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