Ransomware File Detection Using Hashes and Machine Learning

D 2023

Ransomware File Detection Using Hashes and Machine Learning

NOVÁK, Pavel, Patrik KAURA, Václav OUJEZSKÝ and Tomáš HORVÁTH

Basic information

Original name

Ransomware File Detection Using Hashes and Machine Learning

Authors

NOVÁK, Pavel (203 Czech Republic, guarantor, belonging to the institution), Patrik KAURA (203 Czech Republic, belonging to the institution), Václav OUJEZSKÝ (203 Czech Republic, belonging to the institution) and Tomáš HORVÁTH (203 Czech Republic, belonging to the institution)

Edition

Belgium, 2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), p. 107-110, 4 pp. 2023

Publisher

IEEE

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

20203 Telecommunications

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14330/23:00132429

Organization unit

Faculty of Informatics

ISBN

979-8-3503-9329-3

ISSN

DOI

http://dx.doi.org/10.1109/ICUMT61075.2023.10333283

Keywords in English

Machine learning; ransomware; security; technologies; threats

Abstract

V originále

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.

Links

VK01030030, research and development project

Name: Systém pro zálohování a ukládání dat s integrovanou aktivní ochranou proti kybernetickým hrozbám

Investor: Ministry of the Interior of the CR, Data backup and storage system with integrated active protection against cyber threats

Citovat

NOVÁK, Pavel, Patrik KAURA, Václav OUJEZSKÝ and Tomáš HORVÁTH. Ransomware File Detection Using Hashes and Machine Learning. Online. In 2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). Belgium: IEEE, 2023, p. 107-110. ISBN 979-8-3503-9329-3. Available from: https://dx.doi.org/10.1109/ICUMT61075.2023.10333283.

@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 and 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, p.~107-110. ISBN~979-8-3503-9329-3. Available from: https://dx.doi.org/10.1109/ICUMT61075.2023.10333283.

Detailed Information on Publication Record