Dr. Chandrashekhar Uppin

A proactive approach to network forensics intrusion (denial of service flood attack) using dynamic features, selection and convolution neural network

• Authors Details :  
• George G,  
• Uppin C

Journal title : Open Journal of Physical Science (ISSN: 2734-2123)

Publisher : African Researchers Magazine

Online ISSN : 2734-2123

Page Number : 01-09

Journal volume : 2

Journal issue : 2

462 Views Original Article

Currently, the use of internet-connected applications for storage by different organizations have rapidly increased with the vast need to store data, cybercrimes are also increasing and have affected large organizations and countries as a whole with highly sensitive information, countries like the United States of America, United Kingdom and Nigeria. Organizations generate a lot of information with the help of digitalization, these highly classified information are now stored in databases via the use of computer networks. Thus, allowing for attacks by cybercriminals and state-sponsored agents. Therefore, these organizations and countries spend more resources analyzing cybercrimes instead of preventing and detecting cybercrimes. The use of network forensics plays an important role in investigating cybercrimes; this is because most cybercrimes are committed via computer networks. This paper proposes a new approach to analyzing digital evidence in Nigeria using a proactive method of forensics with the help of deep learning algorithms - Convolutional Neural Networks (CNN) to proactively classify malicious packets from genuine packets and log them as they occur.

Article DOI & Crossmark Data

DOI : https://doi.org/10.52417/ojps.v2i2.237

Article Subject Details

• Artificial Intelligence
• Computer Science
• Computer Science Applications
• Information technology
• Research

Article Keywords Details

• Deep-Learning
• Denial of Service Attacks
• Digital Forensic
• Network Forensics
• Network-monitoring system
• Cybercrime

Article File

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