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Enhancing Cyber Security Through Generative Adversarial Networks
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Author(s): Varkha Kumarlal Jewani (Sant Gadge Baba Amravati University, India), Prafulla E. Ajmre (Sant Gadge Baba Amravati University, India), Mohammad Atique (Sant Gadge Baba Amravati University, India)and Suhashini Chaurasia (S.S. Maniar College of Computer and Management, India)
Copyright: 2024
Pages: 16
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.ch013
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Abstract
GANs, or generative adversarial networks, are becoming a very useful tool in many fields, such as data generation, natural language processing, and computer vision. They still have untapped potential to strengthen cyber security protocols, nevertheless. The uses and ramifications of using GANs to improve cyber security are explored in this abstract. First, by using GANs for data augmentation, realistic and varied datasets that are essential for training malware classifiers, anomaly detection models, and intrusion detection systems can be created. Through the generation of synthetic data that closely mimics real-world cyber threats, GANs enable more thorough and efficient training, strengthening security mechanisms against emerging cyber-attacks in the process. GANs help with security testing and password cracking. This helps security experts assess how strong passwords are and strengthen authentication procedures to fend off possible intrusions.
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