Adversarial Machine Learning in Indian Cybersecurity: Threats and Mitigation Strategies

Abstract

With the rise of machine learning (ML) in cybersecurity, adversarial attacks targeting ML models pose new challenges to Indian enterprises. This paper explores the vulnerabilities of machine learning models to adversarial attacks, particularly in critical Indian sectors such as finance, healthcare, and government. The study reviews attack vectors such as evasion, poisoning, and inference attacks, and suggests robust mitigation strategies like adversarial training, gradient masking, and defense distillation. Case studies from Indian organizations implementing adversarial-resistant ML models are analyzed to demonstrate real-world applications and effectiveness.