Analysis of KDD-Cup'99, NSL-KDD and UNSW-NB15 Datasets using Deep Learning in IoT

Internet of Things (IoT) network is the latest technology which is used to connect all the objects near us. Implementation of IoT technology is latest and growing day-by-day, it is coming with risk itself. So, it required the most efficient model to detect malicious activities as fast as possible and accurate. In our paper, we considered Deep Neural Network (DNN) for identifying the attacks in IoT. Intelligent intrusion detection system can only be built if there is availability of an effective dara set. Performance of DNN to correctly identify the attack has been evaluated on the most used data sets, i.e., KDD-Cup'99, NSL-KDD, and UNSW-NB15. Our experimental results showed the accuracy rate of the proposed method using DNN. It showed that accuracy rate is above 90% with each dataset. (C) 2020 The Authors. Published by Elsevier B.V.