Deep Learning for Preventing Botnet Attacks on IoT

The exponential rise of botnet attacks has created an urgent demand for effective intrusion detection systems within Internet of Things (IoT) environments. This study endeavors to tackle this issue by proposing a deep learning-based solution. The main goal is to evaluate and compare the performance of various convolutional neural network (CNN) variations to determine the most accurate and efficient algorithm for identifying and mitigating botnet attacks in IoT networks. To ensure the suitability of the datasets for training and evaluation, meticulous preprocessing techniques are employed, such as normalization and feature selection. The initial phase of the research involved an exploration of machine learning and deep learning methods to safeguard IoT devices against botnet attacks. This paper specifically focuses on studying CNN, 1DCNN, and CNN-RNN for their effectiveness in detecting botnet attacks, conducting a comparative analysis to ascertain the most accurate approach. Subsequently, the deep learning models are trained using the preprocessed data in the implementation phase of the project. The evaluation metrics employed encompass accuracy and loss rate, enabling a comprehensive assessment of the models' performance in classifying network traffic flows and detecting botnet attacks.