@article{ART002601989},
author={Hyung-Woo Lee and HanSeong Lee},
title={Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2020},
volume={6},
number={2},
pages={1-10}
TY - JOUR
AU - Hyung-Woo Lee
AU - HanSeong Lee
TI - Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps
JO - Journal of Internet of Things and Convergence
PY - 2020
VL - 6
IS - 2
PB - The Korea Internet of Things Society
SP - 1
EP - 10
SN - 2466-0078
AB - The mobile application based on the Android platform is simple to decompile, making it possible to create malicious applications similar to normal ones, and can easily distribute the created malicious apps through the Android third party app store. In this case, the Android malicious application in the smartphone causes several problems such as leakage of personal information in the device, transmission of premium SMS, and leakage of location information and call records. Therefore, it is necessary to select a optimal model that provides the best performance among the machine learning techniques that have published recently, and provide a technique to automatically identify malicious Android apps. Therefore, in this paper, after adopting the feature engineering to Android apps on official test set, a total of four performance evaluation experiments were conducted to select the machine learning model that provides the optimal performance for Android malicious app detection.
KW - Smart phones;Android;Malware;Feature Engineering;Machine Learning;Random Forest
DO -
UR -
ER -
Hyung-Woo Lee and HanSeong Lee. (2020). Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps. Journal of Internet of Things and Convergence, 6(2), 1-10.
Hyung-Woo Lee and HanSeong Lee. 2020, "Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps", Journal of Internet of Things and Convergence, vol.6, no.2 pp.1-10.
Hyung-Woo Lee, HanSeong Lee "Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps" Journal of Internet of Things and Convergence 6.2 pp.1-10 (2020) : 1.
Hyung-Woo Lee, HanSeong Lee. Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps. 2020; 6(2), 1-10.
Hyung-Woo Lee and HanSeong Lee. "Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps" Journal of Internet of Things and Convergence 6, no.2 (2020) : 1-10.
Hyung-Woo Lee; HanSeong Lee. Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps. Journal of Internet of Things and Convergence, 6(2), 1-10.
Hyung-Woo Lee; HanSeong Lee. Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps. Journal of Internet of Things and Convergence. 2020; 6(2) 1-10.
Hyung-Woo Lee, HanSeong Lee. Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps. 2020; 6(2), 1-10.
Hyung-Woo Lee and HanSeong Lee. "Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps" Journal of Internet of Things and Convergence 6, no.2 (2020) : 1-10.
