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Explainable AI (XAI) for Ransomware Detection at Pre-Encryption Stage (PES)

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Proposed model detects ransomware at the pre-encryption stage, utilizing explainable AI techniques, suggesting effective defense strategies.

Achieved 97.5% detection accuracy using the random forest classifier, which indicates high effectiveness.
Utilized explainable AI methods like SHAP and LIME to provide interpretable insights into ransomware detection.
Employs dynamic behavioral characteristics from system and API calls, enhancing detection efficiency and reliability.
Integration of SHAP and LIME allows for real-time responses and supports operational trust in cybersecurity.

Wadho et al. (2025) studied this question.

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View Full Paper

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Authors

Proposed model detects ransomware at the pre-encryption stage, utilizing explainable AI techniques, suggesting effective defense strategies.

Achieved 97.5% detection accuracy using the random forest classifier, which indicates high effectiveness.
Utilized explainable AI methods like SHAP and LIME to provide interpretable insights into ransomware detection.
Employs dynamic behavioral characteristics from system and API calls, enhancing detection efficiency and reliability.
Integration of SHAP and LIME allows for real-time responses and supports operational trust in cybersecurity.

Wadho et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark