Conceivable security risks and authentication techniques for smart devices

With the rapidly escalating use of smart devices and fraudulent transaction of users’ data from their devices, efficient and reliable techniques for authentication of the smart devices have become an obligatory issue. This paper reviews the security risks for mobile devices and studies several authentication techniques available for smart devices. The results from field studies enable a comparative evaluation of user-preferred authentication mechanisms and their opinions about reliability, biometric authentication and visual authentication techniques

ConXsense - Automated Context Classification for Context-Aware Access Control

We present ConXsense, the first framework for context-aware access control on mobile devices based on context classification. Previous context-aware access control systems often require users to laboriously specify detailed policies or they rely on pre-defined policies not adequately reflecting the true preferences of users. We present the design and implementation of a context-aware framework that uses a probabilistic approach to overcome these deficiencies. The framework utilizes context sensing and machine learning to automatically classify contexts according to their security and privacy-related properties. We apply the framework to two important smartphone-related use cases: protection against device misuse using a dynamic device lock and protection against sensory malware. We ground our analysis on a sociological survey examining the perceptions and concerns of users related to contextual smartphone security and analyze the effectiveness of our approach with real-world context data. We also demonstrate the integration of our framework with the FlaskDroid architecture for fine-grained access control enforcement on the Android platform.Comment: Recipient of the Best Paper Awar

Mobile Application Security Platforms Survey

Nowadays Smartphone and other mobile devices have become incredibly important in every aspect of our life. Because they have practically offered same capabilities as desktop workstations as well as come to be powerful in terms of CPU (Central processing Unit), Storage and installing numerous applications. Therefore, Security is considered as an important factor in wireless communication technologies, particularly in a wireless ad-hoc network and mobile operating systems. Moreover, based on increasing the range of mobile application within variety of platforms, security is regarded as on the most valuable and considerable debate in terms of issues, trustees, reliabilities and accuracy. This paper aims to introduce a consolidated report of thriving security on mobile application platforms and providing knowledge of vital threats to the users and enterprises. Furthermore, in this paper, various techniques as well as methods for security measurements, analysis and prioritization within the peak of mobile platforms will be presented. Additionally, increases understanding and awareness of security on mobile application platforms to avoid detection, forensics and countermeasures used by the operating systems. Finally, this study also discusses security extensions for popular mobile platforms and analysis for a survey within a recent research in the area of mobile platform security

Survey of End-to-End Mobile Network Measurement Testbeds, Tools, and Services

Mobile (cellular) networks enable innovation, but can also stifle it and lead to user frustration when network performance falls below expectations. As mobile networks become the predominant method of Internet access, developer, research, network operator, and regulatory communities have taken an increased interest in measuring end-to-end mobile network performance to, among other goals, minimize negative impact on application responsiveness. In this survey we examine current approaches to end-to-end mobile network performance measurement, diagnosis, and application prototyping. We compare available tools and their shortcomings with respect to the needs of researchers, developers, regulators, and the public. We intend for this survey to provide a comprehensive view of currently active efforts and some auspicious directions for future work in mobile network measurement and mobile application performance evaluation.Comment: Submitted to IEEE Communications Surveys and Tutorials. arXiv does not format the URL references correctly. For a correctly formatted version of this paper go to http://www.cs.montana.edu/mwittie/publications/Goel14Survey.pd

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