EC2: Ensemble Clustering and Classification for Predicting Android Malware Families

As the most widely used mobile platform, Android is also the biggest target for mobile malware. Given the increasing number of Android malware variants, detecting
malware families
is crucial so that security analysts can identify situations where signatures of a known malware family can be adapted as opposed to manually inspecting behavior of all samples. We present
EC2
(
E
nsemble
C
lustering and
C
lassification), a novel algorithm for discovering Android malware families of
varying sizes
—ranging from very large to very small families (even if previously unseen). We present a performance comparison of several traditional classification and clustering algorithms for Android malware family identification on DREBIN, the largest public Android malware dataset with labeled families. We use the output of both supervised classifiers and unsupervised clustering to design
EC2
. Experimental results on both the DREBIN and the more recent Koodous malware datasets show that
EC2
accurately detects both small and large families, outperforming several comparative baselines. Furthermore, we show how to automatically characterize and explain unique behaviors of specific malware families, such as
FakeInstaller
,
MobileTx
,
Geinimi
. In short,
EC2
presents an early warning system for emerging new malware families, as well as a robust predictor of the family (when it is not new) to which a new malware sample belongs, and the design of novel strategies for data-driven understanding of malware behaviors.