04:00 - 04:30

• IccTA: Detecting Inter-Component Privacy Leaks in Android Apps

  Shake Them All is a popular "Wallpaper" application exceeding millions of downloads on Google Play. At installation, this application is given permission to (1) access the Internet (for updating wallpapers) and (2) use the device microphone (to change background following noise changes). With these permissions, the application could silently record user conversations and upload them remotely. To give more confidence about how Shake Them All actually processes what it records, it is necessary to build a precise analysis tool that tracks the flow of any sensitive data from its source point to any sink, especially if those are in different components. Since Android applications may leak private data carelessly or maliciously, we propose IccTA, a static taint analyzer to detect privacy leaks among components in Android applications. IccTA goes beyond state-of-the-art approaches by supporting inter-component detection. By propagating context information among components, IccTA improves the precision of the analysis. IccTA outperforms existing tools on two benchmarks for ICC-leak detectors: Droid Bench and ICC-Bench. Moreover, our approach detects 534 ICC leaks in 108 apps from MalGenome and 2,395 ICC leaks in 337 apps in a set of 15,000 Google Play apps.

   

  Preprint available:

Li Li; Alexandre Bartel; Tegawendé Bissyande; Jacques Klein; Yves Le Traon; Steven Arzt; Siegfried Rasthofer; Eric Bodden; Damien Octeau; Patrick McDaniel

University of Luxembourg, Luxembourg; Technische Universität Darmstadt, Germany; Pennsylvania State University, USA

04:30 - 05:00

• Do Security Patterns Really Help Designers?

  Security patterns are well-known solutions to security-specific problems. They are often claimed to benefit designers without much security expertise. We have performed an empirical study to investigate whether the usage of security patterns by such an audience leads to a more secure design, or to an increased productivity of the designers. Our study involved 32 teams of master students enrolled in a course on software architecture, working on the design of a realistically-sized banking system. Irrespective of whether the teams were using security patterns, we have not been able to detect a difference between the two treatment groups. However, the teams prefer to work with the support of security patterns.

   

  Preprint available:

Koen Yskout; Riccardo Scandariato; Wouter Joosen

KU Leuven, Belgium

05:00 - 05:30

• AppContext: Differentiating Malicious and Benign Mobile App Behaviors Using Context

  Mobile malware attempts to evade detection during app analysis by mimicking security-sensitive behaviors of benign apps that provide similar functionality (e.g., Sending SMS messages), and suppressing their payload to reduce the chance of being observed (e.g., Executing only its payload at night). Since current approaches focus their analyses on the types of security-sensitive resources being accessed (e.g., Network), these evasive techniques in malware make differentiating between malicious and benign app behaviors a difficult task during app analysis. We propose that the malicious and benign behaviors within apps can be differentiated based on the contexts that trigger security-sensitive behaviors, i.e., The events and conditions that cause the security-sensitive behaviors to occur. In this work, we introduce App Context, an approach of static program analysis that extracts the contexts of security-sensitive behaviors to assist app analysis in differentiating between malicious and benign behaviors. We implement a prototype of App Context and evaluate App Context on 202 malicious apps from various malware datasets, and 633 benign apps from the Google Play Store. App Context correctly identifies 192 malicious apps with 87.7% precision and 95% recall. Our evaluation results suggest that the maliciousness of a security-sensitive behavior is more closely related to the intention of the behavior (reflected via contexts) than the type of the security-sensitive resources that the behavior accesses.

   

  Preprint available:

Wei Yang; Xusheng Xiao; Benjamin Andow; Sihan Li; Tao Xie; William Enck

University of Illinois at Urbana-Champaign, USA; NEC Laboratories America, USA; North Carolina State University, USA