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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.
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.
[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.
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
We are developing a tool for analyse recorded network traffic in order to detect and investigate about IP source address which may had contribute in a DDoS UDP flood attack. This tool also generates sample pcap datasets.