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Data Guardians: Empowering Cybersecurity With Generative Adversarial Networks
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Author(s): Ahmet Okan Arik (Istanbul University, Türkiye)and Busra Ozdenizci Kose (Gebze Technical University, Türkiye)
Copyright: 2024
Pages: 22
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.ch008
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Abstract
This chapter provides an in-depth exploration of generative adversarial networks (GANs) and their profound impact on the field of cybersecurity. GANs have evolved from their initial application in image generation to play a crucial role across a wide spectrum of cybersecurity domains, including synthetic data generation, anomaly detection, malware identification, cryptographic key generation, and biometric security enhancement. By detailing the architecture and types of GANs, alongside their application in generating synthetic data for robust security model training and simulating cyber threats, this analysis highlights the versatility and adaptability of GANs in addressing contemporary cybersecurity challenges. Additionally, the chapter confronts the technical challenges associated with GAN development, and navigates the ethical considerations surrounding their use, advocating for responsible deployment and the establishment of ethical guidelines. Through this comprehensive overview, GANs are positioned as indispensable tools in the development of secure digital infrastructures.
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