Security Framework for Identifying threats in Smart Manufacturing Systems Using STRIDE Approach

The purpose of this paper is to collect and structure the various features of Smart Manufacturing (SM). Researchers have previously identified various characteristics and technologies of Smart Manufacturing System (SMS); this paper collects, discusses and merges some of those characteristics and technologies available in the current body of knowledge. In the future, it is expected that this selection of characteristics and technologies will help to compare and distinguish other initiatives like Industry 4.0, smart factory, intelligent manufacturing, distributive manufacturing, etc. which are frequently used synonymous with SM. The result of this paper is a comprehensive list of characteristics and technologies that are associated with a SMS. As many of the listed items show variating overlaps, certain technologies and characteristics are merged and clustered. This results in a set of five defining characteristics and ten technologies that are considered relevant for a SMS. The authors hope to provide a basis for a broad and interdisciplinary discussion within the SM community about the defining technologies and characteristics of a SMS.

Threat modeling is a critical proactive security technique for identifying threats and determining mitigations. However, traditional approaches often fall short for Industrial Control Systems (ICS), which automate operations in domains like manufacturing and energy and are a subset of Cyber-Physical Systems (CPS). CPS integrates computation, networking, and physical processes, with ICS requiring specialized cybersecurity approaches due to its operational and safety-critical nature. This study explores the use of digital twin technology as a promising cybersecurity tool for ICS, enabling testing and analysis without disrupting operations. By examining the capabilities of digital twins in analysis, simulation, and replication, the research evaluates their potential to enhance threat modeling across the CPS life-cycle. Insights from the European Cyber Security Organisation (ECSO) Technical Paper on Cybersecurity Scenarios and Digital Twins guide the exploration of their role in threat modeling. The study addresses four research questions: 1. What purposes do digital twins serve in cybersecurity? 2. What benefits do digital twins offer in cybersecurity? 3. How can digital twin technology be leveraged for threat modeling? 4. What advantages can the use of digital twins bring to threat modeling? Our findings reveal that digital twins enhance ICS threat modeling by enabling continuous, dynamic, and autonomous assessment, offering valuable insights for advancing cybersecurity strategies in ICS, CPS, and related domains.

This article comprehensively analyzes artificial intelligence (AI) implementation in modern manufacturing systems, focusing on smart factories and predictive maintenance applications. The article explores how AI-driven technologies transform traditional manufacturing processes through advanced data analytics, machine learning algorithms, and Internet of Things (IoT) integration. By examining the evolution of smart factory systems, this study investigates the role of predictive maintenance in reducing operational downtime and optimizing equipment performance. The article analysis encompasses real-time monitoring systems, automated quality control processes, and intelligent production scheduling, highlighting their collective impact on manufacturing efficiency. Through detailed case studies and empirical evidence, the article demonstrates how AI-powered solutions enhance decision-making capabilities, improve product quality, and streamline production workflows in manufacturing environments. The findings reveal significant improvements in operational efficiency, maintenance scheduling, and resource utilization across various manufacturing sectors. This article contributes to the growing knowledge of Industry 4.0 technologies and provides valuable insights for manufacturers seeking to implement AI-driven solutions in their operations. The article also addresses implementation challenges and offers strategic recommendations for successful AI integration in manufacturing processes, paving the way for future developments in smart manufacturing systems.

Computing systems face diverse and substantial cybersecurity threats.To mitigate these cybersecurity threats while developing software, engineers need to be competent in the skill of threat modeling.In industry and academia, there are many frameworks for teaching threat modeling, but our analysis of these frameworks suggests that (1) these approaches tend to be focused on component-level analysis rather than educating students to reason holistically about a system's cybersecurity, and (2) there is no rubric for assessing a student's threat modeling competency.To address these concerns, we propose using systems thinking in conjunction with popular and industry-standard threat modeling frameworks like STRIDE for teaching and assessing threat modeling competency.Prior studies suggest a holistic approach like systems thinking can be suitable for understanding and mitigating cybersecurity threats.Therefore, the purpose of this study is to develop and pilot two novel rubricsone for assessing STRIDE threat modeling performance and the other for assessing systems thinking performance while conducting STRIDE.To conduct this study, we piloted the two rubrics mentioned above to assess threat model artifacts of students enrolled in an upper-level software engineering course at Purdue University in Fall 2021, Spring 2023, and Fall 2023.Our results reveal that students who had both systems thinking and STRIDE instruction identified and attempted to mitigate component-level as well as systems-level threats.On the other hand, students with only STRIDE instruction tended to focus on identifying and mitigating component-level threats and discounted system-level threats.Our work contributes to the engineering education community by: (1) describing a new rubric for assessing threat modeling based on systems thinking; (2) identifying trends and blindspots in students' threat modeling approach; and (3) envisioning the benefits of integrating systems thinking in threat modeling teaching and assessment.

At the current pace of technological advancement, it is evident that IoT (Internet of Things) devices are becoming an inevitable part of daily life. Since the growth rate is exceptional, this situation has led to an alarming rise in concern for security threats and issues in the IoT devices. The purpose of this study is to introduce a novel and well-structured threat-modeling approach which is specifically tailored for IoT devices. Traditional threat models like STRIDE, LINDDUN, CORAS, etc., are effective in software applications. However, these models do not cover every aspect of threats specific to IoT devices. The proposed approach consists of a seven-step threat-modeling process. This methodology also addresses aspects of IoT devices which directly affect the user: Privacy threats; Safety threats; and Malfunction threats (PSM). The mentioned threats cannot be addressed effectively with existing traditional threat-modeling approaches. The proposed threat modeling has been applied to three IoT applications: IoT-based smart home environments; IoT-based garment units; and IoT-based water quality management systems, in order to demonstrate the work’s effectiveness. It was seen that when coupling the proposed methodology with the PSM technique and existing threat modeling methods, threats directly affecting users were also identified. This method increases the performance and effectiveness of threat modeling, which leads to increased mitigation of the identified threats. This also leads to an increase in the security of the devices and environment. The final part of the study presents practical mitigation techniques to eliminate threats in IoT devices.

The Cybersecurity Body of Knowledge explains the content, purpose, and use of eight knowledge areas that define the boundaries of the discipline of cybersecurity. The discussion focuses on, and is driven by, the essential concepts of each knowledge area that collectively capture the cybersecurity body of knowledge to provide a complete picture of the field. This book is based on a brand-new and up to this point unique, global initiative, known as CSEC2017, which was created and endorsed by ACM, IEEE-CS, AIS SIGSEC, and IFIP WG 11.8. This has practical relevance to every educator in the discipline of cybersecurity. Because the specifics of this body of knowledge cannot be imparted in a single text, the authors provide the necessary comprehensive overview. In essence, this is the entry-level survey of the comprehensive field of cybersecurity. It will serve as the roadmap for individuals to later drill down into a specific area of interest. This presentation is also explicitly designed to aid faculty members, administrators, CISOs, policy makers, and stakeholders involved with cybersecurity workforce development initiatives. The book is oriented toward practical application of a computing-based foundation, crosscutting concepts, and essential knowledge and skills of the cybersecurity discipline to meet workforce demands. Dan Shoemaker, PhD, is full professor, senior research scientist, and program director at the University of Detroit Mercy's Center for Cyber Security and Intelligence Studies. Dan is a former chair of the Cybersecurity & Information Systems Department and has authored numerous books and journal articles focused on cybersecurity. Anne Kohnke, PhD, is an associate professor of cybersecurity and the principle investigator of the Center for Academic Excellence in Cyber Defence at the University of Detroit Mercy. Anne's research is focused in cybersecurity, risk management, threat modeling, and mitigating attack vectors. Ken Sigler, MS, is a faculty member of the Computer Information Systems (CIS) program at the Auburn Hills campus of Oakland Community College in Michigan. Ken's research is in the areas of software management, software assurance, and cybersecurity.

To identify and manage their cyber security risks, organisations need to form a thorough understanding of various factors that may expose them to these risks. While cyber security professionals and scholars have developed a plethora of practical methodologies and frameworks to support cyber security risk identification and mitigation, the theoretical foundations on what promotes effective knowledge creation when using these methodologies and frameworks are nascent. Yet, theories developed in the field of knowledge management and intellectual capital may provide valuable insight on how to enhance cyber security risk related knowledge creation in organisations. For example, social capital is considered as an important prerequisite for knowledge exchange and combination when creating new intellectual capital (Nahapiet & Ghoshal, 1998). However, more focused research is required to understand how social capital affects knowledge creation in the context of organisational cyber security risk related activities. Using qualitative data gathered from three cyber security threat modelling workshops, this paper examines how social capital enables conditions for exchanging and combining knowledge on cyber security threats. By comparing the empirical observations with Nahapiet and Ghoshal’s (1998) model, this study identifies practical approaches that are used by threat modelling workshop facilitators to create conditions for effective knowledge exchange and combination. This study provides both cyber security scholars and professionals with an example on how to use knowledge creation related academic theories to analyse and further enhance cyber security risk management approaches by creating a connection between Nahapiet and Ghoshal’s (1998) social capital model and cyber security threat modelling.

The Air Traffic Control industry is being increasingly exposed to rising levels of risk, as criminals and cyber‐attackers look to exploit system vulnerabilities. Air Navigation Service Providers become more demanding regarding cybersecurity concerns in the products they acquire. Consequently, systems engineers need to consider cyber security concerns early in their system's development life cycle. Model‐Based Systems Engineering methodologies are widely used to manage complex engineering projects in terms of system requirements, design, analysis, verification, and validation activities, leaving cyber security aspects aside. This paper presents a conceptual solution of a model‐based security method that aims to enable systems engineers to perform threat modeling analysis of cyber‐physical systems early and incorporate mitigation strategies into the system design, thereby reducing the cyber‐physical system's overall security‐related risks. Based on a real‐life case study the method will be validated later during execution period from Jan. – May 2022.

Today's business models compete on achieving speed of delivery to end customers. To support this, development teams emphasize automation and Secure DevOps as key enablers. The challenge is maintaining speed to support business needs when security activities like threat modeling require highly skilled individuals and detailed analysis. Conducting threat modeling on a per application basis for each iteration is too slow. For example, there are challenges with initial creation of data flow diagrams. Furthermore, application changes might occur several times a day which makes ongoing creation of data flow diagrams onerous. We propose use of a lightweight threat modeling approach which uses a correlation matrix created from common lists and application abstractions. The result is a lightweight threat modeling approach which is quicker and addresses many use cases where a detailed threat modeling approach is not necessary. Traditional data flow threat modeling can still be used for special cases thereby creating a dual velocity approach. From our experience with large organizations, this dual velocity approach lends itself better to scaling threat modeling, automation, and traceability in Secure DevOps.

The Department of the Defense has transitioned smart devices into the battlefield as a portable hand-held unmanned aerial vehicle ground control station without adequate cyber security protections, putting critical mission data at risk to cyber security attacks. Industry has developed software apps for smart phones and tablets that allows soldiers to not only pilot unmanned aerial vehicles (UAVs) but to share and receive intelligence and reconnaissance videos and images remotely from the ground control station (GCS) or directly from the UAV. The Department of Defense has not developed a secure communication network that will support a large quantity of smart devices, nor certification, standards or policies for operation of secure smart devices. Therefore, mission critical information will be shared through unsecured, mobile and wireless networks and through unclassified, unsecure smart technology that are vulnerable to cyber security risks. Lack of security of the mobile and wireless networks and smart devices could result in the unintentional sharing of data as well as loss of control of the UAV to enemies. The Department of Defense has failed to develop a threat model and risk assessment to identify the cyber security threats and ensure the proper security countermeasures are in place. This paper will analyze the cyber security vulnerabilities within the communication links, smart devices hardware, specifically smart phones and tablets, and software applications to develop a risk model of the threat profile of the GCS networking hub. This model will help designers and users of the military and civilian UAV communities to understand the threat profile of the GCS networking hub to develop a secure communication network based upon the vulnerabilities identified for smart phones and tablets.

In the process of designing and forming each system, it is necessary to identify potential vulnerabilities and threats to that system and to include appropriate countermeasures. The process that helps to find the problem in the first phase of design is called threat modeling. Threat modeling is based on the idea that every system has valuable resources that need to be protected. These resources have certain weak points that internal or external threats can use to harm them, while there are as well countermeasures used to mitigate them. Therefore, this paper analyses the security of a Web of Things (WoT)-based system for remote management of windows, which is in the design stage by using a threat modeling approach based on STRIDE and DREAD. The results obtained through Microsoft Threat Modeling Tool (MTMT) justified the use of threat modeling in the design phase given that we have identified in total 118 threats, with Elevation of privilege class of threats being the most prominent ones. The Information disclosure threats are found to be the ones characterized as medium and low risk ones, while the most represented high-risk threats again come from the Elevation of privilege class of threats.

Security and privacy are not only general requirements of a society but also indispensable enablers for innovative IT infrastructure applications aiming at increased, sustainable welfare and safety of a society. A critical activity of these IT applications is spontaneous information exchange. This information exchange, however, creates inevitable, unknown dependencies between the participating IT systems, which, in turn threaten security and privacy. With the current approach to IT security, security and privacy follow changes and incidents rather than anticipating them. By sticking to a given threat model, the current approach fails to consider vulnerabilities which arise during a spontaneous information exchange. With the goal of improving security and privacy, this work proposes adapting an IT security model and its enforcement to current and most probable incidents before they result in an unacceptable risk for the participating parties or failure of IT applications. Usage control is the suitable security policy model, since it allows changes during run-time without conceptually raising additional incidents.

Cyber security in a supply chain (SC) provides an organization the secure network facilities to meet its overall business objectives. The integration of technologies has improved business processes, increased production speed, and reduced distribution costs. However, the increased interdependencies among various supply chain stakeholders have brought many challenges including lack of third party audit mechanisms and cascading cyber threats. This has led to attacks such as the manipulation of the design specifications, alterations, and manipulation during distribution. The aim of this paper is to investigate and understand supply chain threats. In particular, the paper contributes towards modeling and analyzing CSC attacks and cyber threat reporting among supply chain stakeholders. We consider concepts such as goal, actor, attack, TTP, and threat actor relevant to the supply chain, threat model, and requirements domain, and modeled the attack using the widely known STIX threat model. The proposed model was analyzed using a running example of a smart grid case study and an algorithm to model the attack. A discrete probability method for calculating the conditional probabilities was used to determine the attack propagation and cascading effects, and the results showed that our approach effectively analyzed the threats. We have recommended a list of CSC controls to improve the overall security of the studied organization.

Security risk management can be applied on well-defined or existing systems; in this case, the objective is to identify existing vulnerabilities, assess the risks and provide for the adequate countermeasures. Security risk management can also be applied very early in the system's development life-cycle, when its architecture is still poorly defined; in this case, the objective is to positively influence the design work so as to produce a secure architecture from the start. The latter work is made difficult by the uncertainties on the architecture and the multiple round-trips required to keep the risk assessment study and the system architecture aligned. This is particularly true for very large projects running over many years. This paper addresses the issues raised by those risk assessment studies performed early in the system's development life-cycle. Based on industrial experience, it asserts that attack trees can help solve the human cognitive scalability issue related to securing those large, continuously-changing system-designs. However, big attack trees are difficult to build, and even more difficult to maintain. This paper therefore proposes a systematic approach to automate the construction and maintenance of such big attack trees, based on the system's operational and logical architectures, the system's traditional risk assessment study and a security knowledge database.

In the age of Industry 4.0, collaborative robots (cobots) have emerged as pivotal components of smart factories, revolutionizing manufacturing processes by enhancing productivity, flexibility, and safety. These robots, designed to work alongside human operators, have opened new frontiers in automation by enabling seamless human-robot collaboration. However, as cobots become increasingly integrated into interconnected systems within smart factories, the exposure to cyber threats also grows. The complexity and interdependence of the systems that cobots interact with ranging from machine controllers to industrial IoT (IIoT) devices create a fertile ground for potential cyberattacks. This chapter delves into the cybersecurity challenges associated with cobots in smart factories, exploring the unique vulnerabilities of these systems and the implications for industrial operations.