A Deep Learning Approach Against Botnet Attacks to Reduce the Interference Problem of IoT

Today we are witnessing a world where hacking into a user’s computer using tiny bots or intercepting a group of interconnected devices is no more impossible. These tiny bots are called botnets which are a group of malicious codes that can hamper the whole security system without the knowledge of the user. As Internet of Things (IoT) is emerging rapidly, the interconnected devices are susceptible to breach as one affected device can hamper the whole network. The security threat remains as botnet attacks increase their presence to the interconnected devices. In this work, we are implementing Restricted Boltzmann Machine (RBM) algorithm of deep learning approach on the CTU-13 dataset to train the algorithm about the botnet attack patterns in IoT and to prevent the botnet attacks on IoT devices, thus reducing the interference problem in the network.