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Enabling Safety and Security Through GANs and Cybersecurity Synergy for Robust Protection
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Author(s): Aswathy K. Cherian (SRM Institute of Science and Technology, India), Vinoth (SRM Institute of Science and Technology, India), M. Vaidhehi (SRM Institute of Science and Technology, India), S. Ushasukhanya (SRM Institute of Science and Technology, India)and Naga Malleswari (SRM Institute of Science and Technology, India)
Copyright: 2024
Pages: 10
Source title:
Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs)
Source Author(s)/Editor(s): Sivaram Ponnusamy (Sandip University, Nashik, India), Jilali Antari (Ibn Zohr Agadir University, Morocco), Pawan R. Bhaladhare (Sandip University, Nashik, India), Amol D. Potgantwar (Sandip University, Nashik, India)and Swaminathan Kalyanaraman (Anna University, Trichy, India)
DOI: 10.4018/979-8-3693-3597-0.ch011
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Abstract
In the realm of cybersecurity, machine learning emerges as an indispensable tool for advanced threat detection and protection against digital vulnerabilities. GANs, positioned as a potent machine learning paradigm, transcend their traditional role in data generation, showcasing their potential to outsmart detection systems. This chapter sheds light on the evolving challenges posed by GANs in cybersecurity, underscoring the imperative for thorough assessments, especially within the context of intrusion detection systems. While numerous successes characterize various GAN applications, this chapter emphasizes the pressing need to investigate GANs' specific impact on cybersecurity enhancements. GANs not only excel in data generation but also serve as catalysts for novel avenues in privacy and security-oriented research. The chapter concludes by accentuating the limited depth of existing assessments on GANs in privacy and security, urging further exploration to unravel the multifaceted influence of GANs in shaping the future of digital security frameworks.
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