Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50021179" target="_blank" >RIV/62690094:18450/23:50021179 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10068497" target="_blank" >https://ieeexplore.ieee.org/document/10068497</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3256979" target="_blank" >10.1109/ACCESS.2023.3256979</a>

Result language

angličtina

Original language name

Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

Original language description

Malware has emerged as a cyber security threat that continuously changes to target computer systems, smart devices, and extensive networks with the development of information technologies. As a result, malware detection has always been a major worry and a difficult issue, owing to shortcomings in performance accuracy, analysis type, and malware detection approaches that fail to identify unexpected malware attacks. This paper seeks to conduct a thorough systematic literature review (SLR) and offer a taxonomy of machine learning methods for malware detection that considers these problems by analyzing 77 chosen research works related to malware detection using machine learning algorithm. The research investigates malware and machine learning in the context of cybersecurity, including malware detection taxonomy and machine learning algorithm classification into numerous categories. Furthermore, the taxonomy was used to evaluate the most recent machine learning algorithm and analysis. The paper also examines the obstacles and associated concerns encountered in malware detection and potential remedies. Finally, to address the related issues that would motivate researchers in their future work, an empirical study was utilized to assess the performance of several machine learning algorithms.

Czech name

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Czech description

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Type

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

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Volume of the periodical

11

Issue of the periodical within the volume

March

Country of publishing house

US - UNITED STATES

Number of pages

45

Pages from-to

141045-141089

UT code for WoS article

001130213200001

EID of the result in the Scopus database

2-s2.0-85151320871