Assessing and augmenting SCADA cyber security: a survey of techniques

SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability

Daydreaming factories

Optimisation of factories, a cornerstone of production engineering for the past half century, relies on formulating the challenges with limited degrees of freedom. In this paper, technological advances are reviewed to propose a “daydreaming” framework for factories that use their cognitive capacity for looking into the future or “foresighting”. Assessing and learning from the possible eventualities enable breakthroughs with many degrees of freedom and make daydreaming factories antifragile. In these factories with augmented and reciprocal learning and foresighting processes, revolutionary reactions to external and internal stimuli are unnecessary and industrial co-evolution of people, processes and products will replace industrial revolutions

The co-incident flow of work pieces and cutting tools in a restricted category of flexible machining cells

The work reported in this thesis describes research carried out into the detailed design and operation of Flexible Machining Cells (FMC) incorporating automated work and tool flow, dual flow. Three modes of cell management are considered for dual flow cells, where the author examines both their operational and economic performance. A framework is defined for investigating these dual flow cells, and a structured approach providing a novel and detailed modelling capability is described. The question of how this approach compares to single flow modelling and the additional or alternative requirements for dual flow modelling is examined via the following key areas; the specification of material handling requirements, tool transportation and issue and finally, the control required to examine the interaction between the two flows operating concurrently. The framework is tested for its industrial applicability via an industrial case study. A major aim of this study is to examine the view that a hybrid cell management strategy, competitive management, could outperform the other strategies examined. The aim of this methodology is to provide a solution for the control of FMCs. Emphasis is placed on the ease of control and how the loading and control rules selection can maximise economic enhancement of a cells performance

Invest to Save: Report and Recommendations of the NSF-DELOS Working Group on Digital Archiving and Preservation

Digital archiving and preservation are important areas for research and development, but there is no agreed upon set of priorities or coherent plan for research in this area. Research projects in this area tend to be small and driven by particular institutional problems or concerns. As a consequence, proposed solutions from experimental projects and prototypes tend not to scale to millions of digital objects, nor do the results from disparate projects readily build on each other. It is also unclear whether it is worthwhile to seek general solutions or whether different strategies are needed for different types of digital objects and collections. The lack of coordination in both research and development means that there are some areas where researchers are reinventing the wheel while other areas are neglected. Digital archiving and preservation is an area that will benefit from an exercise in analysis, priority setting, and planning for future research. The WG aims to survey current research activities, identify gaps, and develop a white paper proposing future research directions in the area of digital preservation. Some of the potential areas for research include repository architectures and inter-operability among digital archives; automated tools for capture, ingest, and normalization of digital objects; and harmonization of preservation formats and metadata. There can also be opportunities for development of commercial products in the areas of mass storage systems, repositories and repository management systems, and data management software and tools.

Integrated Real-Virtuality System and Environments for Advanced Control System Developers and Machines Builders

The pace of technological change is increasing and sophisticated customer driven markets are forcing rapid machine evolution, increasing complexity and quality, and faster response. To survive and thrive in these markets, machine builders/suppliers require absolute customer and market orientation, focusing on .. rapid provision of solutions rather than products. Their production systems will need to accommodate unpredictable changes while maintaining financial and operational efficiency with short lead and delivery times. Real-Virtuality (R-V) systems are an innovative environment to address these requirements by facilitating enhanced support in machine system design utilising integrated real-virtual environments centred on concurrent machine system development and realization. This environment supports not only machine system design but also the development of the' control system at the same time. Utilising the Real-Virtual Mapping Environment (RVMI;:), 3-D simulation machine models can perform actual machine operations in real-time when coupled with the real machine controller. This provides a more understandable, reliable and transparent machine function and performance. The research study explores different types of controller verification methods and proposes a new method which employs the use of a control signal emulator. The research study has fomulated a novel technique for emulating quadrature encoder signals to provide virtual closed loop control of servomotors. The deployment of a control signal emulator technique makes the system unique and removes its dependency on specific hardware. Enabling the real-time data from the signal emulation environment eases the task of realising a real-time machine simulator. To evaluate the proposed architecture, three case studies were performed. The results have shown that it is possible to create verified and validated machine control programs with no modification needed when applied to the real machine. The migration from the virtual to the real world is totally seamless. The result from the ????study show that the virtual machine is able to operate and respond as a real machine in real-time. This opens up the unexplored potential of integrated 3-D virtual technology. The real-time 3-D simulation virtual machine will enable commissioning and training to be conducted '!-t an earlier stage in the design process (without having to wait for the real machine to be built). Furthermore, various test scenarios can also be developed and tested on the system which helps to provide a better lofriderstanding of the machine behaviours and responses. This research study has made an original contribution in the field of machine system development. It has contributed a novel approach of using emulated control signals to provide machine control programmers with a platform to test their application programs at machine level which involves both discrete digital signals and continuous signals. The real-time virtual environment extends the application domain for the use of simulation. The architecture proposed is generic; to be exact it is not constrained to a specific industrial control system or to a specific simulation vendor

Developing concepts for improved efficiency of robot work preparation

SInBot[1] is a large research project that focuses on maximizing the efficient use of mobile industrial robots during medium sized production runs. The system that will be described in this paper will focusses on the development and validation of concepts for efficient work preparation for cells of intelligent mobile robots that execute medium sized production runs. For a wide range of products, the machining tasks will be defined on an appropriate level, enabling control over the robots behavior and performance. When the system, system operator, and robots have more experience with a product the system can be controlled on a higher level (i.e. the subsystems or even robots can start allocating and executing tasks by themselves). Different test beds are used to test the diversity of aspects involved in the development of the SInBot system. The initial test bed used for this research is a combination of two Lynxmotion AL5D robots and a Samsung SUR40 multi-touch environment. In this paper, novel work preparation concepts will be described and an experiment setup is proposed to validate the model for definition and generation of tasks from a CAD file

Continuous adaption through real data analysis turn simulation models into digital twins

Digital twins of production systems enable new forms of production control, flexibility and continuous improvement. While off-the-shelf software for discrete-event simulation permits the fast implementation of rough simulation models with sufficient accuracy for project-based analysis, they lack the precision and generality of a digital twin. This paper presents an approach to close the gap between model and reality by continuous and iterative updates enabled by connecting the simulation model to IT systems and smart data analysis. However, handling different databases requires a generative and flexible modelling approach as well as suitable algorithms for probability distribution estimation and control logic identification. The presented approach was validated at a real world example from the automotive industry where an average deviation of output to reality per week of 0.1% was achieved, proving the effectiveness of the approach

Innovative Computational Methods for Pharmaceutical Problem Solving a Review Part I: The Drug Development Process

Computational methods have provided pharmaceutical scientists and engineers a means to go beyond what\u27s possible with experimental testing alone. Providing a means to study active pharmaceutical ingredients (API), excipients, and drug interactions at or near-atomic levels. This paper provides a review of this and other innovative computational methods used for solving pharmaceutical problems throughout the drug development process. Part one of two this paper will emphasize the role of computational methods and game theory in solving pharmaceutical challenges

Parallel computing for brain simulation

[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

A Survey on Industrial Control System Testbeds and Datasets for Security Research

The increasing digitization and interconnection of legacy Industrial Control Systems (ICSs) open new vulnerability surfaces, exposing such systems to malicious attackers. Furthermore, since ICSs are often employed in critical infrastructures (e.g., nuclear plants) and manufacturing companies (e.g., chemical industries), attacks can lead to devastating physical damages. In dealing with this security requirement, the research community focuses on developing new security mechanisms such as Intrusion Detection Systems (IDSs), facilitated by leveraging modern machine learning techniques. However, these algorithms require a testing platform and a considerable amount of data to be trained and tested accurately. To satisfy this prerequisite, Academia, Industry, and Government are increasingly proposing testbed (i.e., scaled-down versions of ICSs or simulations) to test the performances of the IDSs. Furthermore, to enable researchers to cross-validate security systems (e.g., security-by-design concepts or anomaly detectors), several datasets have been collected from testbeds and shared with the community. In this paper, we provide a deep and comprehensive overview of ICSs, presenting the architecture design, the employed devices, and the security protocols implemented. We then collect, compare, and describe testbeds and datasets in the literature, highlighting key challenges and design guidelines to keep in mind in the design phases. Furthermore, we enrich our work by reporting the best performing IDS algorithms tested on every dataset to create a baseline in state of the art for this field. Finally, driven by knowledge accumulated during this survey's development, we report advice and good practices on the development, the choice, and the utilization of testbeds, datasets, and IDSs