Publication details
Ransomware File Detection Using Hashes and Machine Learning
| Authors | |
|---|---|
| Year of publication | 2023 |
| Type | Article in Proceedings |
| Conference | 2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) |
| MU Faculty or unit | |
| Citation | |
| Web | https://ieeexplore.ieee.org/document/10333283 |
| Doi | http://dx.doi.org/10.1109/ICUMT61075.2023.10333283 |
| Keywords | Machine learning; ransomware; security; technologies; threats |
| Description | This article explores the integration of machine learning hash analysis within a backup system to proactively detect ransomware threats. By combining multiple data sources and employing intelligent algorithms, the proposed system enhances the detection accuracy and mitigates the risk of data loss caused by ransomware attacks. The integration of machine learning techniques enables real-time analysis of cryptographic hash values, facilitating rapid identification and proactive defense against evolving ransomware variants. Through this approach, organizations can bolster their cybersecurity strategies and safe-guard critical data from malicious encryption attempts. |
| Related projects: |