TY - GEN
T1 - MobiDroid
T2 - 24th International Conference on Engineering of Complex Computer Systems, ICECCS 2019
AU - Feng, Ruitao
AU - Chen, Sen
AU - Xie, Xiaofei
AU - Ma, Lei
AU - Meng, Guozhu
AU - Liu, Yang
AU - Lin, Shang Wei
N1 - Funding Information:
This research was supported (in part) by the National Research Foundation, Prime Ministers Office, Singapore under its National Cybersecurity R&D Program (Award No. NRF2018NCR-NCR005-0001), National Satellite of Excellence in Trustworthy Software System (Award No. NRF2018NCR-NSOE003-0001) administered by the National Cybersecurity R and D Directorate, and JSPS KAKENHI Grant 19H04086, and Qdai-jump Research Program NO.01277.
Funding Information:
ACKNOWLEDGMENTS This research was supported (in part) by the National Research Foundation, Prime Ministers Office, Singapore under its National Cybersecurity R&D Program (Award No. NRF2018NCR-NCR005-0001), National Satellite of Excellence in Trustworthy Software System (Award No. NRF2018NCR-NSOE003-0001) administered by the National Cybersecurity R&D Directorate, and JSPS KAKENHI Grant 19H04086, and Qdai-jump Research Program NO.01277.
PY - 2019/11
Y1 - 2019/11
N2 - Currently, Android malware detection is mostly performed on the server side against the increasing number of Android malware. Powerful computing resource gives more exhaustive protection for Android markets than maintaining detection by a single user in many cases. However, apart from the Android apps provided by the official market (i.e., Google Play Store), apps from unofficial markets and third-party resources are always causing a serious security threat to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection because the network transmission has a lot of overhead. In addition, the uploading process also suffers from the threat of attackers. Consequently, a last line of defense on Android devices is necessary and much-needed. To address these problems, in this paper, we propose an effective Android malware detection system, MobiDroid, leveraging deep learning to provide a real-time secure and fast response environment on Android devices. Although a deep learning-based approach can be maintained on server side efficiently for detecting Android malware, deep learning models cannot be directly deployed and executed on Android devices due to various performance limitations such as computation power, memory size, and energy. Therefore, we evaluate and investigate the different performances with various feature categories, and further provide an effective solution to detect malware on Android devices. The proposed detection system on Android devices in this paper can serve as a starting point for further study of this important area.
AB - Currently, Android malware detection is mostly performed on the server side against the increasing number of Android malware. Powerful computing resource gives more exhaustive protection for Android markets than maintaining detection by a single user in many cases. However, apart from the Android apps provided by the official market (i.e., Google Play Store), apps from unofficial markets and third-party resources are always causing a serious security threat to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection because the network transmission has a lot of overhead. In addition, the uploading process also suffers from the threat of attackers. Consequently, a last line of defense on Android devices is necessary and much-needed. To address these problems, in this paper, we propose an effective Android malware detection system, MobiDroid, leveraging deep learning to provide a real-time secure and fast response environment on Android devices. Although a deep learning-based approach can be maintained on server side efficiently for detecting Android malware, deep learning models cannot be directly deployed and executed on Android devices due to various performance limitations such as computation power, memory size, and energy. Therefore, we evaluate and investigate the different performances with various feature categories, and further provide an effective solution to detect malware on Android devices. The proposed detection system on Android devices in this paper can serve as a starting point for further study of this important area.
UR - http://www.scopus.com/inward/record.url?scp=85074651813&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074651813&partnerID=8YFLogxK
U2 - 10.1109/ICECCS.2019.00014
DO - 10.1109/ICECCS.2019.00014
M3 - Conference contribution
AN - SCOPUS:85074651813
T3 - Proceedings of the IEEE International Conference on Engineering of Complex Computer Systems, ICECCS
SP - 61
EP - 70
BT - Proceedings - 2019 24th International Conference on Engineering of Complex Computer Systems, ICECCS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 November 2019 through 13 November 2019
ER -