Deep Belief Network enhanced intrusion detection system to prevent security breach in the Internet of Things

Abstract

Internet of Things (IoT) is a new age technology, developed with the vision to connect and interconnect all the objects everywhere. This technology enables an overwhelming smartness, which helps the humankind in many ways. Connecting the objects around us, make them communicate with each other towards a mission of intelligent healthcare, safety, Industrial processing applications. As the Internet of Things involved in many various entities and diverse applications, that the vulnerability to unauthorized access is much higher. Today, cyber-attacks faced by the communication networks are very strong and critically alarming. This research represents an intelligent technique or methodology to defend the security breach, developed with the enhancement of Deep Learning algorithms (Deep Belief Network), i.e., Deep Belief Network. This intelligent intrusion detection methodology scrutinizes the malicious activity that is active inside the network, and one tries to get its entry. In this paper, the investigation of embedding the Deep learning methodology is discussed. The DBN enhancement to the security network is compared with standard DGAs and IDS algorithms, and the results are analyzed.