A Kali Linux-based tool that simulates DDoS attacks, collects network traffic data, and applies machine learning algorithms to detect and differentiate DDoS attacks from normal traffic.
Kali Linux sanal makinesi kullanarak DDoS saldırılarının simülasyonunu gerçekleştirip, oluşturulan veri seti üzerinde makine öğrenme algoritmaları ile saldırı tespiti ve normal trafikten ayırma.
This tool is designed for educational and research purposes to simulate DDoS attacks in a controlled environment and generate labeled datasets for training machine learning models. Security researchers, network administrators, and students can use it to understand DDoS attack patterns and evaluate the effectiveness of various ML algorithms in intrusion detection.
The dataset contains sensitive IP address information and is not shared directly; instead, instructions for dataset creation are provided. Users should have a Kali Linux environment and basic Python knowledge to run simulations and analyze data. This tool is intended for educational and research use only and should be used responsibly within legal boundaries.
Set up a Kali Linux virtual machine
Run commands on Kali Linux to simulate DDoS attacks and capture network traffic
Use Python to read and analyze the captured pcap files
Extract features and label data for machine learning
Train and evaluate ML models using the prepared datasetSend continuous SYN packets to target IP to simulate DDoS attack
Initiates the DDoS attack simulation by flooding the target with SYN packets
Capture network traffic using tcpdump or similar tools on Kali Linux
Records the network packets during the simulated attack for analysis
python analyze_pcap.py
Reads and processes the pcap file to extract features and label packets
Train ML models (Decision Tree, Random Forest, etc.) on the extracted dataset
Applies machine learning algorithms to detect and classify DDoS attacks