Мобильное приложение изучения языков на платформе Android
Subject of Research. The paper presents the review of existing platforms for development of mobile applications with outdoor-quests. A method for automation design and planning of outdoor-quests is proposed. The principles for development automation of mobile applications containing such quests are described. Method. The novelty of the proposed approach lies in procedural quest generation based on a set of limitations. The architecture combines the usage of open technologies with quests generation and includes special tools for automated creation of outdoor quest templates on various subjects as well as customizable application templates. Main Results. Experimental research was carried out for evaluation of the proposed architecture features. By implementing quest generator and application templates a demo Android application was created. The application consisted of two quests: manually created and generated one. The generated quest was made by quest generator and extended by human. The application was published at Play Market Store. The experiment goal was to measure how long will it take for application users to find different quest object and how long will it take to pass the quest all over in order to determine the differences in generated and manually created quests. As a result of comparative measurements a conclusion was made about allowable difference between quests because it was less than attention cycle duration for humans. This fact demonstrates that generated quests can be used in the same manner as manually created ones. Practical Relevance. The proposed quest generator can be used for wide range of topics because quest object selection is based on keyword search and quest route geometry criterion application. The solution also has practical significance because mobile applications developed with the use of the proposed architecture can be adapted to different domain areas. Wherein mobile application development time is reduced owing to automation and customizable templates usage.
In this paper, we propose a heuristic approach to static analysis of Android applications based on matching suspicious applications with the predefined malware models. Static models are built from Android capabilities and Android Framework API call chains used by the application. All of the analysis steps and model construction are fully automated. Therefore, the method can be easily deployed as one of the automated checks provided by mobile application marketplaces or other interested organizations. Using the proposed method, we analyzed the Drebin and ISCX malware collections in order to find possible relationships and dependencies between samples in collections, and a large fraction of Google Play apps collected between 2013 and 2016 representing benign data. Analysis results show that a combination of relatively simple static features represented by permissions and API call chains is enough to perform binary classification between malware and benign apps, and even find the corresponding malware family, with an appropriate false positive rate of about 3%. Malware collections exploration results show that modern Android malware rarely uses obfuscation or encryption techniques to make static analysis more difficult, which is quite the opposite of what we see in the case of the “Wintel” endpoint platform family. We also provide the experiment-based comparison with the previously proposed stateof-the-art Android malware detection method adagio. This method outperforms our proposed method in resulting detection coverage (98 vs 91 % of malicious samples are covered) while at the same time causing a significant number of false alarms corresponding to 9.3 % of benign applications on average.
The theme of this work is the development of a system for the transmission of streaming content using p2p-technologies. The advantage of the system being developed is the reduction of the load on the centralized nodes of the system and the maintenance of the maximum distribution of tasks between the nodes in the system.