Deep Learning Models for Cyber Security in IoT Networks: A Review
Kosrat Dlshad Ahmed, Shavan K. Askar · Zenodo (CERN European Organization for Nuclear Research) · 2021
The IoT systems and connectivity provide improved experience and improve the quality of service for the users in different perspectives. Recent development of the technological prospects and management of the sufficient aspects for the delivery of performance need to be ensured in this regard. The concept of IoT is related with the widely connected features, systems, data storage facilities, management processes, applications, devices, users, gateways, services and thousands of other elements. As the importance of IoT applications has been growing in recent times, the prospects for development and management are immense for the development opportunities. In recent times, cybersecurity and ensuring privacy for the users have attracted attention of the users. With growing popularity of the social media platforms, more and more people are becoming connected. With growing opportunity of connectivity, people need more secured space to connect. In this article, different aspects of the cybersecurity based on the deep learning models and analyzing the concepts of machine learning, understanding the concept of security and privacy, contributing to the development and management of cybersecurity etc. To demonstrate the understanding of cybersecurity in the IoT networks, effective deep learning models such as MLP, CNN, LSTP and a hybrid model of CNN and LSTP have been analyzed. To contribute to the learning process, future research opportunities have also been identified.