Enhancing Network Forensic and Deep Learning Mechanism for Internet of Things Networks

The integration of intelligence into everyday products has been possible due to the ongoing shrinking of hardware and a rise in power efficiency. The Internet of Things (IoT) area arose from the tendency to add computational capabilities to so-called non-intelligent daily items. IoT systems are attractive targets for cyber-attacks because they have many applications. Adversaries use a variety of Advanced Persistent Threat (APT) strategies and trace the source of cyber-attack events to safeguard IoT networks. The Particle Deep Framework (PDF), which is proposed in this study, is a novel Network Forensics (NF) that encompasses the digital investigative phases for spotting & tracing attack activity in IoT networks. The suggested framework contains three novel functionalities for dealing with encrypted networks, such as collecting network data flows & confirming their integrity, using a PSO algorithm, "Bot-IoT "& "UNSW NB15" datasets. The suggested PDF is related to several deep-learning methods. Experimental outcomes show that the proposed framework is very good at discovering & tracing cyber-attack occurrences when compared to existing approaches. The proposed design is implemented using neural network technology. The proposed design has 10% accuracy when compared with the existing structure. This paper is expected to offer a quick reference for researchers interested in understanding the use of network forensics and IOT.