Explainable Artificial Intelligence for Ransomware Detection

Complex ransomware has become a serious cyber threat, employing sophisticated methods to evade conventional detection systems. Ransomware detection has been enhanced by enhancing the use of Artificial Intelligence (AI) and Machine Learning (ML). The opacity of these models has given rise to concerns about whether they are trustworthy and whether it is possible to interpret how decisions are made by these models. XAI seeks to bridge the knowledge gap by offering comprehensive information on model decision making, thereby promoting transparency and compliance with regulatory requirements. This paper provides a global overview of XAI techniques for ransomware detection, examining their performance, shortcomings, and potential for future research.

• Publication year

  2025

• Venue

  International Conferences on Networking, Information Systems & Security

• Publication date

  2025-04-10

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/NISS66502.2025.00018
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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