Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts’ work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of “Form and Content” data transformations.

Over the past decade, wireless communication technologies have developed significantly for intelligent applications in road transport. This paper provides an overview of telecommunications-based intelligent transport systems with a focus on ensuring system safety and resilience. In vehicle-to-everything, these problems are extremely acute due to the specifics of the operation of transport networks, which requires the use of special protection mechanisms. In this regard, it was decided to use blockchain as a system platform to support the needs of transport systems for secure information exchange. This paper describes the technological aspects of implementing blockchain technology in vehicle-to-network; the features of such technology are presented, as well as the features of their interaction. The authors considered various network characteristics and identified the parameters that have a primary impact on the operation of the vehicle-to-network (V2N) network when implementing the blockchain. In the paper, an experiment was carried out that showed the numerical characteristics for the allocation of resources on devices involved in organizing V2N communication and conclusions were drawn from the results of the study.

During the last decade there has been an essential development of wireless communication technologies for intelligent transportation system (ITS) applications for motor transport; these advanced infocommunication technologies are called vehicular ad hoc networks (VANET). VANET/ITS, in particular, inform and warn drivers about possible obstacles, and also the possibility of how to organize coordinated actions. Therefore, any violation of its functioning by cyber attacks automatically influences the safety of people and automotive engineering on the road. The purpose of this article is to provide an analytical overview of cyber attacks on VANET/ITS, presented in state-of-the-art publications on this topic by the prediction of its cyber resistance. We start with an analysis of the top 10 cyber threats, considered according to the following schemes: attack mechanism, vulnerability, damage, object of attack, and a counter measure. We then set out a synergistic approach for assessing the cyber resistance of the forward-looking VANET/ITS conceptual model, formed by the merger of the internet of vehicles and software-defined networking technology. Finally, we identify open issues and associated research opportunities, the main ones being the formalization of threats, vulnerability stratification, the choice of the level of network management centralization and, last but not least, the modeling and prediction of VANET/ITS cyber resistance.

This paper solves the problem of modeling the scheme for developing software systems, which can be used in building solutions for secure energy networks. A development scheme is proposed in a set of representations through which each program of the software complex passes, namely the following representations: idea, conceptual model, architecture, algorithm, source code, graphic code, abstract syntax tree, assembler code, machine code, byte code, executed code. The main properties of each representation are indicated, such as the form (text, graphic, programming language, binary, and decoded), development (transformation) methods, as well as vulnerabilities that are detected in it. An example of each representation is given, particularly as applied to the energy networks. The scheme elements (representations, vulnerabilities, forms, etc.) and the main operations for working with their elements (representation transformation, vulnerability injection, and detection) are presented in an analytical form. An example of a development scheme for a simple software complex of energy networks is given. The classification of vulnerabilities is introduced; it divides the vulnerabilities according to the structural level, functioning disruption, and information impact. The vulnerabilities in each of the views are substantiated using the common vulnerabilities and exposures (CVE) database. An experiment was conducted to demonstrate the vulnerability spread across representations during the development of a software complex example for the energy network. The features of the applications of the obtained results for energy networks are taken into account. The advantages, disadvantages, and limitations of the study, as well as ways to eliminate them, are discussed.

Ensuring the security of modern cyberphysical devices is the most important task of the modern world. The reason for this is that such devices can cause not only informational, but also physical damage. One of the approaches to solving the problem is the static analysis of the machine code of the firmware of such devices. The situation becomes more complicated in the case of a Smart Home, since its devices can have different processor architectures (means instruction sets). In the case of cyberphysical devices of the Smart Home, the destruction of machine code due to physical influences is also possible. Therefore, the first step is to correctly identify the processor architecture. In the interests of this, a machine code model is proposed that has a formal notation and takes into account the possibility of code destruction. The article describes the full cycle of research (including experiment) in order to obtain this model. The model is based on byte-frequency machine code signatures. The experiment resulted in obtaining template signatures for the Top-16 processor architectures: Alpha, X32, Amd64, Arm64, Hppa64, I486, I686, Ia64, Mips, Mips64, Ppc, Ppc64, RiscV64, S390, S390x and Sparc64.