Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Machine Learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU133990" target="_blank" >RIV/00216305:26220/19:PU133990 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8769039" target="_blank" >https://ieeexplore.ieee.org/document/8769039</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP.2019.8769039" target="_blank" >10.1109/TSP.2019.8769039</a>

Result language
angličtina
Original language name
Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Machine Learning
Original language description
Android platform due to open source characteristic and Google backing has the largest global market share. Being the world's most popular operating system, it has drawn the attention of cyber criminals operating particularly through wide distribution of malicious applications. This paper proposes an effectual machine-learning based approach for Android Malware Detection making use of evolutionary Genetic algorithm for discriminatory feature selection. Selected features from Genetic algorithm are used to train machine learning classifiers and their capability in identification of Malware before and after feature selection is compared. The experimentation results validate that Genetic algorithm gives most optimized feature subset helping in reduction of feature dimension to less than half of the original feature-set. Classification accuracy of more than 94% is maintained post feature selection for the machine learning based classifiers, while working on much reduced feature dimension, thereby, having a positive impact on computational complexity of learning classifiers.
Czech name
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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
4
Pages from-to
220-223
Publisher name
Neuveden
Place of publication
Neuveden
Event location
Budapest, Hungary
Event date
Jul 1, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000493442800048

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