Ransomware and Artificial Intelligence: A Comprehensive Systematic Review of Reviews

2026-03-13

AI Summary

This study addresses the growing sophistication of ransomware, including its adversarial attacks on AI models and the scarcity of high-quality data, by conducting the first “review of reviews” based on the PRISMA framework to systematically synthesize research on AI-driven ransomware defense from 2020 to 2024. Focusing on the integration of static and dynamic analysis, anomaly detection, and pre-encryption early-warning mechanisms, the work proposes an AI-powered defense roadmap that bridges theoretical advances with practical implementation. The research validates the efficacy of hybrid AI models in enabling real-time response and scalable defense architectures, identifies critical challenges, and offers concrete recommendations and collaborative pathways for researchers, industry practitioners, and policymakers.

Technology Category

Computer Vision: Adversarial Attacks & RobustnessPhilosophy and Ethics of AI: Privacy & SecuritySearch and Optimization: Metareasoning and Metaheuristics

Application Category

Security and Privacy: Cyber-crime defenses and forensicsResponsible Web: Algorithmic accountability and transparency on the webUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This study provides a comprehensive synthesis of Artificial Intelligence (AI), especially Machine Learning (ML) and Deep Learning (DL), in ransomware defense. Using a "review of reviews" methodology based on PRISMA, this paper gathers insights on how AI is transforming ransomware detection, prevention, and mitigation strategies during the past five years (2020-2024). The findings highlight the effectiveness of hybrid models that combine multiple analysis techniques such as code inspection (static analysis) and behavior monitoring during execution (dynamic analysis). The study also explores anomaly detection and early warning mechanisms before encryption to address the increasing complexity of ransomware. In addition, it examines key challenges in ransomware defense, including techniques designed to deceive AI-driven detection systems and the lack of strong and diverse datasets. The results highlight the role of AI in early detection and real-time response systems, improving scalability and resilience. Using a systematic review-of-reviews approach, this study consolidates insights from multiple review articles, identifies effective AI models, and bridges theory with practice to support collaboration among academia, industry, and policymakers. Future research directions and practical recommendations for cybersecurity practitioners are also discussed. Finally, this paper proposes a roadmap for advancing AI-driven countermeasures to protect critical systems and infrastructures against evolving ransomware threats.

Problem

• ransomware
• artificial intelligence
• cybersecurity
• malware detection
• adversarial attacks

Innovation

• review of reviews
• hybrid AI models
• ransomware early detection
• adversarial attacks on AI
• systematic literature review