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Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm

Author

Listed:
  • Jaehyeong Lee

    (Department of Computer Engineering, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Korea)

  • Hyuk Jang

    (Department of Computer Engineering, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Korea)

  • Sungmin Ha

    (Department of Business Administration, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Korea)

  • Yourim Yoon

    (Department of Computer Engineering, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Korea)

Abstract

Since the discovery that machine learning can be used to effectively detect Android malware, many studies on machine learning-based malware detection techniques have been conducted. Several methods based on feature selection, particularly genetic algorithms, have been proposed to increase the performance and reduce costs. However, because they have yet to be compared with other methods and their many features have not been sufficiently verified, such methods have certain limitations. This study investigates whether genetic algorithm-based feature selection helps Android malware detection. We applied nine machine learning algorithms with genetic algorithm-based feature selection for 1104 static features through 5000 benign applications and 2500 malwares included in the Andro-AutoPsy dataset. Comparative experimental results show that the genetic algorithm performed better than the information gain-based method, which is generally used as a feature selection method. Moreover, machine learning using the proposed genetic algorithm-based feature selection has an absolute advantage in terms of time compared to machine learning without feature selection. The results indicate that incorporating genetic algorithms into Android malware detection is a valuable approach. Furthermore, to improve malware detection performance, it is useful to apply genetic algorithm-based feature selection to machine learning.

Suggested Citation

  • Jaehyeong Lee & Hyuk Jang & Sungmin Ha & Yourim Yoon, 2021. "Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm," Mathematics, MDPI, vol. 9(21), pages 1-20, November.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2813-:d:673305
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    Citations

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    Cited by:

    1. Yong-Hyuk Kim & Fabio Caraffini, 2023. "Preface to “Swarm and Evolutionary Computation—Bridging Theory and Practice”," Mathematics, MDPI, vol. 11(5), pages 1-3, March.
    2. Liya Yue & Pei Hu & Shu-Chuan Chu & Jeng-Shyang Pan, 2023. "Multi-Objective Gray Wolf Optimizer with Cost-Sensitive Feature Selection for Predicting Students’ Academic Performance in College English," Mathematics, MDPI, vol. 11(15), pages 1-16, August.

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