Intrusion Detection System for IOT Botnet Attacks Using Deep Learning

The IoT industry is seen intensifying its presence along these recent years. Since devices are small and heterogeneous they can easily fall prey to the cyberattacks. Handling proper up-gradation of network forensic mechanisms for various security attacks like denial service, keylogging, man-in-the-middle etc within networks not easy due large size heterogeneity. Traditional high-end protection systems difficult work in resource constraints network. In this paper, we designed an intrusion detection system based on deep learning uncover DDoS Botnet attacks. dataset used developed a realistic environment Cyber Range Lab centre UNSW Canberra Cyber. traffic data incorporated includes combination normal attack data. A highly extensible Deep Neural Network (DNN) capable headstrong botnet evaluation shows that our DNN outperforms existing with high accuracy precision.