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Ransomware Detection, Avoidance, and Mitigation Scheme: A Review and Future Directions

Author

Listed:
  • Adhirath Kapoor

    (Model Institute of Engineering and Technology (Autonomous), Jammu 181122, India)

  • Ankur Gupta

    (Model Institute of Engineering and Technology (Autonomous), Jammu 181122, India)

  • Rajesh Gupta

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India)

  • Sudeep Tanwar

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India)

  • Gulshan Sharma

    (Department of Electrical Power Engineering, Steve Biko Campus, Durban University of Technology, Durban 4001, South Africa)

  • Innocent E. Davidson

    (Department of Electrical Power Engineering, Steve Biko Campus, Durban University of Technology, Durban 4001, South Africa)

Abstract

Ransomware attacks have emerged as a major cyber-security threat wherein user data is encrypted upon system infection. Latest Ransomware strands using advanced obfuscation techniques along with offline C2 Server capabilities are hitting Individual users and big corporations alike. This problem has caused business disruption and, of course, financial loss. Since there is no such consolidated framework that can classify, detect and mitigate Ransomware attacks in one go, we are motivated to present Detection Avoidance Mitigation (DAM), a theoretical framework to review and classify techniques, tools, and strategies to detect, avoid and mitigate Ransomware. We have thoroughly investigated different scenarios and compared already existing state of the art review research against ours. The case study of the infamous Djvu Ransomware is incorporated to illustrate the modus-operandi of the latest Ransomware strands, including some suggestions to contain its spread.

Suggested Citation

  • Adhirath Kapoor & Ankur Gupta & Rajesh Gupta & Sudeep Tanwar & Gulshan Sharma & Innocent E. Davidson, 2021. "Ransomware Detection, Avoidance, and Mitigation Scheme: A Review and Future Directions," Sustainability, MDPI, vol. 14(1), pages 1-24, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2021:i:1:p:8-:d:707240
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    Citations

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    Cited by:

    1. You-Shyang Chen & Jerome Chih-Lung Chou & Yu-Sheng Lin & Ying-Hsun Hung & Xuan-Han Chen, 2023. "Identification of SMEs in the Critical Factors of an IS Backup System Using a Three-Stage Advanced Hybrid MDM–AHP Model," Sustainability, MDPI, vol. 15(4), pages 1-29, February.
    2. Pigola, Angélica & Da Costa, Priscila Rezende & Ferasso, Marcos & Cavalcanti da Silva, Luís Fabio, 2024. "Enhancing cybersecurity capability investments: Evidence from an experiment," Technology in Society, Elsevier, vol. 76(C).

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