International Journal on Science and Technology
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Volume 17 Issue 1
January-March 2026
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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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IJSAT DOI prefix is
10.71097/IJSAT
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