Cybersecurity issues in motion control – an overview of challenges

The fourth industrial revolution known as Industry 4.0 brings digitalization of manufacturing processes to a new level through ubiquitous interconnection and real-time information flow between information technologies (IT) and operational technologies (OT) as parts of Industrial Control Systems (ICS). This information flow is not limited to but expands beyond factory walls enabling manufacturing systems to adapt quickly and efficiently to changing customer demands and diversified products. The adaptation is carried out through physical and/or functional reconfiguration of manufacturing systems where Industrial Internet of Things (IIoT) based on Cyber-Physical Systems (CPS) represents the key technical enabler. These changes result in a transition from centralized to distributed control systems architecture where the whole control task is achieved through intensive cooperation between smart devices (e.g., sensors and actuators) with integrated communication and computation capabilities. However, introducing IIoT in ICS brings about new cybersecurity issues due to increased communication between system elements and connection to the global network, making ICS vulnerable to different cyber-attacks with potentially catastrophic consequences. Recently, the research in ICS cybersecurity has intensified leading to significant results for continuous time and discrete events-controlled systems. However, cybersecurity issues in motion control systems that are frequently employed in different manufacturing resources such as machine tools and industrial robots were not sufficiently explored. This work provides an overview of the cybersecurity related challenges in motion control tasks

Using machine learning algorithm for detection of cyber-attacks in cyber physical systems

Network integration is common in cyber-physical systems (CPS) to allow for remote access, surveillance, and analysis. They have been exposed to cyberattacks because of their integration with an insecure network. In the event of a violation in internet security, an attacker was able to interfere with the system's functions, which might result in catastrophic consequences. As a result, detecting breaches into mission-critical CPS is a top priority. Detecting assaults on CPSs, which are increasingly being targeted by cyber criminals and cyber threats, is becoming increasingly difficult. Machine Learning (ML) and Artificial Intelligence (AI) have the potential to make these the worst of moments, but it may also be the finest of times. There are a variety of ways in which AI technology can aid in the growth and profitability of a variety of industries. Such data can be parsed using ML and AI approaches in designed to check attacks on CPSs. Hence, in this paper, we propose a novel cyberattack detection framework by integrating AI and ML (ML) methods. Here, initially we collect the dataset from the CPS database and preprocess the data using normalization for removal of errors and redundant data. The features are extracted using Linear Discriminant Analysis (LDA). We have proposed Self-tuned Fuzzy Logic-based Hidden Markov Model (SFL-HMM) with Heuristic Multi-Swarm Optimization (HMS-ACO) algorithm for detection of the cyberattacks. The proposed method is evaluated using the MATLAB simulation tool and the metrics are compared with existing approaches. The results of the experiments reveal that the framework is more successful than traditional strategies in achieving high degrees of privacy. Furthermore, in terms of detection rate, false positive rate, and computing time, the framework beats traditional detection algorithms

Theoretical framework of the Industry 4.0 risks from sustainability perspective

Industry 4.0 is a topic little discussed today, especially in relation to the possible negative risks generated by it. In this way, this work aims to raise and discuss the risks of the Fourth Industrial Revolution currently found in the literature from a sustainability perspective and develop a theoretical framework to represent them. For this, a methodology of systematic analysis of the literature was used to relate the relevant works to the theme and thus to discuss them. Two databases (Scopus and Web of Science) were used in which 7772 articles were evaluated, of which 66 were used for the discussion. The 28 risks found were grouped into four dimensions (Economic Risks, Social Risks, Environmental Risks, and Technological Risks) where their relationships were studied and represent in the theoretical framework constructed. In this way, in addition to contributing to the academy building more theoretical contribution to the theme, the risks raised can help managers and companies to checkpoints of attention before implanting technologies and concepts of industry 4.0.

UM ESTUDO QUANTITATIVO SOBRE OS RISCOS DA INDÚSTRIA 4.0 NO CONTEXTO INDUSTRIAL: UMA REVISÃO SISTEMÁTICA DA LITERATURA

RESUMOA Indústria 4.0 é um tema em ascensão tanto no meio acadêmico como no contexto prático de empresas. As pesquisas atuais focam seus esforços nos estudos dos benefícios dos conceitos e tecnologias envolvidos no conceito da Quarta Revolução Industrial, no entanto, poucas procuram entender quais os pontos negativos. Dessa forma, através de uma revisão bibliográfica sistematizada realizada em janeiro de 2019, esse estudo procura entender quais são riscos em evidência na literatura que empresas do setor industrial podem enfrentar no contexto da Indústria 4.0. Foram selecionados 66 artigos através das bases de dados Scopus e Web of Science, os quais foram bibliometricamente estudados em relação aos autores e periódicos mais relevantes. Em seguida, estudando os 28 riscos mapeados na literatura por Soltovski et al. (2019), foi possível entender quais são mais representativos na literatura na perspectiva da sustentabilidade (ambiental, econômico e social) e na perspectiva tecnológica. De forma geral, descobrimos que os riscos relacionados à conectividade, como cyber atacks e vazamento de dados privados através de tecnologias, como Internet das Coisas, são os mais discutidos na literatura. Além disso, constatamos que existe carência de estudos na perspectiva ambiental e econômica. Por fim, foram sumarizadas contribuições gerenciais e potenciais direcionamentos para futuros pesquisadores.Palavras-chave: Indústria 4.0. Sustentabilidade. Gestão de Risco. ABSTRACTIndustry 4.0 is a growing topic, both in academia and in the practical context of companies. Current research focuses its efforts on studies of benefits and technologies involved in the concept of the Fourth Industrial Revolution, however, they rarely understand what the negative points are. Thus, through a systematic literature review conducted in January 2019, this study can understand what are the risks in evidence in the literature that companies in the industrial sector may face in the context of Industry 4.0. 66 articles were selected on the Scopus and Web of Science databases, which are the bibliometrically studied in relation to the most relevant authors and journals. Then, studying the 28 risks mapped in the literature by Soltovski et al. (2019), it was possible to understand which are the most representative in the literature from the perspective of sustainability (environmental, economic and social) and from a technological perspective. In general, we found that risks related to connectivity such as cyber attacks and data leakage are using technologies like the Internet of Things that are most discussed in the literature. In addition, we note that there is a lack of studies from an environmental and economic perspective. Finally, managerial contributions were summarized and directed to future researchers.Keywords: Industry 4.0. Sustainability. Risk Management

Graph-Theoretic Approach for Manufacturing Cybersecurity Risk Modeling and Assessment

Identifying, analyzing, and evaluating cybersecurity risks are essential to assess the vulnerabilities of modern manufacturing infrastructures and to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. In response, this work proposes a graph-theoretic approach for risk modeling and assessment to address the lack of quantitative cybersecurity risk assessment frameworks for smart manufacturing systems. In doing so, first, threat attributes are represented using an attack graphical model derived from manufacturing cyberattack taxonomies. Attack taxonomies offer consistent structures to categorize threat attributes, and the graphical approach helps model their interdependence. Second, the graphs are analyzed to explore how threat events can propagate through the manufacturing value chain and identify the manufacturing assets that threat actors can access and compromise during a threat event. Third, the proposed method identifies the attack path that maximizes the likelihood of success and minimizes the attack detection probability, and then computes the associated cybersecurity risk. Finally, the proposed risk modeling and assessment framework is demonstrated via an interconnected smart manufacturing system illustrative example. Using the proposed approach, practitioners can identify critical connections and manufacturing assets requiring prioritized security controls and develop and deploy appropriate defense measures accordingly.Comment: 25 pages, 10 figure

Process monitoring for material extrusion additive manufacturing: a state-of-the-art review

Qualitative uncertainties are a key challenge for the further industrialization of additive manufacturing. To solve this challenge, methods for measuring the process states and properties of parts during additive manufacturing are essential. The subject of this review is in-situ process monitoring for material extrusion additive manufacturing. The objectives are, first, to quantify the research activity on this topic, second, to analyze the utilized technologies, and finally, to identify research gaps. Various databases were systematically searched for relevant publications and a total of 221 publications were analyzed in detail. The study demonstrated that the research activity in this field has been gaining importance. Numerous sensor technologies and analysis algorithms have been identified. Nonetheless, research gaps exist in topics such as optimized monitoring systems for industrial material extrusion facilities, inspection capabilities for additional quality characteristics, and standardization aspects. This literature review is the first to address process monitoring for material extrusion using a systematic and comprehensive approach

Intrusion Detection for Cyber-Physical Attacks in Cyber-Manufacturing System

In the vision of Cyber-Manufacturing System (CMS) , the physical components such as products, machines, and tools are connected, identifiable and can communicate via the industrial network and the Internet. This integration of connectivity enables manufacturing systems access to computational resources, such as cloud computing, digital twin, and blockchain. The connected manufacturing systems are expected to be more efficient, sustainable and cost-effective. However, the extensive connectivity also increases the vulnerability of physical components. The attack surface of a connected manufacturing environment is greatly enlarged. Machines, products and tools could be targeted by cyber-physical attacks via the network. Among many emerging security concerns, this research focuses on the intrusion detection of cyber-physical attacks. The Intrusion Detection System (IDS) is used to monitor cyber-attacks in the computer security domain. For cyber-physical attacks, however, there is limited work. Currently, the IDS cannot effectively address cyber-physical attacks in manufacturing system: (i) the IDS takes time to reveal true alarms, sometimes over months; (ii) manufacturing production life-cycle is shorter than the detection period, which can cause physical consequences such as defective products and equipment damage; (iii) the increasing complexity of network will also make the detection period even longer. This gap leaves the cyber-physical attacks in manufacturing to cause issues like over-wearing, breakage, defects or any other changes that the original design didn’t intend. A review on the history of cyber-physical attacks, and available detection methods are presented. The detection methods are reviewed in terms of intrusion detection algorithms, and alert correlation methods. The attacks are further broken down into a taxonomy covering four dimensions with over thirty attack scenarios to comprehensively study and simulate cyber-physical attacks. A new intrusion detection and correlation method was proposed to address the cyber-physical attacks in CMS. The detection method incorporates IDS software in cyber domain and machine learning analysis in physical domain. The correlation relies on a new similarity-based cyber-physical alert correlation method. Four experimental case studies were used to validate the proposed method. Each case study focused on different aspects of correlation method performance. The experiments were conducted on a security-oriented manufacturing testbed established for this research at Syracuse University. The results showed the proposed intrusion detection and alert correlation method can effectively disclose unknown attack, known attack and attack interference that causes false alarms. In case study one, the alarm reduction rate reached 99.1%, with improvement of detection accuracy from 49.6% to 100%. The case studies also proved the proposed method can mitigate false alarms, detect attacks on multiple machines, and attacks from the supply chain. This work contributes to the security domain in cyber-physical manufacturing systems, with the focus on intrusion detection. The dataset collected during the experiments has been shared with the research community. The alert correlation methodology also contributes to cyber-physical systems, such as smart grid and connected vehicles, which requires enhanced security protection in today’s connected world