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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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