Review of network-forensic analysis optimization using deep learning against attacks on IoT devices

Abstract

The growth in the number of systems based on Internet of Things (IoT), real-time service and automation that can be provided to users is enormous in the last decade. One of the major obstacles in securing IoT based network is tracking and tracing cyber-attack events and their sources. The aim of this study is to analyze current research on deep learning-based network forensic optimization techniques using secondary research. Major findings are that deep learning technology can effectively identify attacks during data communication in IoT systems than the state-of-the-art methods. In this study, major components of the systems proposed by researchers were identified, presented in a table format and classified based on methodology which revealed that deep learning technology can identify attacks in IoT devices.