Martina Lindorfer

Jingjing Ren, Ashwin Rao, Martina Lindorfer et al.

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.

Sebastian Neuner, Victor van der Veen, Martina Lindorfer et al.

Expecting the shipment of 1 billion Android devices in 2017, cyber criminals have naturally extended their vicious activities towards Google's mobile operating system. With an estimated number of 700 new Android applications released every day, keeping control over malware is an increasingly challenging task. In recent years, a vast number of static and dynamic code analysis platforms for analyzing Android applications and making decision regarding their maliciousness have been introduced in academia and in the commercial world. These platforms differ heavily in terms of feature support and application properties being analyzed. In this paper, we give an overview of the state-of-the-art dynamic code analysis platforms for Android and evaluate their effectiveness with samples from known malware corpora as well as known Android bugs like Master Key. Our results indicate a low level of diversity in analysis platforms resulting from code reuse that leaves the evaluated systems vulnerable to evasion. Furthermore the Master Key bugs could be exploited by malware to hide malicious behavior from the sandboxes.