Multistage Malware Detection Method for Backup Systems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00139890" target="_blank" >RIV/00216224:14330/24:00139890 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/technologies12020023" target="_blank" >https://doi.org/10.3390/technologies12020023</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/technologies12020023" target="_blank" >10.3390/technologies12020023</a>

Result language

angličtina

Original language name

Multistage Malware Detection Method for Backup Systems

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20203 - Telecommunications

Project

<a href="/en/project/VK01030030" target="_blank" >VK01030030: Data backup and storage system with integrated active protection against cyber threats</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

TECHNOLOGIES

ISSN

2227-7080

e-ISSN

2227-7080

Volume of the periodical

12

Issue of the periodical within the volume

2

Country of publishing house

CH - SWITZERLAND

Number of pages

16

Pages from-to

1-16

UT code for WoS article

001172262100001

EID of the result in the Scopus database

2-s2.0-85185923905