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

Purushottam Sharma (), Shaurya Kapoor and Richa Sharma
Additional contact information
Purushottam Sharma: Amity University Uttar Pradesh
Shaurya Kapoor: Amity University Uttar Pradesh
Richa Sharma: Amity University Uttar Pradesh

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 1, No 25, 287-296

Abstract: 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.

Keywords: Ransomware; Honeypot; Machine learning; Data protection; Detection techniques (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-022-01793-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01793-0

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-022-01793-0

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().