Record Details

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

NOPR - NISCAIR Online Periodicals Repository

View Archive Info
 
 
Field Value
 
Title Enhancing Network Forensic and Deep Learning Mechanism for Internet of Things Networks
 
Creator Avanija, J
Kumar, K E Naresh
Kumari, Ch Usha
Jyothi, G Naga
Raju, K Srujan
Madhavi, K Reddy
 
Subject Attack tracing
Botnets
IOT
Network forensics
Particle swarm optimization
 
Description 522-528
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.
 
Date 2023-05-09T08:33:03Z
2023-05-09T08:33:03Z
2023-05
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61862
https://doi.org/10.56042/jsir.v82i05.1084
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(05) [May 2023]