  Emerging vulnerabilities in machine learning (ML) models due to adversarial
attacks raise concerns about their reliability. Specifically, evasion attacks
manipulate models by introducing precise perturbations to input data, causing
erroneous predictions. To address this, we propose a methodology combining
SHapley Additive exPlanations (SHAP) for feature importance analysis with an
innovative Optimal Epsilon technique for conducting evasion attacks. Our
approach begins with SHAP-based analysis to understand model vulnerabilities,
crucial for devising targeted evasion strategies. The Optimal Epsilon
technique, employing a Binary Search algorithm, efficiently determines the
minimum epsilon needed for successful evasion. Evaluation across diverse
machine learning architectures demonstrates the technique's precision in
generating adversarial samples, underscoring its efficacy in manipulating model
outcomes. This study emphasizes the critical importance of continuous
assessment and monitoring to identify and mitigate potential security risks in
machine learning systems.
