International Journal on Science and Technology

E-ISSN: 2229-7677     Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 17 Issue 1 January-March 2026 Submit your research before last 3 days of March to publish your research paper in the issue of January-March.

Adversarial-Aware Adaptive Defense (AAAD): An AI-Driven Cybersecurity Framework for India’s Digital Infrastructure

Author(s) Mr. SAKET KESAR
Country India
Abstract India’s rapidly expanding digital ecosystem—driven by platforms such as Aadhaar, UPI, and e-governance services—has significantly increased exposure to sophisticated cyber threats. Traditional rule-based cybersecurity systems are reactive, urban-centric, and ineffective against zero-day attacks, multilingual fraud, and low-bandwidth environments. This paper proposes Adversarial-Aware Adaptive Defense (AAAD), an AI-driven cybersecurity framework integrating Generative Adversarial Networks (GANs) for threat simulation and Deep Reinforcement Learning (DRL) for real-time adaptive policy optimization. AAAD is designed for edge deployment, enabling offline threat detection, multilingual fraud identification, and privacy-preserving federated learning. Experimental evaluation demonstrates reduced false-positive rates, lower detection latency, and cost-efficient deployment compared to conventional systems. The framework is particularly suited for securing India’s diverse and resource-constrained digital infrastructure, including financial services, identity verification, and healthcare systems.
Keywords Adversarial AI, Cybersecurity, Deep Reinforcement Learning, Generative Adversarial Networks, Edge Computing, UPI Fraud Detection, Aadhaar Security, Multilingual Fraud Detection, AI Defense Systems
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-01-18

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