@article{ART003119133},
author={Seongwon Jeong and Seokhyun Ahn and SEONG JE CHO and Dongjae Kim and Youngsup Hwang},
title={Enhancing Sustainability of an Android Malware Detection Technique using K-means Clustering},
journal={Journal of Software Assessment and Valuation},
issn={2092-8114},
year={2024},
volume={20},
number={3},
pages={21-32},
doi={10.29056/jsav.2024.09.03}
TY - JOUR
AU - Seongwon Jeong
AU - Seokhyun Ahn
AU - SEONG JE CHO
AU - Dongjae Kim
AU - Youngsup Hwang
TI - Enhancing Sustainability of an Android Malware Detection Technique using K-means Clustering
JO - Journal of Software Assessment and Valuation
PY - 2024
VL - 20
IS - 3
PB - Korea Software Assessment and Valuation Society
SP - 21
EP - 32
SN - 2092-8114
AB - Traditional machine learning-based Android malicious app(malware) detection techniques have limitations in detecting new types of malware due to concept drift. In other words, traditional machine learning-based malware detection techniques may not be sustainable. Concept drift refers to the evolving nature of malware features over time and the resulting degradation in the performance of machine learning-based detection models In this paper, we propose a technique to improve the sustainability of the method for detecting Android malware using API call information and machine learning. In the proposed technique, apps are first grouped using K-means clustering, and then classification models are applied to detect malicious apps for each group.
In the K-means clustering, the elbow method is used to find the optimal k value, and thresholding and hyperparameter optimization processes are applied to the classifiers for each cluster. The classifiers include random forest, K-nearest neighbor, and AdaBoost. The experimental results show that the random forest classifier showed the highest performance, with the F1 score and AUT value calculated by the micro-means method being improved by 20.1%p and 20.4%p, respectively, compared to the traditional random forest model.
KW - machine learning;K-means clustering;malicious app detection;sustainability;Android app;concept drift
DO - 10.29056/jsav.2024.09.03
ER -
Seongwon Jeong, Seokhyun Ahn, SEONG JE CHO, Dongjae Kim and Youngsup Hwang. (2024). Enhancing Sustainability of an Android Malware Detection Technique using K-means Clustering. Journal of Software Assessment and Valuation, 20(3), 21-32.
Seongwon Jeong, Seokhyun Ahn, SEONG JE CHO, Dongjae Kim and Youngsup Hwang. 2024, "Enhancing Sustainability of an Android Malware Detection Technique using K-means Clustering", Journal of Software Assessment and Valuation, vol.20, no.3 pp.21-32. Available from: doi:10.29056/jsav.2024.09.03
Seongwon Jeong, Seokhyun Ahn, SEONG JE CHO, Dongjae Kim, Youngsup Hwang "Enhancing Sustainability of an Android Malware Detection Technique using K-means Clustering" Journal of Software Assessment and Valuation 20.3 pp.21-32 (2024) : 21.
Seongwon Jeong, Seokhyun Ahn, SEONG JE CHO, Dongjae Kim, Youngsup Hwang. Enhancing Sustainability of an Android Malware Detection Technique using K-means Clustering. 2024; 20(3), 21-32. Available from: doi:10.29056/jsav.2024.09.03
Seongwon Jeong, Seokhyun Ahn, SEONG JE CHO, Dongjae Kim and Youngsup Hwang. "Enhancing Sustainability of an Android Malware Detection Technique using K-means Clustering" Journal of Software Assessment and Valuation 20, no.3 (2024) : 21-32.doi: 10.29056/jsav.2024.09.03
Seongwon Jeong; Seokhyun Ahn; SEONG JE CHO; Dongjae Kim; Youngsup Hwang. Enhancing Sustainability of an Android Malware Detection Technique using K-means Clustering. Journal of Software Assessment and Valuation, 20(3), 21-32. doi: 10.29056/jsav.2024.09.03
Seongwon Jeong; Seokhyun Ahn; SEONG JE CHO; Dongjae Kim; Youngsup Hwang. Enhancing Sustainability of an Android Malware Detection Technique using K-means Clustering. Journal of Software Assessment and Valuation. 2024; 20(3) 21-32. doi: 10.29056/jsav.2024.09.03
Seongwon Jeong, Seokhyun Ahn, SEONG JE CHO, Dongjae Kim, Youngsup Hwang. Enhancing Sustainability of an Android Malware Detection Technique using K-means Clustering. 2024; 20(3), 21-32. Available from: doi:10.29056/jsav.2024.09.03
Seongwon Jeong, Seokhyun Ahn, SEONG JE CHO, Dongjae Kim and Youngsup Hwang. "Enhancing Sustainability of an Android Malware Detection Technique using K-means Clustering" Journal of Software Assessment and Valuation 20, no.3 (2024) : 21-32.doi: 10.29056/jsav.2024.09.03
