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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Feature Optimization for Run Time Analysis of Malware in Windows Operating System using Machine Learning Approach
Original language description
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.
Czech name
—
Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2019 42nd International Conference on Telecommunications and Signal Processing (TSP)
ISBN
978-1-7281-1864-2
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
255-260
Publisher name
IEEE
Place of publication
Budapest, Hungary
Event location
Budapest, Hungary
Event date
Jul 1, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000493442800056

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