From malware samples to fractal images: A new paradigm for classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10254672" target="_blank" >RIV/61989100:27240/24:10254672 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/24:10254672

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0378475423004937" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0378475423004937</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.matcom.2023.11.032" target="_blank" >10.1016/j.matcom.2023.11.032</a>

Jazyk výsledku

angličtina

Název v původním jazyce

From malware samples to fractal images: A new paradigm for classification

Popis výsledku v původním jazyce

To date, a large number of research papers have been written on malware classification, identification, classification into different families, and the distinction between malware and goodware. These works have been based on captured malware samples and have attempted to analyse malware and goodware using various techniques like the analysis of malware using malware visualization. These works usually convert malware samples capturing the malware structure into image structures which are then subject to image processing. In this paper, we propose an unconventional and novel approach to malware visualization based on its dynamical analysis, subsequent complex network conversion and fractal geometry, e.g. Julia sets visualization. Very interesting images being subsequently used to classify as malware and goodware. The classification is done by deep learning network. The results of the presented experiments of fractal conversion and subsequent classification are based on a database of 6,589,997 goodware, 827,853 potentially unwanted applications and 4,174,203 malware samples provided by ESET.1 This paper aims to show a new direction in visualizing malware using fractal geometry and possibilities in analysis and classification.

Název v anglickém jazyce

From malware samples to fractal images: A new paradigm for classification

Popis výsledku anglicky

To date, a large number of research papers have been written on malware classification, identification, classification into different families, and the distinction between malware and goodware. These works have been based on captured malware samples and have attempted to analyse malware and goodware using various techniques like the analysis of malware using malware visualization. These works usually convert malware samples capturing the malware structure into image structures which are then subject to image processing. In this paper, we propose an unconventional and novel approach to malware visualization based on its dynamical analysis, subsequent complex network conversion and fractal geometry, e.g. Julia sets visualization. Very interesting images being subsequently used to classify as malware and goodware. The classification is done by deep learning network. The results of the presented experiments of fractal conversion and subsequent classification are based on a database of 6,589,997 goodware, 827,853 potentially unwanted applications and 4,174,203 malware samples provided by ESET.1 This paper aims to show a new direction in visualizing malware using fractal geometry and possibilities in analysis and classification.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Mathematics and computers in simulation

ISSN

0378-4754

e-ISSN

1872-7166

Svazek periodika

218

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

30

Strana od-do

174-203

Kód UT WoS článku

001126383400001

EID výsledku v databázi Scopus

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