Ethical and Privacy Considerations in GAN-Based Security
Generative Adversarial Networks (GANs) have caused a great transformation in a number of fields, including anomaly detection, healthcare, data encryption, deep learning (DL), machine learning (ML), and entertainment. They are also employed in various security applications. This chapter explores mainly the privacy and ethical concerns associated with the use of GANs. Although GANs have enormous possibilities for security applications such as biometrics, intrusion detection, and data augmentation, implementing them requires careful consideration regarding privacy and ethical issues. GANs ability to produce remarkably lifelike deepfakes poses a risk since it might promote social engineering attacks. Moreover, these discrepancies may be increased by biased security programs that arise from biases in training data. Even with all of the potential benefits, ethical and privacy concerns must be prioritized for the safe and reliable application of GANs in security. This chapter also lays the foundation of GAN-based applications in Cybersecurity. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors.
Resmi, K.R.; Joseph, Amitha; George, Bindu; Job, Dhanya; Cyriac, Sebastian
Generative Adversarial Networks for Cybersecurity:: Protecting Data and Networks;pp.235-245
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https://doi.org/10.1201/9781003652991-17 https://www.scopus.com/pages/publications/105040316894?origin=resultslist
Resmi K.R., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Joseph A., Department of Computer Science, St Alberts College (Autonomous), Kerala, Ernakulam, India; George B., Department of Computer Science, Nirmala College, Kerala, Muvattupuzha, India; Job D., Department of Computer Applications, BPC College, Kerala, Piravom, India; Cyriac S., Department of Information Technology, Southeast College, Weyburn, SK, Canada