The large amounts of malware, and its diversity, have made it necessary for the security community to use automated dynamic analysis systems. These systems often rely on virtualization or emulation, and have recently started to be available to process mobile malware. Conversely, malware authors seek to detect such systems and evade analysis. In this paper, we present techniques for detecting Android runtime analysis systems. Our techniques are classified into four broad classes showing the ability to detect systems based on differences in behavior, performance, hardware and software components, and those resulting from analysis system design choices. We also evaluate our techniques against current publicly accessible systems, all of which are easily identified and can therefore be hindered by a motivated adversary. Our results show some fundamental limitations in the viability of dynamic mobile malware analysis platforms purely based on virtualization.

Despite considerable research on passwords, empirical studies of password strength have been limited by lack of access to plaintext passwords, small data sets, and password sets specifically collected for a research study or from low-value accounts. Properties of passwords used for high-value accounts thus remain poorly understood.

Application marketplaces are the main software distribution mechanism for modern mobile devices but are also emerging as a viable alternative to brick-and-mortar stores for personal computers. While most application marketplaces require applications to be cryptographically signed by their developers, in Android marketplaces, self-signed certificates are common, thereby offering very limited authentication properties. As a result, there have been reports of malware being distributed through application "repackaging". We provide a quantitative assessment of this phenomenon by collecting 41,057 applications from 194 alternative Android application markets in October 2011, in addition to a sample of 35,423 applications from the official Android market, Google Play. We observe that certain alternative markets almost exclusively distribute repackaged applications containing malware. To remedy this situation we propose a simple verification protocol, and discuss a proof-of-concept implementation, AppIntegrity. AppIntegrity strengthens the authentication properties offered in application marketplaces, thereby making it more difficult for miscreants to repackage apps, while presenting very little computational or communication overhead, and being deployable without requiring significant changes to the Android platform.

Like computers, mobile devices are both used to commit and are the subject of crimes. Digital forensics collection and analysis techniques need to adapt to cope with the unique characteristics of mobile devices. Fortunately, the rapid adoption of such devices has also resulted in a relatively homogeneous set of software platforms. In this paper, we describe a general methodology for performing digital forensic data collection on Android-based devices. By re-purposing a special Android boot mode, comprehensive extraction of evidence, with minimal potential for data corruption or omission, is possible.

Users tend to create passwords that are easy to guess, while system-assigned passwords tend to be hard to remember. Passphrases, space-delimited sets of natural language words, have been suggested as both secure and usable for decades. In a 1,476-participant online study, we explored the usability of 3- and 4-word system-assigned passphrases in comparison to system-assigned passwords composed of 5 to 6 random characters, and 8-character system-assigned pronounceable passwords. Contrary to expectations, system-assigned passphrases performed similarly to system-assigned passwords of similar entropy across the usability metrics we examined. Passphrases and passwords were forgotten at similar rates, led to similar levels of user difficulty and annoyance, and were both written down by a majority of participants. However, passphrases took significantly longer for participants to enter, and appear to require error-correction to counteract entry mistakes. Passphrase usability did not seem to increase when we shrunk the dictionary from which words were chosen, reduced the number of words in a passphrase, or allowed users to change the order of words.

Text-based passwords remain the dominant authentication method in computer systems, despite significant advancement in attackers' capabilities to perform password cracking. In response to this threat, password composition policies have grown increasingly complex. However, there is insufficient research defining metrics to characterize password strength and using them to evaluate password-composition policies. In this paper, we analyze 12,000 passwords collected under seven composition policies via an online study. We develop an efficient distributed method for calculating how effectively several heuristic password-guessing algorithms guess passwords. Leveraging this method, we investigate (a) the resistance of passwords created under different conditions to guessing, (b) the performance of guessing algorithms under different training sets, (c) the relationship between passwords explicitly created under a given composition policy and other passwords that happen to meet the same requirements, and (d) the relationship between guess ability, as measured with password-cracking algorithms, and entropy estimates. Our findings advance understanding of both password-composition policies and metrics for quantifying password security.