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@inproceedings{2346559,
author = {Novák, Pavel and Kaura, Patrik and Oujezský, Václav and Horváth, Tomáš},
address = {Belgium},
booktitle = {2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)},
doi = {http://dx.doi.org/10.1109/ICUMT61075.2023.10333283},
keywords = {Machine learning; ransomware; security; technologies; threats},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Belgium},
isbn = {979-8-3503-9329-3},
pages = {107-110},
publisher = {IEEE},
title = {Ransomware File Detection Using Hashes and Machine Learning},
url = {https://ieeexplore.ieee.org/document/10333283},
year = {2023}
}
TY - JOUR
ID - 2346559
AU - Novák, Pavel - Kaura, Patrik - Oujezský, Václav - Horváth, Tomáš
PY - 2023
TI - Ransomware File Detection Using Hashes and Machine Learning
PB - IEEE
CY - Belgium
SN - 9798350393293
KW - Machine learning
KW - ransomware
KW - security
KW - technologies
KW - threats
UR - https://ieeexplore.ieee.org/document/10333283
N2 - 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.
ER -
NOVÁK, Pavel, Patrik KAURA, Václav OUJEZSKÝ a Tomáš HORVÁTH. Ransomware File Detection Using Hashes and Machine Learning. Online. In \textit{2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)}. Belgium: IEEE, 2023, s.~107-110. ISBN~979-8-3503-9329-3. Dostupné z: https://dx.doi.org/10.1109/ICUMT61075.2023.10333283.
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