Published Paper Details:

Effective detection has become a critical challenge due to the rise of IoT devices, which has led to an increase in botnet attacks. This research extends traditional botnet detection models by incorporating advanced ensemble deep learning techniques to improve prediction accuracy. We integrate CNN, LSTM, and GRU in hybrid architectures such as CNN + LSTM + GRU and CNN + BiLSTM + GRU, which effectively capture both spatial and temporal patterns in IoT network traffic. Feature selection using Mutual Information optimises model performance, reducing computational complexity while improving detection efficiency. Additionally, a Flask is used to create a user-friendly front-end application, which allows for smooth testing and evaluation of the model. Secure user authentication protects sensitive information and ensures data integrity. The experiment's findings demonstrate that the suggested ensemble models achieve superior accuracy, surpassing 97%, in detecting botnet activity, highlighting their effectiveness in securing IoT environments.