Real world use of Deep Learning Models for Cyber Security in IoT Networks

Abstract

The expanding Internet of Things (IoT) presents new security problems for network activity monitoring as the number of connected devices increases. The majority of Internet of Things devices are susceptible due to a lack of security knowledge on the part of device makers and end users. Therefore, they have become ideal targets for malware writers who seek to convert them into bots and use them to launch large-scale assaults against a wide range of targets.  In this article, we present deep learning models for cyber security in IoT (Internet of Things) networks, which are based on deep reinforcement learning. The Internet of Objects (IoT) network is a promising technology that links both living and nonliving things all over the globe. As IoT adoption increases, cyber security continues to be a weakness, making it vulnerable to many cyber-attacks. For any network to be successful, it is critical that the network be fully safe, or else people may be hesitant to use this technology as a result of the security flaw. As a result of advances in machine learning and soft computing, deep learning (DL) algorithms have recently developed. Many deep learning (DL) algorithms have appeared during the last few years, and they are now in wide use in the scientific community and other areas of application. The use of DL has now become necessary owing to its sharp, speedy, accurate, and strong model building capabilities. It should be noted, however, that while there are now many DL algorithms, a complete list of them has not yet been compiled in the scientific literature. This article presents a comprehensive list of DL algorithms and their respective applicability areas.