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Ransomware detection, prevention and protection in IoT devices using ML techniques based on dynamic analysis approach

Author

Listed:
  • Purushottam Sharma

    (Amity University Uttar Pradesh)

  • Shaurya Kapoor

    (Amity University Uttar Pradesh)

  • Richa Sharma

    (Amity University Uttar Pradesh)

Abstract

Ransomware has become one of the most influential and most potent cybersecurity threats, which, is expected to damage 20 Billion USD by 2021. The primary issue in dealing with these threats is that ransomware techniques are ever-evolving, and even if we develop a counter-mechanism against one ransomware, the next one will be completely immune to that technique. As a result, it is crucial to focus on broad-spectrum measures to detect and prevent ransomware of a wide variety. Artificial Intelligence can play a huge role in achieving this. Machine Learning is fast becoming a go-to method to detect ransomware; however, the techniques based on ML are generally limited to scanning. However, there are broader aspects to using this technique, work on which has been limited or has not been looked at yet. Another important aspect is preventing the ransomware from affecting the system files and spreading in the file system and preventing the ransomware from affecting the system files and spreading in the file system and protecting the files from the ransomware if prevention fails. Currently, there are no such techniques that provide detection, prevention, and protection in one suit. The method we propose highlights this aspect and aims to provide a complete solution to protect the users against ransomware attacks.

Suggested Citation

  • Purushottam Sharma & Shaurya Kapoor & Richa Sharma, 2023. "Ransomware detection, prevention and protection in IoT devices using ML techniques based on dynamic analysis approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 287-296, February.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01793-0
    DOI: 10.1007/s13198-022-01793-0
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    References listed on IDEAS

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    1. Purushottam Sharma & Kanak Saxena, 2017. "Application of fuzzy logic and genetic algorithm in heart disease risk level prediction," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1109-1125, November.
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