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Light-weight Classification Model for Android Malware through the Dimensional Reduction of API Call Sequence using PCA

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(11), pp.123-130
  • DOI : 10.9708/jksci.2022.27.11.123
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 12, 2022
  • Accepted : November 11, 2022
  • Published : November 30, 2022

Dong-Ha Jeon 1 Soojin Lee 1

1국방대학교

Accredited

ABSTRACT

Recently, studies on the detection and classification of Android malware based on API Call sequence have been actively carried out. However, API Call sequence based malware classification has serious limitations such as excessive time and resource consumption in terms of malware analysis and learning model construction due to the vast amount of data and high-dimensional characteristic of features. In this study, we analyzed various classification models such as LightGBM, Random Forest, and k-Nearest Neighbors after significantly reducing the dimension of features using PCA(Principal Component Analysis) for CICAndMal2020 dataset containing vast API Call information. The experimental result shows that PCA significantly reduces the dimension of features while maintaining the characteristics of the original data and achieves efficient malware classification performance. Both binary classification and multi-class classification achieve higher levels of accuracy than previous studies, even if the data characteristics were reduced to less than 1% of the total size.

Citation status

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