Information security is one of the most important attributes of distributed systems that often operate on unreliable networks. Enabling security features during the development of a distributed system requires the careful analysis of potential attacks or threats in different contexts, a process often referred to as «threat modeling». Information protection should be comprehensive, but it is also necessary to take into account the possibility of the emergence of threats specific to a certain information system. Many public and private organizations are still trying to implement system models and the threats directed at them on their own. The main reason for this is the lack of useful and high-quality methodologies that can help developers design system models. This review explores a variety of the literature on confidentiality- and integrity-aware system design methodologies, as well as threat classification methods, and identifies key issues that may be referenced by organizations to make design system processes easier. In particular, this article takes a look at the extent to which existing methodologies cover objects of protection and methods of classifying threats, as well as whether there are such models of systems in which the object itself and the threats directed at it are described. This includes whether the compiled models exhibit symmetry or asymmetry. This literature research shows that methodologies appear to be heterogeneous and versatile, since existing methodologies often only focus on one object of protection (a system). Based on the given analysis, it can be concluded that the existing methodologies only relate superficially to the description of system models and threats, and it is necessary to develop a more complete abstract model of the protected object and threats aimed at it in order to make this model suitable for any organization and protect it against most threats.

Current atmospheric composition sensors provide a large amount of high spectral resolution data. The accurate processing of this data employs time-consuming line-by-line (LBL) radiative transfer models (RTMs). In this paper, we describe a method to accelerate hyperspectral radiative transfer models based on the clustering of the spectral radiances computed with a low-stream RTM and the regression analysis performed for the low-stream and multi-stream RTMs within each cluster. This approach, which we refer to as the Cluster Low-Streams Regression (CLSR) method, is applied for computing the radiance spectra in the O₂ A-band at 760 nm and the CO₂ band at 1610 nm for five atmospheric scenarios. The CLSR method is also compared with the principal component analysis (PCA)-based RTM, showing an improvement in terms of accuracy and computational performance over PCA-based RTMs. As low-stream models, the two-stream and the single-scattering RTMs are considered. We show that the error of this approach is modulated by the optical thickness of the atmosphere. Nevertheless, the CLSR method provides a performance enhancement of almost two orders of magnitude compared to the LBL model, while the error of the technique is below 0.1% for both bands.

КОНЦЕПЦИЯ И СТРУКТУРА АВТОМАТИЗИРОВАННОЙ СИСТЕМЫ МОНИТОРИНГА КАЧЕСТВА ОБУЧЕНИЯ СТУДЕНТОВ