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Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron

Author

Listed:
  • Sheeraz Ahmed

    (Department of Computer Science, Iqra National University, Peshawar 25000, Pakistan)

  • Zahoor Ali Khan

    (Faculty of Computer Information Science, Higher Colleges of Technology, Fujairah 4114, United Arab Emirates)

  • Syed Muhammad Mohsin

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan
    College of Intellectual Novitiates (COIN), Virtual University of Pakistan, Lahore 55150, Pakistan)

  • Shahid Latif

    (Department of Computer Science, Iqra National University, Peshawar 25000, Pakistan)

  • Sheraz Aslam

    (Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus
    Department of Computer Science, Ctl Eurocollege, 3077 Limassol, Cyprus)

  • Hana Mujlid

    (Department of Computer Engineering, Taif University, Taif 11099, Saudi Arabia)

  • Muhammad Adil

    (Department of Computer Science, Iqra National University, Peshawar 25000, Pakistan)

  • Zeeshan Najam

    (CEO, Ultimate Engineering Consultants Private Limited, Peshawar 25000, Pakistan)

Abstract

Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS attacks that are not easily detectable because of impersonating authentic users. In this study, we address novel application layer DDoS attacks by analyzing the characteristics of incoming packets, including the size of HTTP frame packets, the number of Internet Protocol (IP) addresses sent, constant mappings of ports, and the number of IP addresses using proxy IP. We analyzed client behavior in public attacks using standard datasets, the CTU-13 dataset, real weblogs (dataset) from our organization, and experimentally created datasets from DDoS attack tools: Slow Lairs, Hulk, Golden Eyes, and Xerex. A multilayer perceptron (MLP), a deep learning algorithm, is used to evaluate the effectiveness of metrics-based attack detection. Simulation results show that the proposed MLP classification algorithm has an efficiency of 98.99% in detecting DDoS attacks. The performance of our proposed technique provided the lowest value of false positives of 2.11% compared to conventional classifiers, i.e., Naïve Bayes, Decision Stump, Logistic Model Tree, Naïve Bayes Updateable, Naïve Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier, and OneR.

Suggested Citation

  • Sheeraz Ahmed & Zahoor Ali Khan & Syed Muhammad Mohsin & Shahid Latif & Sheraz Aslam & Hana Mujlid & Muhammad Adil & Zeeshan Najam, 2023. "Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron," Future Internet, MDPI, vol. 15(2), pages 1-24, February.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:2:p:76-:d:1069521
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    References listed on IDEAS

    as
    1. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    2. Demir, Kubilay & Nayyer, Ferdaus & Suri, Neeraj, 2019. "MPTCP-H: A DDoS attack resilient transport protocol to secure wide area measurement systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 25(C), pages 84-101.
    3. Filippo Rebecchi & Julien Boite & Pierre‐Alexis Nardin & Mathieu Bouet & Vania Conan, 2019. "DDoS protection with stateful software‐defined networking," International Journal of Network Management, John Wiley & Sons, vol. 29(1), January.
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    Cited by:

    1. Abdulkader Hajjouz, 2023. "A CatBoost-Based Approach for High-Accuracy Botnet Detection," Technium, Technium Science, vol. 15(1), pages 26-32.
    2. Abbas Javed & Amna Ehtsham & Muhammad Jawad & Muhammad Naeem Awais & Ayyaz-ul-Haq Qureshi & Hadi Larijani, 2024. "Implementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes," Future Internet, MDPI, vol. 16(6), pages 1-22, June.

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