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A Review of Deep Learning Strategies for Enhancing Cybersecurity in Networks

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Field Value
 
Title A Review of Deep Learning Strategies for Enhancing Cybersecurity in Networks
 
Creator A J, Bhuvaneshwari
Kaythry, P
 
Subject Cyber attacks
Deep learning models
Network vulnerabilities
Security solutions
Threats
 
Description 1316-1330
Rapid technological improvements have brought significant hazards to sensitive data and information. Cyberspace has
connected various data structures, ranging from private communications/transactions to government activities. Cyberattacks
are growing more complex which emphasizes the need to improve cybersecurity. Cyber security is more crucial as
everything becomes more digital and as the number of connected devices keeps increasing. Cyber security techniques are
used to keep networks, applications, and devices safe from intruders. Cloud and IoT technologies have expanded the
complexity of computing, communication, and networking infrastructures, making cybercrime prevention more difficult.
It takes a long time to develop threat recognition algorithms by the existing methods. Innovative strategies, like employing
deep learning tools for cybersecurity, are anticipated to provide a solution to the issue. Deep learning approaches have many
benefits which include the ability to solve complex problems quickly, high levels of automation, the best use of informal
information, the capacity to generate excellent results at a lower cost, and the ability to recognize complex interactions. A
diverse range of applications can be employed in deep learning models to make decisions based on predictions in the daily
routine. The significant benefits of deep learning-enabled cyber security have improved security and reduced risks. The
intensity of this systematic study provides consolidated knowledge about recent trends and serves as a foundation for future
research in Deep learning-enabled Cybersecurity. This paper highlights the potential challenges and current cybersecurity
issues with cutting-edge Deep Learning technologies.
 
Date 2023-12-29T12:08:29Z
2023-12-29T12:08:29Z
2023-12
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/63130
https://doi.org/10.56042/jsir.v82i12.1702
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.82(11) [November 2023]