Distributed denial of service attack defense system-based auto machine learning algorithm
نویسندگان
چکیده
The use of network-connected gadgets is rising quickly in the internet age, which escalating number cyberattacks. detection distributed denial service (DDoS) attacks a tedious task that has necessitated development models for its identification recently. Nonetheless, because major fluctuations subscriptions and traffic rates, it continues to be difficult challenge. A novel automatic technique was created address this issue work, reduces feature space consequently minimizes computational time model overfitting. Data preprocessing done first increase model's generalizability; then, selection method used choose most pertinent features accuracy classification process. Additionally, hyperparameter tuning-choosing proper parameters learning approach-improved performance. Finally, support vector machine (SVM) compatible with optimization hyperparameters offered by supervised methods. CICDDoS2019 dataset evaluate each these assays, experimental findings demonstrated that, an 99.95%, suggested performs well when compared more modern techniques.
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ژورنال
عنوان ژورنال: Bulletin of Electrical Engineering and Informatics
سال: 2023
ISSN: ['2302-9285']
DOI: https://doi.org/10.11591/eei.v12i1.4537