TY - GEN
T1 - Familial Classification of Android Malware using Hybrid Analysis
AU - Faruk Turan Cavli, Omer
AU - Sen, Sevil
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/3
Y1 - 2020/12/3
N2 - With the developments in mobile and wireless technology, mobile devices have become important part of our lives. While Android is the leading operating system in the market share, it is also the most targeted platform by attackers. While there have been many solutions proposed for detection of Android malware in the literature, the family classification of detected malicious applications becomes important, especially where the number of mobile malware variants increases every day in the market. In this study, a solution based on machine learning and hybrid analysis is proposed for the Android malware familial classification problem. An extensive feature set including network-related features and activity bigrams is proposed. The effective static and dynamic analysis features are studied thoroughly and evaluated on Malgenome [1], Drebin [2], and UpDroid [3] datasets.
AB - With the developments in mobile and wireless technology, mobile devices have become important part of our lives. While Android is the leading operating system in the market share, it is also the most targeted platform by attackers. While there have been many solutions proposed for detection of Android malware in the literature, the family classification of detected malicious applications becomes important, especially where the number of mobile malware variants increases every day in the market. In this study, a solution based on machine learning and hybrid analysis is proposed for the Android malware familial classification problem. An extensive feature set including network-related features and activity bigrams is proposed. The effective static and dynamic analysis features are studied thoroughly and evaluated on Malgenome [1], Drebin [2], and UpDroid [3] datasets.
KW - Android
KW - hybrid analysis
KW - machine learning
KW - malware analysis and detection
KW - malware family classification
KW - mobile security
KW - static/dynamic analysis
UR - https://www.scopus.com/pages/publications/85101176249
U2 - 10.1109/ISCTURKEY51113.2020.9308003
DO - 10.1109/ISCTURKEY51113.2020.9308003
M3 - Conference contribution
AN - SCOPUS:85101176249
T3 - 2020 International Conference on Information Security and Cryptology, ISCTURKEY 2020 - Proceedings
SP - 62
EP - 67
BT - 2020 International Conference on Information Security and Cryptology, ISCTURKEY 2020 - Proceedings
A2 - Sagiroglu, Seref
A2 - Akleylek, Sedat
A2 - Ozbudak, Ferruh
A2 - Canbay, Yavuz
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Information Security and Cryptology, ISCTURKEY 2020
Y2 - 3 December 2020 through 4 December 2020
ER -