Detection of IT and Mobile Network Attacks

The objective of this project is to develop an intelligent system for detecting IT and mobile network attacks using machine learning algorithms. The system utilizes the Edge-IIoTset dataset to identify and classify network traffic into six classes: DDoS_UDP, DDoS_ICMP, DDoS_TCP, Port_Scanning, Ransomware, and Benign. Machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and XGBoost are applied to achieve high detection accuracy. Implemented using Python and the Flask framework, the system enables users to upload datasets, evaluate models, and visualize prediction results. The objective is to automate attack detection, enhance security, and minimize manual monitoring efforts.