Explainable AI for Adversarial Machine Learning: Enhancing Transparency and Trust in Cyber Security

Explainable artificial intelligence (XAI) is essential for improving machine learning models' interpretability, transparency, and reliability—especially in challenging and important fields like cybersecurity. These abstract addresses approaches, structures, and evaluation criteria for putting XAI techniques into practice and comparing them, as well as offering a thorough understanding of all the important components of XAI in the context of adversarial machine learning. Model-agnosticism, global/local explanation, adversarial assault resistance, interpretability, computing efficiency, and scalability are all covered in the discussion. Notably, the suggested SHIME approach shows excellent performance in a number of dimensions, making it a promising solution. The need of carefully weighing XAI solutions based on particular application requirements is emphasized in the abstract's conclusion, opening the door for future developments in the field to handle changing difficulties at the nexus of cybersecurity and artificial intelligence.