February 2026
Intermediate to advanced
496 pages
13h 3m
English
book
Generative Artificial Intelligence for Next-Generation Security Paradigms
by Santosh Kumar Srivastava, Durgesh Srivastava, Manoj Kumar Mahto, Ben Othman Soufiane, Praveen Kantha
Content preview from Generative Artificial Intelligence for Next-Generation Security Paradigms
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14Leveraging Generative AI for Advanced Threat Detection in Cybersecurity
Anuradha Reddy1, Mamatha Kurra2, G. S. Pradeep Ghantasala3* and Pellakuri Vidyullatha4
1Department of CSE (AI&ML), Sri Devi Women’s Engineering College, V.N. Pally, Near Gandipet, RR District, Hyderabad, India
2Department of Computer Science & Engineering, Malla Reddy Institute of Technology & Science, Maisammaguda, Secunderabad, India
3Department of Computer Science and Engineering, Alliance College of Engineering and Design, Alliance University, Bengaluru, India
4Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
Abstract
The landscape of cybersecurity is perpetually developing, with intimidation becoming progressively erudite and stimulating to distinguish using outmoded methods alone. In recent years, the amalgamation of deep learning procedures, mainly procreant AI, has emerged as a promising tactic to improve threat discovery abilities. This chapter discovers the application of procreant AI in cybersecurity and its probability to transform threat discovery. Generative AI discusses a subcategory of artificial intelligence that emphasizes on generating novel data samples that are comparable to a given dataset. In cybersecurity, generative AI models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), offer unique advantages for sleuthing and investigating malevolent happenings. These replicas ...
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