CNN-based Android Malware Detection Using Reduced Feature Set (축소된 특성집합을 이용한 CNN 기반의 안드로이드 악성코드 탐지)

Dong-Min Kim (김동민); Soojin Lee (이수진)

doi:10.9708/jksci.2021.26.10.019

CNN-based Android Malware Detection Using Reduced Feature Set

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(10), pp.19~26
DOI : 10.9708/jksci.2021.26.10.019
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : August 24, 2021
Accepted : October 6, 2021
Published : October 29, 2021

Dong-Min Kim ¹, Soojin Lee ¹

¹국방대학교

Accredited

ABSTRACT

The performance of deep learning-based malware detection and classification models depends largely on how to construct a feature set to be applied to training. In this paper, we propose an approach to select the optimal feature set to maximize detection performance for CNN-based Android malware detection. The features to be included in the feature set were selected through the Chi-Square test algorithm, which is widely used for feature selection in machine learning and deep learning. To validate the proposed approach, the CNN model was trained using 36 characteristics selected for the CICANDMAL2017 dataset and then the malware detection performance was measured. As a result, 99.99% of Accuracy was achieved in binary classification and 98.55% in multiclass classification.

KEYWORDS

CNN, Android, Malware, Feature selection. Binary classification, Multiclass classification

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* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

CNN-based Android Malware Detection Using Reduced Feature Set

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

CNN-based Android Malware Detection Using Reduced Feature Set

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.