Abstract:

This paper is devoted to the design and development of a system for automating numerical similarity assessment of Android applications. The task of application similarity evaluation is reduced to the similarity evaluation of sets of control flow graphs constructed based on code from classes.dex files of applications. The similarity value was calculated based on the similarity matrix. The algorithms of graph editing and Levenshtein distance were used to compare control flow graphs. Application similarity criteria were formulated and their representation forms were investigated. Types of Android application models and methods of their construction are presented. A prototype of the system for automating the numerical evaluation of Android-applications similarity is developed. Optimization of the software solution is performed with the help of parallel programming tools. Experiments are carried out and the conclusion is made about the ability of the developed system to detect similarities between Android applications.

Abstract: It is shown that trust in the content of information resources can be assessed by means of a publishing criterion, with the information recourses being of the trusted and distrusted type. The task of assessment of trust consists of four subtasks: (1) building multisets of physical quantities available in primary data sources, (2) alignment of values of physical quantities, (3) formulation of quantitative restrictions for publishing criterion in different ranges of change of physical quantities, and (4) decomposition of expert data. Spectral data publishing criteria and restrictions required for solving data alignment tasks are outlined. Alignment results have been tabulated. Using vacuum wavenumbers as an example, restrictions inherent in publishing criteria are formulated. The assessment of the content trust obtained from solutions to the tasks of decomposition f expert data are presented as the OWL-ontologies. Building knowledge bases of this kind at virtual data centers intended for data intensive science will provide realization of an automatic selection of spectroscopic information resources exhibiting a high degree of trust.