@article{ART002899738},
author={Dong-Ha Jeon and Soojin Lee},
title={Light-weight Classification Model for Android Malware through the Dimensional Reduction of API Call Sequence using PCA},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2022},
volume={27},
number={11},
pages={123-130},
doi={10.9708/jksci.2022.27.11.123}

TY - JOUR
AU - Dong-Ha Jeon
AU - Soojin Lee
TI - Light-weight Classification Model for Android Malware through the Dimensional Reduction of API Call Sequence using PCA
JO - Journal of The Korea Society of Computer and Information
PY - 2022
VL - 27
IS - 11
PB - The Korean Society Of Computer And Information
SP - 123
EP - 130
SN - 1598-849X
AB - 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.
KW - API-Call;PCA;Dimensional Reduction;LGBM;RF;KNN
DO - 10.9708/jksci.2022.27.11.123
ER -

Dong-Ha Jeon and Soojin Lee. (2022). 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, 27(11), 123-130.

Dong-Ha Jeon and Soojin Lee. 2022, "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, vol.27, no.11 pp.123-130. Available from: doi:10.9708/jksci.2022.27.11.123

Dong-Ha Jeon, Soojin Lee "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 27.11 pp.123-130 (2022) : 123.

Dong-Ha Jeon, Soojin Lee. Light-weight Classification Model for Android Malware through the Dimensional Reduction of API Call Sequence using PCA. 2022; 27(11), 123-130. Available from: doi:10.9708/jksci.2022.27.11.123

Dong-Ha Jeon and Soojin Lee. "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 27, no.11 (2022) : 123-130.doi: 10.9708/jksci.2022.27.11.123

Dong-Ha Jeon; Soojin Lee. 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, 27(11), 123-130. doi: 10.9708/jksci.2022.27.11.123

Dong-Ha Jeon; Soojin Lee. 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. 2022; 27(11) 123-130. doi: 10.9708/jksci.2022.27.11.123

Dong-Ha Jeon, Soojin Lee. Light-weight Classification Model for Android Malware through the Dimensional Reduction of API Call Sequence using PCA. 2022; 27(11), 123-130. Available from: doi:10.9708/jksci.2022.27.11.123

Dong-Ha Jeon and Soojin Lee. "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 27, no.11 (2022) : 123-130.doi: 10.9708/jksci.2022.27.11.123