Hybrid Deep Neural Network Model for Detection of Security Attacks in IoT Enabled Environment

The extensive use of Internet of Things (IoT) appliances has greatly contributed in the growth of smart cities. Moreover, the smart city deploys IoT-enabled applications, communications, and technologies to improve the quality of life, people's wellbeing, quality of services for the service providers and increase the operational efficiency. Nevertheless, the expansion of smart city network has become the utmost hazard due to increased cyber security attacks and threats. Consequently, it is more significant to develop the system models for preventing the attacks and also to protect the IoT devices from hazards. This paper aims to present a novel deep hybrid attack detection method. The input data is subjected for preprocessing phase. Here, data normalization process is carried out. From the preprocessed data, the statistical and higher order statistical features are extracted. Finally, the extracted features are subjected to hybrid deep learning model for detecting the presence of attack. The proposed hybrid classifier combines the models like Convolution Neural Network (CNN) and Deep Belief Network (DBN). To make the detection more precise and accurate, the training of CNN and DBN is carried out by using Seagull Adopted Elephant Herding optimization (SAEHO) model by tuning the optimal weights.