Utilizing Deep Learning to Identify Potentially Dangerous Routing Attacks in the IoT

Abstract

Due to the rapid increase of cyber-security difficulties brought about by sophisticated assaults such as data injection attacks, replay attacks, etc., the design of cyber-attack detection and control systems has emerged as an essential subfield within cyber-physical systems (CPSs) during the past few years. The outcome of these attacks could be a system failure, malfunctioning, or other undesirable effects. Consequently, it may be necessary to implement the cyber defense system in preparation for impending CPSs to have an improved security system. The various cyber-attack detection schemes based on deep learning algorithms have been intended to detect and mitigate the cyber-attacks that can be launched against CPSs, smart grids, power systems, and other similar infrastructure. This article comprehensively reviews several different deep learning algorithms suggested for use in CPSs to accomplish cyber defense. In the beginning, several methods devised by earlier academics are analyzed in great detail. After that, a comparison study is performed to determine the shortcomings of each algorithm and offer a recommendation for how further improvements to CPSs might be made more effectively.