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@article{2376880,
author = {Novák, Pavel and Oujezský, Václav and Kaura, Patrik and Horváth, Tomáš and Holík, Martin},
article_location = {SWITZERLAND},
article_number = {12(2)},
doi = {http://dx.doi.org/10.3390/technologies12020023},
keywords = {backup; detection; hashes; malware; model; machine learning; system},
language = {eng},
issn = {2227-7080},
journal = {TECHNOLOGIES},
title = {Multistage Malware Detection Method for Backup Systems},
url = {https://doi.org/10.3390/technologies12020023},
volume = {23},
year = {2024}
}
TY - JOUR
ID - 2376880
AU - Novák, Pavel - Oujezský, Václav - Kaura, Patrik - Horváth, Tomáš - Holík, Martin
PY - 2024
TI - Multistage Malware Detection Method for Backup Systems
JF - TECHNOLOGIES
VL - 23
IS - 12(2)
SP - 1-16
EP - 1-16
PB - MDPI
SN - 22277080
KW - backup
KW - detection
KW - hashes
KW - malware
KW - model
KW - machine learning
KW - system
UR - https://doi.org/10.3390/technologies12020023
N2 - This paper proposes an innovative solution to address the challenge of detecting latent malware in backup systems. The proposed detection system utilizes a multifaceted approach that combines similarity analysis with machine learning algorithms to improve malware detection. The results demonstrate the potential of advanced similarity search techniques, powered by the Faiss model, in strengthening malware discovery within system backups and network traffic. Implementing these techniques will lead to more resilient cybersecurity practices, protecting essential systems from hidden malware threats. This paper’s findings underscore the potential of advanced similarity search techniques to enhance malware discovery in system backups and network traffic, and the implications of implementing these techniques include more resilient cybersecurity practices and protecting essential systems from malicious threats hidden within backup archives and network data. The integration of AI methods improves the system’s efficiency and speed, making the proposed system more practical for real-world cybersecurity. This paper’s contribution is a novel and comprehensive solution designed to detect latent malware in backups, preventing the backup of compromised systems. The system comprises multiple analytical components, including a system file change detector, an agent to monitor network traffic, and a firewall, all integrated into a central decision-making unit. The current progress of the research and future steps are discussed, highlighting the contributions of this project and potential enhancements to improve cybersecurity practices.
ER -
NOVÁK, Pavel, Václav OUJEZSKÝ, Patrik KAURA, Tomáš HORVÁTH a Martin HOLÍK. Multistage Malware Detection Method for Backup Systems. \textit{TECHNOLOGIES}. SWITZERLAND: MDPI, 2024, roč.~23, 12(2), s.~1-16. ISSN~2227-7080. Dostupné z: https://dx.doi.org/10.3390/technologies12020023.
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