TY - GEN
T1 - Mass discovery of android malware behavioral characteristics for detection consideration
AU - Su, Xin
AU - Shi, Weiqi
AU - Lin, Jiuchuan
AU - Wang, Xin
N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Android malware have surged and been sophisticated, posing a great threat to users. The key challenge of detect Android malware is how to discovery their behavioral characteristics at a large scale, and use them to detect Android malware. In this work, we are motivated to discover the discriminatory features extracted from Android APK files for Android malware detection. To achieve this goal, firstly we extract a very large number of static features from each Android application (or app). Secondly, we explain the importance of each kind of feature in Android malware detection. Thirdly, we fed these features into three different classifiers (e.g., SVM, DT, RandomFoerst) for the detection of Android malware. We conduct extensive experiments on large real-world app sets consisting of 6,820 Android malware and 37,581 Android benign apps. The experimental results and our analysis give insights regarding what discriminatory features are most effective to characterize Android malware for building an effective and efficient Android malware detection approach.
AB - Android malware have surged and been sophisticated, posing a great threat to users. The key challenge of detect Android malware is how to discovery their behavioral characteristics at a large scale, and use them to detect Android malware. In this work, we are motivated to discover the discriminatory features extracted from Android APK files for Android malware detection. To achieve this goal, firstly we extract a very large number of static features from each Android application (or app). Secondly, we explain the importance of each kind of feature in Android malware detection. Thirdly, we fed these features into three different classifiers (e.g., SVM, DT, RandomFoerst) for the detection of Android malware. We conduct extensive experiments on large real-world app sets consisting of 6,820 Android malware and 37,581 Android benign apps. The experimental results and our analysis give insights regarding what discriminatory features are most effective to characterize Android malware for building an effective and efficient Android malware detection approach.
UR - https://www.scopus.com/pages/publications/85053923853
U2 - 10.1007/978-3-030-00012-7_10
DO - 10.1007/978-3-030-00012-7_10
M3 - Conference contribution
SN - 9783030000110
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 101
EP - 112
BT - Cloud Computing and Security - 4th International Conference, ICCCS 2018, Revised Selected Papers
A2 - Sun, Xingming
A2 - Pan, Zhaoqing
A2 - Bertino, Elisa
PB - Springer Verlag
T2 - 4th International Conference on Cloud Computing and Security, ICCCS 2018
Y2 - 8 June 2018 through 10 June 2018
ER -