Vetting undesirable behaviors in android apps with permission use analysis

Android platform adopts permissions to protect sensitive resources from untrusted apps. However, after permissions are granted by users at install time, apps could use these permissions (sensitive resources) with no further restrictions. Thus, recent years have witnessed the explosion of undesirable behaviors in Android apps. An important part in the defense is the accurate analysis of Android apps. However, traditional syscall-based analysis techniques are not well-suited for Android, because they could not capture critical interactions between the application and the Android system. This paper presents VetDroid, a dynamic analysis platform for reconstructing sensitive behaviors in Android apps from a novel permission use perspective. VetDroid features a systematic frame-work to effectively construct permission use behaviors, i.e., how applications use permissions to access (sensitive) system resources, and how these acquired permission-sensitive resources are further utilized by the application. With permission use behaviors, security analysts can easily examine the internal sensitive behaviors of an app. Using real-world Android malware, we show that VetDroid can clearly reconstruct fine-grained malicious behaviors to ease malware analysis. We further apply VetDroid to 1,249 top free apps in Google Play. VetDroid can assist in finding more information leaks than TaintDroid [24], a state-of-the-art technique. In addition, we show howwe can use VetDroid to analyze fine-grained causes of information leaks that TaintDroid cannot reveal. Finally, we show that VetDroid can help identify subtle vulnerabilities in some (top free) applications otherwise hard to detect

Significant Permission Identification for Android Malware Detection

A recent report indicates that a newly developed malicious 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 thesis, we introduce SigPID, a malware detection system based on permission analysis to cope with the rapid increase in the number of Android malware. Instead of analyzing all 135 Android permissions, our approach applies 3-level pruning by mining the permission data to identify only significant permissions that can be effective in distinguishing benign and malicious apps. Based on the identified significant permissions, SigPID utilizes classification algorithms to classify different families of malware and benign apps. Our evaluation finds that only 25% of permissions (34 out of 135 permissions) are significant. We then compare the performance of our approach, using only 25% of all permissions, against a baseline approach that analyzes all permissions. The results indicate that when Support Vector 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 baseline approach. We also show that SigPID is effective when used with 67 other commonly used supervised learning approaches. We find that 55 out of 67 algorithms can achieve F-measure of at least 85%, while the average running time can be reduced by 85.6\% compared with the baseline approach. When we compare the detection effectiveness of SigPID to those of other approaches, SigPID can detect 96.54% of malware in the data set while other approaches detect 3.99% to 96.41%. Advisers: Witawas Srisa-an, Qiben Ya

Significant Permission Identification for Android Malware Detection

A recent report indicates that a newly developed malicious 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 thesis, we introduce SigPID, a malware detection system based on permission analysis to cope with the rapid increase in the number of Android malware. Instead of analyzing all 135 Android permissions, our approach applies 3-level pruning by mining the permission data to identify only significant permissions that can be effective in distinguishing benign and malicious apps. Based on the identified significant permissions, SigPID utilizes classification algorithms to classify different families of malware and benign apps. Our evaluation finds that only 25% of permissions (34 out of 135 permissions) are significant. We then compare the performance of our approach, using only 25% of all permissions, against a baseline approach that analyzes all permissions. The results indicate that when Support Vector 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 baseline approach. We also show that SigPID is effective when used with 67 other commonly used supervised learning approaches. We find that 55 out of 67 algorithms can achieve F-measure of at least 85%, while the average running time can be reduced by 85.6\% compared with the baseline approach. When we compare the detection effectiveness of SigPID to those of other approaches, SigPID can detect 96.54% of malware in the data set while other approaches detect 3.99% to 96.41%. Advisers: Witawas Srisa-an, Qiben Ya

ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic

It is well known that apps running on mobile devices extensively track and leak users' personally identifiable information (PII); however, these users have little visibility into PII leaked through the network traffic generated by their devices, and have poor control over how, when and where that traffic is sent and handled by third parties. In this paper, we present the design, implementation, and evaluation of ReCon: a cross-platform system that reveals PII leaks and gives users control over them without requiring any special privileges or custom OSes. ReCon leverages machine learning to reveal potential PII leaks by inspecting network traffic, and provides a visualization tool to empower users with the ability to control these leaks via blocking or substitution of PII. We evaluate ReCon's effectiveness with measurements from controlled experiments using leaks from the 100 most popular iOS, Android, and Windows Phone apps, and via an IRB-approved user study with 92 participants. We show that ReCon is accurate, efficient, and identifies a wider range of PII than previous approaches.Comment: Please use MobiSys version when referencing this work: http://dl.acm.org/citation.cfm?id=2906392. 18 pages, recon.meddle.mob

SaaS: A situational awareness and analysis system for massive android malware detection

A large amount of mobile applications (Apps) are uploaded, distributed and updated in various Android markets, e.g., Google Play and Huawei AppGallery every day. One of the ongoing challenges is to detect malicious Apps (also known as malware) among those massive newcomers accurately and efficiently in the daily security management of Android App markets. Customers rely on those detection results in the selection of Apps upon downloading, and undetected malware may result in great damages. In this paper, we propose a cloud-based malware detection system called SaaS by leveraging and marrying multiple approaches from diverse domains such as natural language processing (n-gram), image processing (GLCM), cryptography (fuzzy hash), machine learning (random forest) and complex networks. We firstly extract n-gram features and GLCM features from an App's smali code and DEX file, respectively. We next feed those features into training data set, to create a machine learning detect model. The model is further enhanced by fuzzy hash to detect whether inspected App is repackaged or not. Extensive experiments (involving 1495 samples) demonstrates that the detecting accuracy is more than 98.5%, and support a large-scale detecting and monitoring. Besides, our proposed system can be deployed as a service in clouds and customers can access cloud services on demand