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Kali Linux sanal makinesi kullanarak DDoS saldırılarının simülasyonunu gerçekleştirip, oluşturulan veri seti üzerinde makine öğrenme algoritmaları ile saldırı tespiti ve normal trafikten ayırma.
[IEEE Internet of Things 2022]: This study presents a competent feature selection method extreme gradient boosting (XGBoost) for determining the most relevant data features with a hybrid convolutional neural network and long short-term memory (CNN-LSTM) for DDoS attack classification in software-defined IIoT networks.
Various supervised machine learning techniques on the highly optimized NSL-KDD dataset to create an efficient and accurate predictor of possible intrusions on a network.
Detecting and mitigating DDoS attacks using Software Defined Networks. This was created as a part of Research Based Learning Project for the SET5002 Course offered at Vellore Institute of Technology, Chennai.
This repository is a collection of code that I use for the "seminar" course in the Computer Science Department of Gadjah Mada University. The topic of this seminar is "Deep Learning Method for Prediction of DDoS Attacks on Social Media" which is taken from DOI:10.1142/S2424922X19500025