Almost all industrial internet of things (IIoT) attacks happen at the data transmission layer according to a majority of the sources. In IIoT, different machine learning (ML) and deep learning (DL) techniques are used for building the intrusion detection system (IDS) and models to detect the attacks in any layer of its architecture. In this regard, minimizing the attacks could be the major objective of cybersecurity, while knowing that they cannot be fully avoided. The number of people resisting the attacks and protection system is less than those who prepare the attacks. Well-reasoned and learning-backed problems must be addressed by the cyber machine, using appropriate methods alongside quality datasets. The purpose of this paper is to describe the development of the cybersecurity datasets used to train the algorithms which are used for building IDS detection models, as well as analyzing and summarizing the different and famous internet of things (IoT) attacks. This is carried out by assessing the outlines of various studies presented in the literature and the many problems with IoT threat detection. Hybrid frameworks have shown good performance and high detection rates compared to standalone machine learning methods in a few experiments. It is the researchers’ recommendation to employ hybrid frameworks to identify IoT attacks for the foreseeable future.

The article explores approaches to determining the author of a natural language text and the advantages and disadvantages of these approaches. The importance of the considered problem is due to the active digitalization of society and reassignment of most parts of the life activities online. Text authorship methods are particularly useful for information security and forensics. For example, such methods can be used to identify authors of suicide notes, and other texts are subjected to forensic examinations. Another area of application is plagiarism detection. Plagiarism detection is a relevant issue both for the field of intellectual property protection in the digital space and for the educational process. The article describes identifying the author of the Russian-language text using support vector machine (SVM) and deep neural network architectures (long short-term memory (LSTM), convolutional neural networks (CNN) with attention, Transformer). The results show that all the considered algorithms are suitable for solving the authorship identification problem, but SVM shows the best accuracy. The average accuracy of SVM reaches 96%. This is due to thoroughly chosen parameters and feature space, which includes statistical and semantic features (including those extracted as a result of an aspect analysis). Deep neural networks are inferior to SVM in accuracy and reach only 93%. The study also includes an evaluation of the impact of attacks on the method on models’ accuracy. Experiments show that the SVM-based methods are unstable to deliberate text anonymization. In comparison, the loss in accuracy of deep neural networks does not exceed 20%. Transformer architecture is the most effective for anonymized texts and allows 81% accuracy to be achieved.

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.

The article deals with the current problem of ensuring security when developing embedded technologies for the Internet of things. The authors analyze modern technological features of this concept; consider the main vulnerabilities of information security. An approach is proposed to protect the data transferred between the physical, logical and virtual components of the dedicated Internet infrastructure of things. Distinctive features of the proposed approach are the intellectual analysis of messages transmitted in IoT and the use of digital watermarks for data transfer between IoT-devices.

Authorship attribution is one of the important fields of natural language processing (NLP). Its popularity is due to the relevance of implementing solutions for information security, as well as copyright protection, various linguistic studies, in particular, researches of social networks. The article is a continuation of the series of studies aimed at the identification of the Russian-language text’s author and reducing the required text volume. The focus of the study was aimed at the attribution of textual data created as a product of human online activity. The effectiveness of the models was evaluated on the two Russian-language datasets: literary texts and short comments from users of social networks. Classical machine learning (ML) algorithms, popular neural networks (NN) architectures, and their hybrids, including convolutional neural network (CNN), networks with long short-term memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and fastText, that have not been used in previous studies, were applied to solve the problem. A particular experiment was devoted to the selection of informative features using genetic algorithms (GA) and evaluation of the classifier trained on the optimal feature space. Using fastText or a combination of support vector machine (SVM) with GA reduced the time costs by half in comparison with deep NNs with comparable accuracy. The average accuracy for literary texts was 80.4% using SVM combined with GA, 82.3% using deep NNs, and 82.1% using fastText. For social media comments, results were 66.3%, 73.2%, and 68.1%, respectively.

In Tomsk University of Control Systems and Radioelectronics (TUSUR) one of the main areas of research is information security. The work is carried out by a scientific group under the guidance of Professor Shelupanov. One of the directions is the development of a comprehensive approach to assessing the security of the information systems. This direction includes the construction of an information security threats model and a protection system model, which allow to compile a complete list of threats and methods of protection against them. The main directions of information security tools development are dynamic methods of biometrics, methods for generating prime numbers for data encryption, steganography, methods and means of data protection in Internet of Things (IoT) systems. The article presents the main results of research in the listed areas of information security. The resultant properties in symmetric cryptography are based on the properties of the power of the generating functions. The authors have obtained symmetric principles for the development of primality testing algorithms, as discussed in the Appendix.