Ransomware File Detection Using Hashes and Machine Learning

D 2023

Ransomware File Detection Using Hashes and Machine Learning

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

Základní údaje

Originální název

Ransomware File Detection Using Hashes and Machine Learning

Autoři

NOVÁK, Pavel (203 Česká republika, garant, domácí), Patrik KAURA (203 Česká republika, domácí), Václav OUJEZSKÝ (203 Česká republika, domácí) a Tomáš HORVÁTH (203 Česká republika, domácí)

Vydání

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

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

20203 Telecommunications

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14330/23:00132429

Organizační jednotka

Fakulta informatiky

ISBN

979-8-3503-9329-3

ISSN

DOI

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

Klíčová slova anglicky

Machine learning; ransomware; security; technologies; threats

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 29. 5. 2024 14:34, doc. Ing. Václav Oujezský, Ph.D.

Anotace

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.

Návaznosti

VK01030030, projekt VaV

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

Investor: Ministerstvo vnitra ČR, Systém pro zálohování a ukládání dat s integrovanou aktivní ochranou proti kybernetickým hrozbám

Citovat

NOVÁK, Pavel, Patrik KAURA, Václav OUJEZSKÝ a 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, s. 107-110. ISBN 979-8-3503-9329-3. Dostupné z: 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 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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