Blockchain based integrated security measure for reliable service delegation in 6G communication environment

5G networks: Types of isolation and their parameters in RAN and CN slices

In this paper authors propose a new approach to security assesment for cyberphysical systems. Existing security methodology applicable to information systems is ineffective for cyberphysical systems, which consists of information and physical components. Authors formulate requirements for security assesment taking into account key characteristics of cyberphysical systems. Analysis of scientific research devoted to development of a security assessment for cyberphysical systems has demonstrated their inconsistency with the formulated requirements. Authors propose a new assessment of cyberphysical systems security in the form of cyber-sustainabilty, which ensures retention of system in a stable steady state under destructive influence. Authors also propose a computational measure for assessing cyber-sustainability, based on self-similarity assessment. As a numerical measure, the Hurst exponent was chosen. Conducted experimental tests demonstrated effectiveness of proposed approach and high sensitivity of the Hurst exponent to destructive influence on cyberphysical systems.

Sustainability of cyber-physical systems in the context of targeted destructive influences

This paper describes security indicators specific to digital manufacturing. We divided the set of indicators into three groups: self-similarity-based security indicators, sustainability indicators, and indicators characterizing the homeostatic ability of cyberphysical systems that form the basis of digital production. Indicators can be applied to any type of digital production systems to detect security problems, control the sustainability of their operation, and maintain resilience.

Digital Manufacturing Security Indicators

This paper provides an approach to the detection of information security breaches in automated process control systems (APCS), which consists in forecasting multivariate time series formed from the values of the operating parameters of the end system devices. Using an experimental model of water treatment, a comparison was made of the forecasting results for the parameters characterizing the operation of the entire model, and for the parameters characterizing the flow of individual subprocesses implemented by the model. For forecasting, GRU-neural network training was performed.

Using GRU neural network for cyber-attack detection in automated process control systems

Characteristic features of security management for the cyber-physical system (CPS) have been distinguished. Existing approaches to the CPS security management have been systematized and their limited use in the CPS has been shown. An homeostatic approach to security management based on the CPS functional stability preservation has been proposed. A structure of homeostat for controlling the CPS has been described. A homeostatic model has been developed using logical predicates. The criteria for assessing the functional stability of the CPS have been proposed. The results of experimental studies showing the applicability of the proposed criteria have been presented.

Cyber-physical system homeostatic security management

Control methods for software defined networks and techniques for evaluating their operation using the multiobjective optimization apparatus are considered. The methods are based on monitoring the Quality of Service (QoS) parameters and ensuring network load balancing, higher response speed, and the availability and fault tolerance of OpenFlow-compatible network equipment and software defined network as a whole.

Increasing the fault tolerance and availability of software defined networks using network equipment control based on multiobjective optimization by service quality parameters

The problem of detection of automatically managed accounts (bots) in social networks has been considered. The method of their detection based on machine learning methods is proposed. The paper describes an example of a method based on artificial neural network learning. The parameters of a page in a social network used to detect bots have been presented. An experimental evaluation of the proposed system performance is given that demonstrates a high level of detection of bots in social networks.

The use of an artificial neural network to detect automatically managed accounts in social networks

The correctness and efficiency of the homeostatic approach to ensuring cyber-sustainability based on the software-defined networking (SDN) technology is proven. The cyberphysical system Smart Home is simulated using this technology. The authors conducted a series of experiments in which the system was countering various attacks. The software-defined network used three self-adaptation mechanisms to ensure cyber-sustainability in the experiment. The experiments confirmed that the SDN-based Smart Home system retained its sustainability under destructive impacts.

E. Yu. Pavlenko

Papers

Cyber-physical system homeostatic security management

Digital Manufacturing Security Indicators

Increasing the fault tolerance and availability of software defined networks using network equipment control based on multiobjective optimization by service quality parameters

The use of an artificial neural network to detect automatically managed accounts in social networks

Cyber-sustainability of Software-Defined Networks Based on Situational Management