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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

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

4

Strana od-do

220-223

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Budapest, Hungary

Datum konání akce

1. 7. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000493442800048