Survey on Explainable AI Techniques for Malware Detection
/ 1 min read
🕵️♂️ Survey highlights the importance of Explainable AI in malware detection. As machine learning (ML) continues to transform cybersecurity, the lack of transparency in traditional black-box models poses challenges for malware detection. This survey reviews state-of-the-art ML techniques, emphasizing explainable AI (XAI) methods that enhance model interpretability while maintaining detection accuracy. It examines existing XAI frameworks, their applications in malware classification, and the challenges of improving interpretability in security-critical environments. The paper also discusses recent advancements and identifies open research challenges, providing a structured overview that serves as a resource for researchers and practitioners aiming to reconcile ML performance with the need for explainability in cybersecurity.
