Automatic Control and Computer Sciences

The paper presents an overview of current attacks on BIOS and Intel ME embedded software of modern Intel-based computers. We describe the results of the analysis of its security for system boards of basic manufacturers. We also allocate classes of attacks that make it possible to create implants whose discovery by traditional methods of searching for undeclared features becomes impossible or extremely difficult.

In this paper, we consider some probability-theoretical models of information distortion at the message level. The checksum (CS) distributions typical of TCP-type protocols are investigated. As an addition operator in the CS, bitwise coordinate addition and binary addition with carryover are considered. For the distortion models described, asymptotic estimates of CS error probabilities are obtained at a small distortion probability. These asymptotic estimates can be useful for messages with long segments.

The paper is devoted to the problem of improving the performance of microprocessors used in access control systems. We allocate requirements and propose a variant of the composition of instructions that are necessary for the high-quality design of microprocessors (operating through a residue number system, for access control), which are aimed at improving their performance in the composition of an access control system.

The article presents a model of a Big Data management system for solving information security (IS) problems for systems of this class. The features of the considered systems are highlighted. The model and architecture of data processing are mathematically described based on the ANSI/SPARC concept and the principles of modeling information systems in IS. The model is analyzed.

This article considers the solution to the problem of building secure Big Data management systems using a consistent approach. The concept and features of Big Data management systems and their differences from traditional DBMS’s are presented. The principles of a new, consistent approach to building secure Big Data management systems are given and substantiated. The security subsystem architecture is proposed.

The paper is concerned with the current state of the question of using unmanned ships in the field of water transport, as well as the most promising areas for applying remote ship piloting technology. The organization of information systems for data transmission is considered in detail, and relevant protocols are proposed. Technical solutions for equipping specialized unmanned transport vessels for piloting and mooring of heavy cargo ships are also proposed.

This paper presents the issues of cybersecurity of the industrial internet of things as the main driver for the development of Enterprise 4.0, compared with the staged protection approach used in automated process control systems. Threat classes relevant to the industrial internet of things are presented. Based on the analysis, requirements are put forward for designing an architecture of the industrial internet of things resistant to network threats, as well as an approach to the implementation of this architecture based on control and communications stability.

The problem of the dynamic analysis of website security using asynchronous content loading technologies is considered. The possibility of solving the problem of detecting scan input vectors using the technology of interpreting JavaScript scripts is considered.

Open Platform Communications (OPC), the interoperability standard for the secure and reliable exchange of data in the industrial automation space, consists of two main types of protocol – classic and unified. The paper reviews a classic set of DA/HDA/A&E protocols, which is based on Microsoft DCOM and RPC technologies. Architectural cyber threats of the classic type of OPC are systematized in this work.

In this paper, we discuss the construction of a model for protecting information resources based on new approaches to active search, unified investigation, and response to cyber threats. The process of identifying cyber threats is based on search prediction methods interconnected with the cyclic nature of the behavioral activity of users of network resources.

This article presents an approach to building a system for identifying cyberthreats in modern industrial systems (IIoT, VANET, WSN) by means of artificial intelligence and deep learning. The results of the tests conducted to assess the suggested approach for efficiency based on deep-learning networks are provided.

The model of cyberattacks on digital production systems is presented. The examples of the most significant information security incidents in this type of systems are considered in full detail. Possible approaches to ensuring the cybersecurity of digital production systems are described.

The author proposes an approach for predicting the state of Smart Grid components, which is based on a combination of the mathematical techniques of the Kalman filter and machine learning. Prediction of the state will make it possible to detect cyberattacks implemented against a Smart Grid at an early stage.

The problem of automation of the process of detecting weaknesses in information systems based on the analysis of its vulnerabilities is solved. The input data used for the classification, the process of their preparation, and the classification results are described. The interpretation and analysis of the results are presented. The problem solved is a part of the approach proposed by the authors to automatic formation of a set of required security tools for a particular information system. A necessary set of security tools is determined by the set of possible threats.

The use of clustering algorithms to determine the types of malicious software files based on the analysis of the WinAPI function call sequences is investigated. The use of clustering algorithms such as k-means, EM-algorithm, hierarchical algorithm, and the affinity propagation method is considered. The quality of clustering is evaluated using the silhouette metrics, the Calinski–Harabasz index, and the Davies–Bouldin index.

The authors propose an approach for the prevention of network denial of service attacks, which is based on predicting the values of the multiple correlation coefficients of the detail coefficients of the discrete wavelet transform of network traffic parameters.