Detailed Information on Publication Record
2024
Multistage Malware Detection Method for Backup Systems
NOVÁK, Pavel, Václav OUJEZSKÝ, Patrik KAURA, Tomáš HORVÁTH, Martin HOLÍK et. al.Basic information
Original name
Multistage Malware Detection Method for Backup Systems
Authors
NOVÁK, Pavel (203 Czech Republic, belonging to the institution), Václav OUJEZSKÝ (203 Czech Republic, guarantor, belonging to the institution), Patrik KAURA (203 Czech Republic, belonging to the institution), Tomáš HORVÁTH (203 Czech Republic, belonging to the institution) and Martin HOLÍK (203 Czech Republic, belonging to the institution)
Edition
TECHNOLOGIES, SWITZERLAND, MDPI, 2024, 2227-7080
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
20203 Telecommunications
Country of publisher
Switzerland
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 3.600 in 2022
Organization unit
Faculty of Informatics
UT WoS
001172262100001
Keywords in English
backup; detection; hashes; malware; model; machine learning; system
Tags
International impact, Reviewed
Změněno: 29/5/2024 14:25, doc. Ing. Václav Oujezský, Ph.D.
Abstract
V originále
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.
Links
VK01030030, research and development project |
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