An Analysis of Tracking Service Settings in Blackberry 10 and Windows Phone 8 Smartphones

The use of tracking settings in smartphones facilitates the provision of tailored services to users by allowing service providers access to unique identifiers stored on the smartphones. In this paper, we investigate the `tracking of' settings on the Blackberry 10 and Windows Phone 8 platforms. To determine if they work as claimed, we set up a test bed suitable for both operating systems to capture traffic between the smartphone and external servers. We dynamically execute a set of similar Blackberry 10 and Windows Phone 8 applications, downloaded from their respective official markets. Our results indicate that even if users turn of tracking settings in their smartphones, some applications leak unique identifiers without their knowledge

HLA genotyping in the international Type 1 Diabetes Genetics Consortium

Background Although human leukocyte antigen (HLA) DQ and DR loci appear to confer the strongest genetic risk for type 1 diabetes, more detailed information is required for other loci within the HLA region to understand causality and stratify additional risk factors. The Type 1 Diabetes Genetics Consortium (T1DGC) study design included high-resolution genotyping of HLA-A, B, C, DRB1, DQ, and DP loci in all affected sibling pair and trio families, and cases and controls, recruited from four networks worldwide, for analysis with clinical phenotypes and immunological markers

Android applications : Data leaks via advertising libraries

Recent studies have determined that many Android applications in both official and non-official online markets expose details of the users\u27 smartphones without user consent. In this paper, we explain why such applications leak, how they leak and where the data is leaked to. In order to achieve this, we combine static and dynamic analysis to examine Java classes and application behaviour for a set of popular, clean applications from the Finance and Games categories. We observed that all the applications in our data set which leaked information (10%) had third-party advertising libraries embedded in their respective Java packages

Mining permission patterns for contrasting clean and malicious android applications

An Android application uses a permission system to regulate the access to system resources and users\u27 privacy-relevant information. Existing works have demonstrated several techniques to study the required permissions declared by the developers, but little attention has been paid towards used permissions. Besides, no specific permission combination is identified to be effective for malware detection. To fill these gaps, we have proposed a novel pattern mining algorithm to identify a set of contrast permission patterns that aim to detect the difference between clean and malicious applications. A benchmark malware dataset and a dataset of 1227 clean applications has been collected by us to evaluate the performance of the proposed algorithm. Valuable findings are obtained by analyzing the returned contrast permission patterns. © 2013 Elsevier B.V. All rights reserved

Contrasting permission patterns between clean and malicious android applications

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2013. The Android platform uses a permission system model to allow users and developers to regulate access to private information and system resources required by applications. Permissions have been proved to be useful for inferring behaviors and characteristics of an application. In this paper, a novel method to extract contrasting permission patterns for clean and malicious applications is proposed. Contrary to existing work, both required and used permissions were considered when discovering the patterns. We evaluated our methodology on a clean and a malware dataset, each comprising of 1227 applications. Our empirical results suggest that our permission patterns can capture key differences between clean and malicious applications, which can assist in characterizing these two types of applications

Analysis of malicious and benign android applications

Since its establishment, the Android applications market has been infected by a proliferation of malicious applications. Recent studies show that rogue developers are injecting malware into legitimate market applications which are then installed on open source sites for consumer uptake. Often, applications are infected several times. In this paper, we investigate the behavior of malicious Android applications, we present a simple and effective way to safely execute and analyze them. As part of this analysis, we use the Android application sandbox Droidbox to generate behavioral graphs for each sample and these provide the basis of the development of patterns to aid in identifying it. As a result, we are able to determine if family names have been correctly assigned by current anti-virus vendors. Our results indicate that the traditional anti-virus mechanisms are not able to correctly identify malicious Android applications

Efficient Classification of Android Malware in the Wild Using Robust Static Features

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