Security Of Industrial Systems Research Articles

Enhancing attack resilience of cyber-physical systems through state dependency graph models

AbstractThis paper presents a method that utilizes graph theory and state modelling algorithms to perform automatic complexity analysis of the architecture of cyber-physical systems (CPS). It describes cyber physical systems risk assessment (CPSRA), a tool to provide automatic decision support for enhancing the overall resilience of CPS architectures often used in critical infrastructures. CPRSA is built to enhance industrial risk assessment and improve the resilience of CPS architecture against malicious attacks on the cyber domain that can affect industrial processes, which is critical in a distributed cyber environment. Such attacks often compromise execution states on physical components and lead to hazards or even disasters through plant malfunction. CPSRA is tested against a real-world testbed model of a large SCADA system that is infused with real-world CVE vulnerabilities in some of its components. The tool creates an isomorphic graph of the CPS process model and uses graph algorithms and network analytics on the model to test cyber-attacks and evaluate attack resilience aspects. The tool’s output is then used to pinpoint high-complexity components in terms of influence on the overall CPS architecture and suggest mitigation points for security measure implementation while considering every potential subattack path and subliminal path on the model’s attack graph. The paper complements standardized assessment reports and contributes to automatic architecture assessment for critical infrastructure environments and can be used as the basis to model dependencies and threat propagation in larger digital twins, a need outlined in major NIST publications concerning the security of industrial systems that was previously done manually, without automatic insight into state and vulnerability influences.

Methods and technologies for ensuring cybersecurity of industrial and web-oriented systems and networks

The subject matter of the article is methods and technologies of ensuring the cybersecurity of industrial and web-oriented systems and networks, training of cybersecurity specialists during the acquisition of professional knowledge. The purpose of the article is to ensure the cybersecurity of industrial and web-oriented systems and networks by developing and implementing appropriate methodologies (concepts, principles, set of models, methods) and technologies in the industry, as well as in training the cybersecurity specialists during the acquisition of professional knowledge. The problem of developing models, methods, and technologies for ensuring the cybersecurity of mobile systems, web-oriented systems based on content management systems, virtual networks that provide their interaction, and a methodology for training cybersecurity specialists are formulated. Based on the analysis the particular tasks of developing convolutional neural network model, information technology methods and models for ensuring the cybersecurity of web-oriented systems and networks, a methodological framework of creating information technology, and a model of digital knowledge platform for use in the field of cybersecurity specialists training and security of industrial systems were formulated. The basic theoretical decisions, which underlie the construction of real industrial and web-oriented systems and networks, were described in the article. The results of work are increased reliability value of detecting the malware in the Android operating system, reduced rates of false positives, provided an allowable value of the success rate of attacks at a minimum cost, reduced time spent on building and rebuilding the structure of the virtual network, increased efficiency of cybersecurity specialists training and security of industrial systems. From the above, it is possible to conclude that the obtained results can be used in a line of existing and prospective approaches at designing difficult, complex, hybrid, technical, cyber-physical systems with a web-oriented interface for users and administrators.

Alignment of safety and security risk assessments for modular production systems

In order to ensure the safety and security of industrial systems with regard to all life cycle phases from development through operation to disposal, specific regulatory and normative requirements are imposed. Due to the digitalization, interconnection, and constantly increasing complexity of manufacturing systems in the context of Industrie 4.0, the manual effort necessary to achieve the required safety and security is becoming ever greater and almost impossible to manage, especially for small and medium-sized enterprises. Therefore, this paper examines the existing challenges in this area in more detail and gives an outlook on the possible solutions to ensure safety and security much quicker and with less manual effort. The overall vision is a (partially) automated risk assessment of modular systems with respect to safety and security, including the alignment of the corresponding processes from both domains and the formalization of the information models needed.

La vigilancia científica como apoyo a la gestión educativa. Un análisis de la formación en Automatización Industrial.

This research presents an innovative methodology, which allows the articulation of different participants in the academic community to transform processes in educational institutions. It uses a bibliometric analysis of scientific literature, supported by expert judgments. To present it, the field of Industrial Automation was analyzed by means of recognized software for bibliometric analysis (Bibliometrix de R) and analysis of Vosviewer, Sci2 and gephi networks. The allowed us to identify the main authors, journals, countries, institutions and relevant documents, in addition to trends in this field. Based on the findings, it is concluded that it is necessary to update the curricular designs and create new programs related to industrial automation. It also provides relevant information to update the training plan for teachers on issues related to wireless sensors and security of industrial systems. The research also provides teachers with an overview of the past, present and future of industrial automation, which will allow them to improve their educational practice.

Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions

Motor fault diagnosis is imperative to enhance the reliability and security of industrial systems. However, since motors are often operated under nonstationary conditions, the high complexity of vibration signals raises notable difficulties for fault diagnosis. Therefore, considering the special physical characteristics of motor signals under nonstationary conditions, in this article, we propose a multiscale kernel based residual convolutional neural network (CNN) for motor fault diagnosis. Our contributions mainly fall into two aspects. First, we notice that each motor fault category has various patterns in vibration signals due to the changing operational conditions of the motor. To capture these patterns, a multiscale kernel algorithm is applied in the CNN architecture. Second, since the motor vibration signals are made up of many different components from different transfer paths, they are very complex and variable. To enable the architecture to extract fault features from deep and hierarchical representation spaces, sufficient depth of the network is needed, which will lead to the degradation problem. In the proposed method, residual learning is embedded into the multiscale kernel CNN to avoid performance degradation and build a deeper network. To validate the effectiveness of the proposed networks, a normal motor and five motors with different failures are tested. The results and comparisons with state-of-the-art methods highlight the superiority of the proposed method.

Recent Development in Big Data Analytics for Business Operations and Risk Management.

"Big data" is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.