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Mobile applications create their own security and privacy models through permission-based models. Some applications may request extra permissions that they do not need but may use for suspicious activities. The aim of this study is to identify those spare permissions requested and use this information in the security and privacy approach, which uses static and code analysis together and applies them to the existing datasets; then the results are compared and accuracy level is determined. Classification is made with an accuracy rate of 91.95%.

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Summary As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.

As the use of smartphones increases, Android, as a Linux-based open source mobile operating system (OS), has become the most popular mobile OS in time. Due to the widespread use of Android, malware developers mostly target Android devices and users. Malware detection systems to be developed for Android devices are important for this reason. Machine learning methods are being increasingly used for detection and analysis of Android malware. This study presents a method for detecting Android malware using feature selection with genetic algorithm (GA). Three different classifier methods with different feature subsets that were selected using GA were implemented for detecting and analyzing Android malware comparatively. A combination of Support Vector Machines and a GA yielded the best accuracy result of 98.45% with the 16 selected permissions using the dataset of 1740 samples consisting of 1119 malwares and 621 benign samples.

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Besides the applications aimed at increasing the efficiency of the Android mobile devices, also many malicious applications, millions of Android malware according to various security company reports, are being developed and uploaded into the application stores. In order to detect those applications, a malicious Android application detection system based on permission and permission groups namely, AppPerm Analyzer has been developed. The AppPerm Analyzer software extracts the manifest and code permissions of analyzed applications, creates duple and triple permission groups from them, calculates risk scores of these permissions and permission groups according to their usage rates in malicious and benign applications and calculates the total risk score of the analyzed application. After training the software with 7776 applications in total, it is tested with 1664 benign and 1664 malicious applications. In the tests, AppPerm Analyzer detected malicious applications with an accuracy of 96.19% at most. At this point, sensitivity (true-positive ratio) is 95.50% and specificity (true-negative ratio) is 96.88%. If a false-positive ratio up to 10% is accepted, the sensitivity increases to 99.04%.