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