Abstract: Malware identification and prevention are crucial for computer security to safeguard data from harmful software. Traditional methods for detecting malware, such as signature-based, heuristic-based, and anomaly-based, cannot keep up with the dynamic and polymorphic nature of next-generation malware. In answer to this obstacle, deep learning (DL) and machine learning (ML) detection algorithms have made significant progress. However, adversarial attacks on deep learning and machine learning models have made these cutting-edge detection methods vulnerable. Consequently, the research community feels the strain to develop advanced attack mechanisms and defenses for these detection models, which emerged as a new discipline named Adversarial Machine Learning (AML) in the AI-enabled cybersecurity domain This presentation will demonstrate an overview of adversarial machine learning, focusing on its application in malware detection systems using ML and DL approaches.