Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy

Honeypot-based Security Enhancements for Information Systems

The purpose of this thesis is to explore honeypot-based security enhancements for information systems. First, we provide a comprehensive survey of the research that has been carried out on honeypots and honeynets for Internet of Things (IoT), Industrial Internet of Things (IIoT), and Cyber-physical Systems (CPS). We provide a taxonomy and extensive analysis of the existing honeypots and honeynets, state key design factors for the state-of-the-art honeypot/honeynet research and outline open issues. Second, we propose S-Pot, a smart honeypot framework based on open-source resources. S-Pot uses enterprise and IoT honeypots to attract attackers, learns from attacks via ML classifiers, and dynamically configures the rules of SDN. Our performance evaluation of S-Pot in detecting attacks using various ML classifiers shows that it can detect attacks with 97% accuracy using J48 algorithm. Third, for securing host-based Docker containers from cryptojacking, using honeypots, we perform a forensic analysis to identify indicators for the detection of unauthorized cryptomining, present measures for securing them, and propose an approach for monitoring host-based Docker containers for cryptojacking detection. Our results reveal that host temperature, combined with container resource usage, Stratum protocol, keywords in DNS requests, and the use of the container’s ephemeral ports are notable indicators of possible unauthorized cryptomining

Smart Factories, Dumb Policy? Managing Cybersecurity and Data Privacy Risks in the Industrial Internet of Things

Interest is booming in the so-called Internet of Things (IoT). The Industrial Internet of Things (IIoT) is one application of this trend and involves the use of smart technologies in a manufac- turing context. Even though these applications hold the promise to revolutionize manufacturing, there are a number of outstand- ing cybersecurity and data privacy issues impacting the realiza- tion of the myriad benefits promised by IIoT proponents. This ar- ticle analyzes some of these pressing issues, focusing on: (1) critical infrastructure protection and cybersecurity due diligence, (2) trends in transatlantic data privacy protections, and (3) the regulation of new technologies like artificial intelligence (AI) and blockchain. The aticle concludes with a list of recommendations for state and federal policymakers to consider in an effort to harden the IIoT along with the supply chains critical to the con- tinued development of smart factories

Towards trustworthy end-to-end communication in industry 4.0

Industry 4.0 considers integration of IT and control systems with physical objects, software, sensors and connectivity in order to optimize manufacturing processes. It provides advanced functionalities in control and communication for an infrastructure that handles multiple tasks in various locations automatically. Automatic actions require information from trustworthy sources. Thus, this work is focused on how to ensure trustworthy communication from the edge devices to the backend infrastructure. We derive a meta-model based on RAMI 4.0, which is used to describe an end-to-end communication use case for an Industry 4.0 application scenario and to identify dependabilities in case of security challenges. Furthermore, we evaluate secure messaging protocols and the integration of Trusted Platform Module (TPM) as a root of trust for dataexchange. We define a set of representative measurable indicator points based on existing standards and use them for automated dependability detection within the whole system

Digital Transformation in the Ornamental Stone Industry: Case Studies on Industry 4.0 and Digital Twins

Funding program "LISBOA-01-0247-FEDER-046083" for this R&D scholarship.The rapid evolution of Industry 4.0 technologies has ushered in a new era in manufacturing systems, with Digital Twins leading the way. These virtual replicas offer invaluable opportunities for simulating and optimizing new manufacturing processes, and their most transformative impact may lie in the creation of these digital models. This research unifies the main key concepts of four separate studies, all of which explore the application of Digital Twins in the ornamental stone industry. Industry 4.0 systems and their technologies have directly influenced the ornamental stone industry, addressing both the effects on mineral resources and energy consumption in daily operations. In addition, research and development initiatives seek to make this industry more efficient and sustainable, addressing crucial issues such as economic growth, environmental impact, and social welfare. The increasing digitization of manufacturing systems and their integration with digital models has played a key role in this process, enabling the replication of shop floor operations and the optimization of material use. The application of Digital Twins, which are virtual replicas of physical systems, has been explored in an ornamental stone manufacturing company. These digital models have demonstrated the ability to save time and resources during prototype design, as well as offering continuous diagnostics and optimization throughout production. It is important to note that the implementation of Digital Twins requires care due to technical challenges, but their adoption promises to significantly impact business value, despite the initial complexities. Managing stone cutting devices with Digital Twins presents real challenges in the ornamental stone industry, but it also paves the way for greater precision, efficiency, and cost savings. These digital models enable real-time monitoring, predictive maintenance, and virtual simulations. This study explores different approaches to connecting physical cutting machines to their respective Digital Twins, evaluating criteria such as communication speed, security, scalability, and cost. The results of this analysis provide valuable information for implementing Digital Twins in the stone cutting industry

Technology-Enabled Medical IoT System for Drug Management

This study introduces an innovative framework for the storage and administration of pharmaceuticals, which effectively tackles the pressing requirements of maintaining optimal temperature and humidity conditions, monitoring medicine inventory, and processing real-time data in healthcare establishments. By utilizing a comprehensive network of Internet of Things (IoT) sensors strategically positioned within pharmaceutical storage facilities, our technology effectively guarantees the preservation and security of stored drugs. The study conducted in our research demonstrates that low temperature fluctuation effectively protects medicinal substances, hence reducing potential dangers to patients. The real-time inventory management system effectively optimizes medicine control by following expiry criteria and minimizing wasted spending. Furthermore, our study emphasizes the importance of cloud response latency, as the average data transfer time is a rapid 100 milliseconds. The expeditious integration of crucial data enables prompt notifications and alerts, hence augmenting the quality and safety of pharmaceutical products

Unlocking value from machines: business models and the industrial internet of things

In this article we argue that the Industrial Internet of Things (IIoT) offers new opportunities and harbors threats that companies are not able to address with existing business models. Entrepreneurship and Transaction Cost Theories are used to explore the conditions for designing nonownership business models for the emerging IIoT with its implications for sharing uncertain opportunities and downsides, and for transforming these uncertainties into business opportunities. Nonownership contracts are introduced as the basis for business model design and are proposed as an architecture for the productive sharing of uncertainties in IIoT manufacturing networks. The following three main types of IIoT-enabled business models were identified: (1) Provision of manufacturing assets, maintenance and repair, and their operation, (2) innovative information and analytical services that help manufacturing (e.g., based on artificial intelligence, big data, and analytics), and (3) new services targeted at end-users (e.g., offering efficient customization by integrating end-users into the manufacturing and supply chain ecosystem)