The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from device security and network optimization, to fine-grained user profiling). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: (i) the goal of the analysis; (ii) the point where the network traffic is captured; and (iii) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi Access Points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.Comment: 55 page

A Comprehensive Security Framework for Securing Sensors in Smart Devices and Applications

This doctoral dissertation introduces novel security frameworks to detect sensor-based threats on smart devices and applications in smart settings such as smart home, smart office, etc. First, we present a formal taxonomy and in-depth impact analysis of existing sensor-based threats to smart devices and applications based on attack characteristics, targeted components, and capabilities. Then, we design a novel context-aware intrusion detection system, 6thSense, to detect sensor-based threats in standalone smart devices (e.g., smartphone, smart watch, etc.). 6thSense considers user activity-sensor co-dependence in standalone smart devices to learn the ongoing user activity contexts and builds a context-aware model to distinguish malicious sensor activities from benign user behavior. Further, we develop a platform-independent context-aware security framework, Aegis, to detect the behavior of malicious sensors and devices in a connected smart environment (e.g., smart home, offices, etc.). Aegis observes the changing patterns of the states of smart sensors and devices for user activities in a smart environment and builds a contextual model to detect malicious activities considering sensor-device-user interactions and multi-platform correlation. Then, to limit unauthorized and malicious sensor and device access, we present, kratos, a multi-user multi-device-aware access control system for smart environment and devices. kratos introduces a formal policy language to understand diverse user demands in smart environment and implements a novel policy negotiation algorithm to automatically detect and resolve conflicting user demands and limit unauthorized access. For each contribution, this dissertation presents novel security mechanisms and techniques that can be implemented independently or collectively to secure sensors in real-life smart devices, systems, and applications. Moreover, each contribution is supported by several user and usability studies we performed to understand the needs of the users in terms of sensor security and access control in smart devices and improve the user experience in these real-time systems

A study of security issues of mobile apps in the android platform using machine learning approaches

Mobile app poses both traditional and new potential threats to system security and user privacy. There are malicious apps that may do harm to the system, and there are mis-behaviors of apps, which are reasonable and legal when not abused, yet may lead to real threats otherwise. Moreover, due to the nature of mobile apps, a running app in mobile devices may be only part of the software, and the server side behavior is usually not covered by analysis. Therefore, direct analysis on the app itself may be incomplete and additional sources of information are needed. In this dissertation, we discuss how we can apply machine learning techniques in multiple tasks for security issues in regard of mobile apps in the Android platform. These include malicious apps detection and security risk estimation of apps. Both direct sources of information from the developer of apps and indirect sources of information from user comments are utilized in these tasks. We also propose comparison of these different sources in the task of security risk estimation to point out the necessity of usage of indirect sources in mobile app security tasks

Privacy Leakage in Mobile Computing: Tools, Methods, and Characteristics

The number of smartphones, tablets, sensors, and connected wearable devices are rapidly increasing. Today, in many parts of the globe, the penetration of mobile computers has overtaken the number of traditional personal computers. This trend and the always-on nature of these devices have resulted in increasing concerns over the intrusive nature of these devices and the privacy risks that they impose on users or those associated with them. In this paper, we survey the current state of the art on mobile computing research, focusing on privacy risks and data leakage effects. We then discuss a number of methods, recommendations, and ongoing research in limiting the privacy leakages and associated risks by mobile computing

An Empirical Study on Android for Saving Non-shared Data on Public Storage

With millions of apps that can be downloaded from official or third-party market, Android has become one of the most popular mobile platforms today. These apps help people in all kinds of ways and thus have access to lots of user's data that in general fall into three categories: sensitive data, data to be shared with other apps, and non-sensitive data not to be shared with others. For the first and second type of data, Android has provided very good storage models: an app's private sensitive data are saved to its private folder that can only be access by the app itself, and the data to be shared are saved to public storage (either the external SD card or the emulated SD card area on internal FLASH memory). But for the last type, i.e., an app's non-sensitive and non-shared data, there is a big problem in Android's current storage model which essentially encourages an app to save its non-sensitive data to shared public storage that can be accessed by other apps. At first glance, it seems no problem to do so, as those data are non-sensitive after all, but it implicitly assumes that app developers could correctly identify all sensitive data and prevent all possible information leakage from private-but-non-sensitive data. In this paper, we will demonstrate that this is an invalid assumption with a thorough survey on information leaks of those apps that had followed Android's recommended storage model for non-sensitive data. Our studies showed that highly sensitive information from billions of users can be easily hacked by exploiting the mentioned problematic storage model. Although our empirical studies are based on a limited set of apps, the identified problems are never isolated or accidental bugs of those apps being investigated. On the contrary, the problem is rooted from the vulnerable storage model recommended by Android. To mitigate the threat, we also propose a defense framework