Explainable Malware Analysis: Concepts, Approaches and Challenges
<|reference_start|>Explainable Malware Analysis: Concepts, Approaches and Challenges: Machine learning (ML) has seen exponential growth in recent years, finding applications in various domains such as finance, medicine, and cybersecurity. Malware remains a significant threat to modern computing, frequently used by attackers to compromise systems. While numerous machine learning-based approaches for malware detection achieve high performance, they often lack transparency and fail to explain their predictions. This is a critical drawback in malware analysis, where understanding the rationale behind detections is essential for security analysts to verify and disseminate information. Explainable AI (XAI) addresses this issue by maintaining high accuracy while producing models that provide clear, understandable explanations for their decisions. In this survey, we comprehensively review the current state-of-the-art ML-based malware detection techniques and popular XAI approaches. Additionally, we discuss research implementations and the challenges of explainable malware analysis. This theoretical survey serves as an entry point for researchers interested in XAI applications in malware detection. By analyzing recent advancements in explainable malware analysis, we offer a broad overview of the progress in this field, positioning our work as the first to extensively cover XAI methods for malware classification and detection.<|reference_end|>
@article{manthena2024explainable,
    title={Explainable Malware Analysis: Concepts, Approaches and Challenges},
    author={Harikha Manthena, Shaghayegh Shajarian, Jeffrey Kimmell, Mahmoud
  Abdelsalam, Sajad Khorsandroo, and Maanak Gupta},
    journal={arXiv preprint arXiv:2409.13723},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13723},
    primaryClass={cs.CR cs.AI}
}