Final Year Project

We have proposed an Invasion Detection System based on Machine Learning which leverages the ANN (Artificial Neural Networks) for combating such attacks. The ANN is trained on IP traces data to classify safe and hostile packets, and based on this knowledge it is able to detect a DDoS attack. This framework is tried out in a simulated IoT Environment and is able to achieve significant accuracy.

This project utilizes Machine Learning and Deep Learning to detect DDoS attacks in Software-Defined Networks. It explores models like LSTM, Bi-LSTM, CNN, ANN, and SVM for accurate and timely detection, enhancing network security.

Adaptive IoT DDoS detection system using CIC-IoT-2023. Features Genetic Algorithm feature selection, Optuna tuning, and Shannon Entropy switching logic to toggle between high-speed (XGBoost) and robust (ANN) modes. Optimized for real-time security, it integrates RF, XGB, LGBM, and Deep Learning for precise intrusion detection and mitigation.