Towards a Network-based Approach for Smartphone Security

Smartphones have become an important utility that affects many aspects of our daily life. Due to their large dissemination and the tasks that are performed with them, they have also become a valuable target for criminals. Their specific capabilities and the way they are used introduce new threats in terms of information security. The research field of smartphone security has gained a lot of momentum in the past eight years. Approaches that have been presented so far focus on investigating design flaws of smartphone operating systems as well as their potential misuse by an adversary. Countermeasures are often realized based upon extensions made to the operating system itself, following a host-based design approach. However, there is a lack of network-based mechanisms that allow a secure integration of smartphones into existing IT infrastructures. This topic is especially relevant for companies whose employees use smartphones for business tasks. This thesis presents a novel, network-based approach for smartphone security called CADS: Context-related Signature and Anomaly Detection for Smartphones. It allows to determine the security status of smartphones by analyzing three aspects: (1) their current configuration in terms of installed software and available hardware, (2) their behavior and (3) the context they are currently used in. Depending on the determined security status, enforcement actions can be defined in order to allow or to deny access to services provided by the respective IT infrastructure. The approach is based upon the distributed collection and central analysis of data about smartphones. In contrast to other approaches, it explicitly supports to leverage existing security services both for analysis and enforcement purposes. A proof of concept is implemented based upon the IF-MAP protocol for network security and the Google Android platform. An evaluation verifies (1) that the CADS approach is able to detect so-called sensor sniffing attacks and (2) that reactions can be triggered based on detection results to counter ongoing attacks. Furthermore, it is demonstrated that the functionality of an existing, host-based approach that relies on modifications of the Android smartphone platform can be mimicked by the CADS approach. The advantage of CADS is that it does not need any modifications of the Android platform itself

Advanced Security Analysis for Emergent Software Platforms

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems. This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the focus on detecting vulnerable and unsafe interactions between applications that share common components and devices. Contributions of this work include novel insights and methods for: (1) detecting vulnerable interactions between Android applications that leverage dynamic loading features for concealing the interactions; (2) identifying unsafe interactions between smart home applications by considering physical and cyber channels; (3) detecting malicious IoT applications that are developed to target numerous IoT devices; (4) detecting insecure patterns of emergent security APIs that are reused from open-source software. In all of the four research thrusts, we present thorough security analysis and extensive evaluations based on real-world applications. Our results demonstrate that the proposed detection mechanisms can efficiently and effectively detect vulnerabilities in contemporary software platforms. Advisers: Hamid Bagheri and Qiben Ya

MobiCoMonkey - Context Testing of Android Apps

The functionality of many mobile applications is dependent on various contextual, external factors. Depending on unforeseen scenarios, mobile apps can even malfunction or crash. In this paper, we have introduced MobiCoMonkey - automated tool that allows a developer to test app against custom or auto generated contextual scenarios and help detect possible bugs through the emulator. Moreover, it reports the connection between the bugs and contextual factors so that the bugs can later be reproduced. It utilizes the tools offered by Android SDK and logcat to inject events and capture traces of the app execution.Comment: 4 page

PADA: Power-aware development assistant for mobile sensing applications

� 2016 ACM. We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.N

How Should You plan Your App’s Features? Selecting and Prioritizing A Mobile App’s Initial Features Based on User Reviews

The app market is extremely competitive, with users typically having several alternative app possibilities. To attract and retain users, it is imperative for developers to consider the ratings and reviews their apps receive. App reviews frequently contain feature requests, sometimes hidden among complaints. Developers use these complaints and requests to improve their apps, thus increasing their rating which is incredibly important for attracting new users. Unfortunately, developers of new apps are at a severe disadvantage: They do not have the benefit of existing reviews, with only the reviews of similar apps to potentially rely upon. To address this problem, we conducted a study and developed a novel technique that extracts feature requests from similar, existing apps to help prioritize the features and requirements important in an initial app release. We compared different classification models in order to identify most appropriate classifier for classifying reviews category-wise. We found that there is not one single classifier that could have a higher accuracy than others for all categories.Our study also involved extracting features from user reviews in the Google Play store. The features were presented to 17 Android developers twice; once without applying our technique and once after applying our technique. Our proposed technique created a 48\% reduction in the number of features considered high priority by participants; helping developers focus on what features to consider for their apps. We surprisingly found that the frequency of requested features did not impact the developer\u27s decisions in prioritizing the features in the inclusion of new apps