Feature Optimization for Run Time Analysis of Malware in Windows Operating System using Machine Learning Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU134086" target="_blank" >RIV/00216305:26220/19:PU134086 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8768808" target="_blank" >https://ieeexplore.ieee.org/document/8768808</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2019.8768808" target="_blank" >10.1109/TSP.2019.8768808</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Feature Optimization for Run Time Analysis of Malware in Windows Operating System using Machine Learning Approach

Popis výsledku v původním jazyce

With the development of the web's high usage, the number of malware affecting the system are incresing. Various techniques have been used but they are incapable to identify unknown malware. To counter such threats, the proposed work makes utilization of dynamic malware investigation systems based on machine learning technique for windows based malware recognization. In this paper two methods to analyses the behaviour of the malware and feature selection of windows executables file. Cuckoo is a malicious code analysis apparatus which analyzes the malware more detail and gives the far-reaching results dependent on the arrangement of tests made by it and second, the feature selection for windows dynamic malware anaysis has been done by using Genetic Algorithm. Three classifiers have been used to compare the detection result of Windows-based malware: Support Vector Machine with detection accuracy of 81.3%, Naive Bayes classifier with accuracy of 64.7% and Random Forest classifier achieving 86.8% accurate results.

Název v anglickém jazyce

Feature Optimization for Run Time Analysis of Malware in Windows Operating System using Machine Learning Approach

Popis výsledku anglicky

With the development of the web's high usage, the number of malware affecting the system are incresing. Various techniques have been used but they are incapable to identify unknown malware. To counter such threats, the proposed work makes utilization of dynamic malware investigation systems based on machine learning technique for windows based malware recognization. In this paper two methods to analyses the behaviour of the malware and feature selection of windows executables file. Cuckoo is a malicious code analysis apparatus which analyzes the malware more detail and gives the far-reaching results dependent on the arrangement of tests made by it and second, the feature selection for windows dynamic malware anaysis has been done by using Genetic Algorithm. Three classifiers have been used to compare the detection result of Windows-based malware: Support Vector Machine with detection accuracy of 81.3%, Naive Bayes classifier with accuracy of 64.7% and Random Forest classifier achieving 86.8% accurate results.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 42nd International Conference on Telecommunications and Signal Processing (TSP)

ISBN

978-1-7281-1864-2

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

255-260

Název nakladatele

IEEE

Místo vydání

Budapest, Hungary

Místo konání akce

Budapest, Hungary

Datum konání akce

1. 7. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000493442800056