Adversarial Machine Learning: Understanding and Mitigating Vulnerabilities

Abstract

This paper explores the evolving landscape of machine learning (ML) security by investigating adversarial attacks and developing robust defense mechanisms. This abstract delves into the intricate relationship between ML models and potential vulnerabilities, emphasizing the importance of comprehending adversarial strategies to fortify systems effectively. The study delves into the various types of adversarial attacks, including evasion and poisoning attacks, and analyzes their impact on the reliability and security of ML models across different domains. Furthermore, it examines the underlying mechanisms exploited by adversaries to subvert ML systems and explores countermeasures such as robust training algorithms, adversarial detection techniques, and model interpretability methods. It examines various attack vectors, such as evasion and poisoning attacks, while proposing countermeasures rooted in enhanced model training, feature engineering, and ensemble methods. By fostering a deeper understanding of adversarial dynamics and implementing proactive defense strategies, this research aims to bolster the resilience of ML systems against emerging threats in dynamic environments.