Attack Classification Analysis of IoT Network via Deep Learning Approach

Abstract

A variety of attacks in the transportation layer of IoT network seeks for a detection and preventionmechanism such as intrusion detection systems (IDSs). Anomaly detection is one of the most demandingtask in IDSs. It requires a robust classifier model which is able to detect different kinds ofattacks intelligently. This paper addresses deep neural network for classifying attacks in IoT network.The performance of the proposed method is evaluated on the three novel benchmarking datasets inwired and wireless network environment, i.e. UNSW-NB15, CIDDS-001, and GPRS. Furthermore,deep neural network combined with grid search strategy are utilized to obtain the best parameter settingsfor each dataset. The experimental results demonstrate the effectiveness of our approach usingdeep neural network in terms of accuracy, precision, recall and false alarm rate.