The rapid evolution of malware poses an ever-growing challenge to cybersecurity professionals and organizations worldwide. As malicious software becomes more sophisticated, traditional detection methods often fall short, necessitating advanced solutions that not only identify threats but also provide clear explanations for their predictions. This book, Transparent AI Defenses: A Random Forest Approach Augmented by SHAP for Malware Threat Evaluation, emerges from this critical need, offering a comprehensive exploration of an explainable artificial intelligence (XAI) framework tailored for malware analysis. Our journey began with a desire to bridge the gap between the predictive power of machine learning and the interpretability demanded by security experts. The Random Forest algorithm, known for its robustness, serves as the backbone of our approach, while SHAP (SHapley Additive exPlanations) enhances it by delivering actionable insights into feature importance.
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- Manas Yogi (Autor:in), Pendyala Devi Sravanthi (Autor:in), 2025, Transparent AI Defenses. A Random Forest Approach Augmented by SHAP for Malware Threat Evaluation, München, GRIN Verlag, https://www.grin.com/document/1617469
