A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions
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Abstract (arXiv)
In recent years, ransomware has been one of the most notorious malware targeting end users, governments, and business organizations. It has become a very profitable business for cybercriminals with revenues of millions of dollars, and a very serious threat to organizations with financial loss of billions of dollars. Numerous studies were proposed to address the ransomware threat, including surveys that cover certain aspects of ransomware research. However, no study exists in the literature that gives the complete picture on ransomware and ransomware defense research with respect to the diversity of targeted platforms. Since ransomware is already prevalent in PCs/workstations/desktops/laptops, is becoming more prevalent in mobile devices, and has already hit IoT/CPS recently, and will likely grow further in the IoT/CPS domain very soon, understanding ransomware and analyzing defense mechanisms with respect to target platforms is becoming more imperative. In order to fill this gap and motivate further research, in this paper, we present a comprehensive survey on ransomware and ransomware defense research with respect to PCs/workstations, mobile devices, and IoT/CPS platforms. Specifically, covering 137 studies over the period of 1990-2020, we give a detailed overview of ransomware evolution, comprehensively analyze the key building blocks of ransomware, present a taxonomy of notable ransomware families, and provide an extensive overview of ransomware defense research (i.e., analysis, detection, and recovery) with respect to platforms of PCs/workstations, mobile devices, and IoT/CPS. Moreover, we derive an extensive list of open issues for future ransomware research. We believe this survey will motivate further research by giving a complete picture on state-of-the-art ransomware research.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2021.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 2; 2025: 1; 2026: 2.
9 papers.
- 2026MalTool: Malicious Tool Attacks on LLM Agentscited by 17
- 2021Demystifying the Transferability of Adversarial Attacks in Computer Networkscited by 9
- 2023Scamming the Scammers: Using ChatGPT to Reply Mails for Wasting Time and Resourcescited by 8
- 2022AI-based Malware and Ransomware Detection Modelscited by 7
- 2024Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware Attack Simulationscited by 1
- 2022Radial Spike and Slab Bayesian Neural Networks for Sparse Data in Ransomware Attackscited by 0
- 2024On the Generalizability of Machine Learning-based Ransomware Detection in Block Storagecited by 0
- 2025Towards Trustworthy Keylogger detection: A Comprehensive Analysis of Ensemble Techniques and Feature Selections through Explainable AIcited by 0
- 2026Cost-Aware Hierarchical Multi-Agent Ransomware Detection and Family Attributioncited by 0
- 2026Cost-Aware Hierarchical Multi-Agent Ransomware Detection and Family Attribution4 Sep 2026
- 2026MalTool: Malicious Tool Attacks on LLM Agents12 Feb 2026
- 2025Towards Trustworthy Keylogger detection: A Comprehensive Analysis of Ensemble Techniques and Feature Selections through Explainable AI22 May 2025
- 2024On the Generalizability of Machine Learning-based Ransomware Detection in Block Storage30 Dec 2024
- 2024Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware Attack Simulations25 Jun 2024
- 2023Scamming the Scammers: Using ChatGPT to Reply Mails for Wasting Time and Resources10 Feb 2023
- 2022AI-based Malware and Ransomware Detection Models5 Jul 2022
- 2022Radial Spike and Slab Bayesian Neural Networks for Sparse Data in Ransomware Attacks29 May 2022
- 2021Demystifying the Transferability of Adversarial Attacks in Computer Networks9 Oct 2021