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Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

Author

Listed:
  • Umar Islam

    (Department of Computer Science, IQRA National University, Swat Campus, Swat 19220, Pakistan)

  • Ali Muhammad

    (Institute of Management Studies, University of Peshawar, Peshawar 25000, Pakistan)

  • Rafiq Mansoor

    (Department of Mechanical Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan)

  • Md Shamim Hossain

    (Department of Marketing, Hajee Mohammad Danesh Science & Technology University, Dinajpur 5200, Bangladesh)

  • Ijaz Ahmad

    (Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518040, China)

  • Elsayed Tag Eldin

    (Electrical Engineering Department, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt)

  • Javed Ali Khan

    (Department of Software Engineering, University of Science & Technology, Bannu 28100, Pakistan)

  • Ateeq Ur Rehman

    (Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan)

  • Muhammad Shafiq

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea)

Abstract

Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.

Suggested Citation

  • Umar Islam & Ali Muhammad & Rafiq Mansoor & Md Shamim Hossain & Ijaz Ahmad & Elsayed Tag Eldin & Javed Ali Khan & Ateeq Ur Rehman & Muhammad Shafiq, 2022. "Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8374-:d:858705
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    Citations

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    Cited by:

    1. Muhammad Abbas Khan & Ijaz Ahmad & Anis Nurashikin Nordin & A. El-Sayed Ahmed & Hiren Mewada & Yousef Ibrahim Daradkeh & Saim Rasheed & Elsayed Tag Eldin & Muhammad Shafiq, 2022. "Smart Android Based Home Automation System Using Internet of Things (IoT)," Sustainability, MDPI, vol. 14(17), pages 1-17, August.
    2. Sanjay Kumar & Rafeeq Ahmed & Salil Bharany & Mohammed Shuaib & Tauseef Ahmad & Elsayed Tag Eldin & Ateeq Ur Rehman & Muhammad Shafiq, 2022. "Exploitation of Machine Learning Algorithms for Detecting Financial Crimes Based on Customers’ Behavior," Sustainability, MDPI, vol. 14(21), pages 1-24, October.
    3. Jin, Keyan & Zhong, Ziqi & Zhao, Elena Yifei, 2024. "Sustainable digital marketing under big data: an AI random forest model approach," LSE Research Online Documents on Economics 121402, London School of Economics and Political Science, LSE Library.
    4. Aoqi Xu & Mehdi Darbandi & Danial Javaheri & Nima Jafari Navimipour & Senay Yalcin & Anas A. Salameh, 2023. "The Management of IoT-Based Organizational and Industrial Digitalization Using Machine Learning Methods," Sustainability, MDPI, vol. 15(7), pages 1-28, March.

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