Multiclass Botnet Detection in IoT Smart Home Using Deep and Ensemble Learning Techniques

In the rapidly expanding domain of the Internet of Things (IoT), smart homes have become increasingly prevalent, integrating various interconnected devices that enhance convenience, security, and energy efficiency. Nevertheless, this interconnectedness also brings up notable security complexities, including the threat of botnet assaults, which have the potential to jeopardize entire networks of devices. With the increasing quantity and variety of interconnected devices in smart homes, it is imperative to implement strong security measures to safeguard these systems. This work aims to improve the security of IoT smart homes by assessing the efficacy of several deep learning algorithms in detecting and classifying multiclass botnet attacks. We employed the Bot-IoT dataset to execute and evaluate the effectiveness of three advanced deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN), and two Ensemble Learning (EL) methods, namely Gradient Boost (GB) and AdaBoost (AB). The primary objective was to determine which models offer the most reliable protection against sophisticated cyber threats targeting smart home environments. The experimental findings demonstrate that DL models consistently achieved superior performance compared to the other EL models in many performance parameters, such as accuracy, sensitivity, false positive rate (FPR), false negative rate (FNR), Matthews correlation coefficient (MCC), and Area Under the Curve (AUC). The results emphasize the capability of DL models to greatly enhance the security of IoT smart homes, offering a robust defense mechanism against the changing nature of botnet attacks. This study emphasizes the crucial importance of utilizing modern DL methods to protect the growingly interconnected and susceptible IoT ecosystems.