IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i2p76-d1069521.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/2/76/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/2/76/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abdulkader Hajjouz, 2023. "A CatBoost-Based Approach for High-Accuracy Botnet Detection," Technium, Technium Science, vol. 15(1), pages 26-32.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    2. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
    3. Wu, Han & Liang, Yan & Heng, Jiani, 2023. "Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting," Applied Energy, Elsevier, vol. 339(C).
    4. Fahad R. Albogamy & Ghulam Hafeez & Imran Khan & Sheraz Khan & Hend I. Alkhammash & Faheem Ali & Gul Rukh, 2021. "Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids," Sustainability, MDPI, vol. 13(20), pages 1-29, October.
    5. Seyfi, Mohammad & Mehdinejad, Mehdi & Mohammadi-Ivatloo, Behnam & Shayanfar, Heidarali, 2022. "Deep learning-based scheduling of virtual energy hubs with plug-in hybrid compressed natural gas-electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    6. Thamer Alquthami & Ahmad H. Milyani & Muhammad Awais & Muhammad B. Rasheed, 2021. "An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
    7. Herodotos Herodotou & Sheraz Aslam, 2022. "Data-Intensive Computing in Smart Microgrids: Volume II," Energies, MDPI, vol. 15(16), pages 1-2, August.
    8. Fachrizal Aksan & Vishnu Suresh & Przemysław Janik & Tomasz Sikorski, 2023. "Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models," Energies, MDPI, vol. 16(14), pages 1-24, July.
    9. Yannik Hahn & Tristan Langer & Richard Meyes & Tobias Meisen, 2023. "Time Series Dataset Survey for Forecasting with Deep Learning," Forecasting, MDPI, vol. 5(1), pages 1-21, March.
    10. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
    11. Li, Tianyu & Gao, Ciwei & Chen, Tao & Jiang, Yu & Feng, Yingchun, 2022. "Medium and long-term electricity market trading strategy considering renewable portfolio standard in the transitional period of electricity market reform in Jiangsu, China," Energy Economics, Elsevier, vol. 107(C).
    12. Noman Khan & Fath U Min Ullah & Ijaz Ul Haq & Samee Ullah Khan & Mi Young Lee & Sung Wook Baik, 2021. "AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
    13. Ullah, Asad & Zhang, Qingyu & Raza, Syed Ali & Ali, Sajid, 2021. "Renewable energy: Is it a global challenge or opportunity? Focusing on different income level countries through Panel Smooth Transition Regression Model," Renewable Energy, Elsevier, vol. 177(C), pages 689-699.
    14. Yukta Mehta & Rui Xu & Benjamin Lim & Jane Wu & Jerry Gao, 2023. "A Review for Green Energy Machine Learning and AI Services," Energies, MDPI, vol. 16(15), pages 1-30, July.
    15. Khan, Waqas & Liao, Juo Yu & Walker, Shalika & Zeiler, Wim, 2022. "Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism," Applied Energy, Elsevier, vol. 319(C).
    16. Nasir Ayub & Usman Ali & Kainat Mustafa & Syed Muhammad Mohsin & Sheraz Aslam, 2022. "Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid," Forecasting, MDPI, vol. 4(4), pages 1-13, November.
    17. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    18. Hamdi Abdi, 2022. "A Brief Review of Microgrid Surveys, by Focusing on Energy Management System," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    19. Liang, Xinbin & Chen, Siliang & Zhu, Xu & Jin, Xinqiao & Du, Zhimin, 2023. "Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions," Applied Energy, Elsevier, vol. 344(C).
    20. Kaleem Ullah & Abdul Basit & Zahid Ullah & Rafiq Asghar & Sheraz Aslam & Ayman Yafoz, 2022. "Line Overload Alleviations in Wind Energy Integrated Power Systems Using Automatic Generation Control," Sustainability, MDPI, vol. 14(19), pages 1-19, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:15:y:2023:i:2:p:76-:d:1069521. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.