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All Journal Bulletin of Electrical Engineering and Informatics
Shatha Talib
Al-Bayan Universiy College
Electrical & Electronics Engineering

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Distributed denial of service attack defense system-based auto machine learning algorithm Mohammad Aljanabi; Russul Hayder; Shatha Talib; Ahmed Hussien Ali; Mostafa Abdulghafoor Mohammed; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4537

Abstract

The use of network-connected gadgets is rising quickly in the internet age, which is escalating the number of cyberattacks. The detection of distributed denial of service (DDoS) attacks is a tedious task that has necessitated the development of a number of models for its identification recently. Nonetheless, because of major fluctuations in subscriptions and traffic rates, it continues to be a difficult challenge. A novel automatic detection technique was created to address this issue in this work, which reduces the feature space and consequently minimizes the computational time and model overfitting. Data preprocessing is done first to increase the model's generalizability; then, a feature selection method is used to choose the most pertinent features to increase the accuracy of the classification process. Additionally, hyperparameter tuning-choosing the proper parameters for the learning approach-improved model performance. Finally, the support vector machine (SVM) is compatible with the optimization and the hyperparameters offered by supervised learning methods. The CICDDoS2019 dataset was used to evaluate each of these assays, and the experimental findings demonstrated that, with an accuracy of 99.95%, the suggested model performs well when compared to more modern techniques.
Co-Authors Ahmed Hussien Ali Mohammad Aljanabi Mostafa Abdulghafoor Mohammed Russul Hayder Tole Sutikno