Biblio

A recent report indicates that a newly developed mali- cious app for Android is introduced every 11 seconds.  To combat this alarming rate of malware creation,  we need a scalable malware detection approach that is effective and efficient. In this paper, we introduce SIGPID, a malware detection system based on permission  analysis to cope with the rapid increase in the number of Android malware. In- stead of analyzing all 135 Android permissions, our ap- proach applies 3-level pruning by mining the permission data to identify only significant permissions that can be ef- fective in distinguishing benign and malicious apps. SIG- PID then utilizes classification algorithms to classify differ- ent families of malware and benign apps. Our evaluation finds that only 22 out of 135 permissions are significant. We then compare the performance of our approach, using only

22 permissions, against a baseline approach that analyzes all permissions. The results indicate that when Support Vec- tor Machine (SVM) is used as the classifier, we can achieve over 90% of precision, recall, accuracy, and F-measure, which  are about the same as those produced by the base- line approach while incurring the analysis times that are 4 to 32 times smaller that those of using all 135 permissions. When we compare the detection effectiveness of SIGPID to those of other approaches, SIGPID can detect 93.62% of malware in the data set, and 91.4% unknown malware.

A recent report has shown that there are more than 5,000 malicious applications created for Android devices each day. This creates a need for researchers to develop effective and efficient malware classification and detection approaches. To address this need, we introduce DroidClassifier: a systematic framework for classifying network traffic generated by mobile malware. Our approach utilizes network traffic analysis to construct multiple models in an automated fashion using a supervised method over a set of labeled malware network traffic (the training dataset). Each model is built by extracting common identifiers from multiple HTTP header fields. Adaptive thresholds are designed to capture the disparate characteristics of different malware families. Clustering is then used to improve the classification efficiency. Finally, we aggregate the multiple models to construct a holistic model to conduct cluster-level malware classification. We then perform a comprehensive evaluation of DroidClassifier by using 706 malware samples as the training set and 657 malware samples and 5,215 benign apps as the testing set. Collectively , these malicious and benign apps generate 17,949 network flows. The results show that DroidClassifier successfully identifies over 90% of different families of malware with more than 90% accuracy with accessible computational cost. Thus, DroidClassifier can facilitate network management in a large network, and enable unobtrusive detection of mobile malware. By focusing on analyzing network behaviors, we expect DroidClassifier to work with reasonable accuracy for other mobile platforms such as iOS and Windows Mobile as well.