Smart Botnet Detection in IoT using Stacked Neural Networks

The increasing range of IoT (Internet of Things) devices has rendered them very susceptible to attacks by botnets that can destroy the security of networks as well as interfere with services. The proposed project is geared towards creating an effective hybrid machine learning model to identify botnet attacks within the IoT networks. The proposed methodology is a stacked combination of deep learning models, i.e., LSTM, RNN, and CNN, and one of the classic machine learning classifiers. The training dataset is composed of network traffic data, which is pre-processed with such techniques as feature selection (Select Best) and data balancing (SMOTE Tomek). Sequential data characteristics of deep learning models are combined with the classification capabilities of tree-based models in the hybrid model to enhance accuracy of detection and decrease false positives. These findings indicate that LSTM + Random Forest recorded an accuracy of 88% which is better than the individual model. This is a hybrid solution that is scalable and robust to detect botnets and guarantee the security of IoT networks.