Publication detail

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

FATIMA, A. MAUYA, R. DUTTA, M. BURGET, R. MAŠEK, J.

Original Title

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

English Title

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

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

Feature extraction;Malware;Genetic algorithms;Machine learning;Support vector machines;Smart phones;Machine learning algorithms

Released

01.07.2019

ISBN

978-1-7281-1864-2

Book

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

Pages from

220

Pages to

223

Pages count

4

URL

BibTex


@inproceedings{BUT159715,
  author="Anam {Fatima} and Ritesh {Mauya} and Malay Kishore {Dutta} and Radim {Burget} and Jan {Mašek}",
  title="Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Machine Learning",
  annote="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.",
  booktitle="2019 42nd International Conference on Telecommunications and Signal Processing (TSP)",
  chapter="159715",
  doi="10.1109/TSP.2019.8769039",
  howpublished="electronic, physical medium",
  year="2019",
  month="july",
  pages="220--223",
  type="conference paper"
}