A Brief Overview of Deep Learning Approaches for IoT Security

Abstract

The continuous increasing usage of internet devices in many areas of human life is continuously growing and demanding a proper method to protect these IoT devices from cyber-attacks and vulnerabilities. In this aspect, deep learning neural networks are a very helpful and beneficial method to provide protection to internet devices in many aspects. This study's objective is to systematically research and examine the background of research about neural networks tactics functional to different internet-based devices' safety use-cases. We put the influences we looked at into many different groups to show where this research is lacking. This particular research or findings mainly captures the articles related to “Deep neural networks”, “Cyber risks” and “IOT” in four databanks, namely Science Direct, ACM Digital Library, IEEEXplore and Springer Link. These studies concern 3 main studies, which involve safety aspects, their own Deep Learning network designs, and the occupied datasets. The final round is to find out the loopholes to be examined in the future and vulnerabilities and drawbacks in the IoT security scenario.