novel signature detection method for identifying forged request headers in App-DDoS attacks.
  • To address the limitations of outdated attack patterns and lack of public datasets by using recent, representative data.

Main Methods:

  • Analyzing network traffic request headers to categorize them as malicious or legitimate.
  • Employing a hybrid feature selection method to identify key indicators of forged headers.

Related Experiment Videos

  • Utilizing a recent, real-world dataset representative of current App-DDoS attack patterns.

    • Main Results:

    • The signature detection method achieved high performance metrics: 96.93% accuracy, 99.11% precision, 97.55% recall, and 98.32% F1-score.
    • The proposed detection algorithms successfully identify forged request headers at an early stage, before web server processing.
    • The study confirmed that App-DDoS attack strategies rely on manipulating request headers.

    Conclusions:

    • Signature-based detection is effective and suitable for identifying App-DDoS attacks by analyzing request headers.
    • The developed method offers a practical and accurate solution for real-world cybersecurity applications.
    • This approach complements Machine Learning (ML) by effectively detecting attack signatures in request headers.