Anti-Obfuscation Techniques: Recent Analysis of Malware Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019519" target="_blank" >RIV/62690094:18450/22:50019519 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3233/FAIA220249" target="_blank" >http://dx.doi.org/10.3233/FAIA220249</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/FAIA220249" target="_blank" >10.3233/FAIA220249</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Anti-Obfuscation Techniques: Recent Analysis of Malware Detection

Popis výsledku v původním jazyce

One of the challenging issues in detecting the malware is that modern stealthy malware prefers to stay hidden during their attacks on our devices and be obfuscated. They can evade antivirus scanners or other malware analysis tools and might attempt to thwart modern detection, including altering the file attributes or performing the action under the pretense of authorized services. Therefore, it&apos;s crucial to understand and analyze how malware implements obfuscation techniques to curb these concerns. This paper is dedicated to presenting an analysis of anti-obfuscation techniques for malware detection. Furthermore, an empirical analysis of the performance evaluation of malware detection using machine learning algorithms and the obfuscation techniques was conducted to address the associated issues that might help researchers plan and generate an efficient algorithm for malware detection. © 2022 The authors and IOS Press. All rights reserved.

Název v anglickém jazyce

Anti-Obfuscation Techniques: Recent Analysis of Malware Detection

Popis výsledku anglicky

One of the challenging issues in detecting the malware is that modern stealthy malware prefers to stay hidden during their attacks on our devices and be obfuscated. They can evade antivirus scanners or other malware analysis tools and might attempt to thwart modern detection, including altering the file attributes or performing the action under the pretense of authorized services. Therefore, it&apos;s crucial to understand and analyze how malware implements obfuscation techniques to curb these concerns. This paper is dedicated to presenting an analysis of anti-obfuscation techniques for malware detection. Furthermore, an empirical analysis of the performance evaluation of malware detection using machine learning algorithms and the obfuscation techniques was conducted to address the associated issues that might help researchers plan and generate an efficient algorithm for malware detection. © 2022 The authors and IOS Press. All rights reserved.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Frontiers in Artificial Intelligence and Applications

ISBN

978-1-64368-316-4

ISSN

0922-6389

e-ISSN

1535-6698

Počet stran výsledku

12

Strana od-do

181-192

Název nakladatele

IOS Press BV

Místo vydání

Amsterdam

Místo konání akce

Kitakyushu

Datum konání akce

20. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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