| 1. |
- Jiang, Yuning, 1993-
(författare)
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Vulnerability Analysis for Critical Infrastructures
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The rapid advances in information and communication technology enable a shift from diverse systems empowered mainly by either hardware or software to cyber-physical systems (CPSs) that are driving Critical infrastructures (CIs), such as energy and manufacturing systems. However, alongside the expected enhancements in efficiency and reliability, the induced connectivity exposes these CIs to cyberattacks exemplified by Stuxnet and WannaCry ransomware cyber incidents. Therefore, the need to improve cybersecurity expectations of CIs through vulnerability assessments cannot be overstated. Yet, CI cybersecurity has intrinsic challenges due to the convergence of information technology (IT) and operational technology (OT) as well as the crosslayer dependencies that are inherent to CPS based CIs. Different IT and OT security terminologies also lead to ambiguities induced by knowledge gaps in CI cybersecurity. Moreover, current vulnerability-assessment processes in CIs are mostly subjective and human-centered. The imprecise nature of manual vulnerability assessment operations and the massive volume of data cause an unbearable burden for security analysts. Latest advances in machine-learning (ML) based cybersecurity solutions promise to shift such burden onto digital alternatives. Nevertheless, the heterogeneity, diversity and information gaps in existing vulnerability data repositories hamper accurate assessments anticipated by these ML-based approaches. Therefore, a comprehensive approach is envisioned in this thesis to unleash the power of ML advances while still involving human operators in assessing cybersecurity vulnerabilities within deployed CI networks.Specifically, this thesis proposes data-driven cybersecurity indicators to bridge vulnerability management gaps induced by ad-hoc and subjective auditing processes as well as to increase the level of automation in vulnerability analysis. The proposed methodology follows design science research principles to support the development and validation of scientifically-sound artifacts. More specifically, the proposed data-driven cybersecurity architecture orchestrates a range of modules that include: (i) a vulnerability data model that captures a variety of publicly accessible cybersecurity-related data sources; (ii) an ensemble-based ML pipeline method that self-adjusts to the best learning models for given cybersecurity tasks; and (iii) a knowledge taxonomy and its instantiated power grid and manufacturing models that capture CI common semantics of cyberphysical functional dependencies across CI networks in critical societal domains. This research contributes data-driven vulnerability analysis approaches that bridge the knowledge gaps among different security functions, such as vulnerability management through related reports analysis. This thesis also correlates vulnerability analysis findings to coordinate mitigation responses in complex CIs. More specifically, the vulnerability data model expands the vulnerability knowledge scope and curates meaningful contexts for vulnerability analysis processes. The proposed ML methods fill information gaps in vulnerability repositories using curated data while further streamlining vulnerability assessment processes. Moreover, the CI security taxonomy provides disciplined and coherent support to specify and group semanticallyrelated components and coordination mechanisms in order to harness the notorious complexity of CI networks such as those prevalent in power grids and manufacturing infrastructures. These approaches learn through interactive processes to proactively detect and analyze vulnerabilities while facilitating actionable insights for security actors to make informed decisions.
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| 2. |
- Barrera Diaz, Carlos Alberto, 1987-, et al.
(författare)
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A Study of Discrete Event Simulation Project Data and Provenance Information Management in an Automotive Manufacturing Plant
- 2017
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Ingår i: Proceedings of the 2017 Winter Simulation Conference. - : IEEE. - 9781538634288 - 9781538634295 - 9781538634301 ; , s. 4012-4023
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Konferensbidrag (refereegranskat)abstract
- Discrete Event Simulation (DES) project data management is a complex and important engineering activity which impacts on an organization’s efficiency. This efficiency could be decreased by the lack of provenance information or the unreliability of existing information regarding previous simulation projects, all of which complicates the reusability of the existing data. This study presents an analysis of the management of simulation projects and their provenance data, according to the different types of scenarios usually found at a manufacturing plant. A survey based on simulation projects at an automotive manufacturing plant was conducted, in order to categorize the information regarding the studied projects, map the available provenance data and standardize its management. This study also introduces an approach that demonstrates how a structured framework based on the specific data involved in the different types of scenarios could allow an improvement of the management of DES projects.
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| 3. |
- Jiang, Yuning, 1993-, et al.
(författare)
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Multi-Level Vulnerability Modeling of Cyber-Physical Systems
- 2018
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Konferensbidrag (refereegranskat)abstract
- Vulnerability is defined as ”weakness of an asset or control that can be exploited by a threat” according to ISO/IEC 27000:2009, and it is a vital cyber-security issue to protect cyber-physical systems (CPSs) employed in a range of critical infrastructures (CIs). However, how to quantify both individual and system vulnerability are still not clear. In our proposed poster, we suggest a new procedure to evaluate CPS vulnerability. We reveal a vulnerability-tree model to support the evaluation of CPS-wide vulnerability index, driven by a hierarchy of vulnerability-scenarios resulting synchronously or propagated by tandem vulnerabilities throughout CPS architecture, and that could be exploited by threat agents. Multiple vulnerabilities are linked by boolean operations at each level of the tree. Lower-level vulnerabilities in the tree structure can be exploited by threat agents in order to reach parent vulnerabilities with increasing CPS criticality impacts. At the asset-level, we suggest a novel fuzzy-logic based valuation of vulnerability along standard metrics. Both the procedure and fuzzy-based approach are discussed and illustrated through SCADA-based smart power-grid system as a case study in the poster, with our goal to streamline the process of vulnerability computation at both asset and CPS levels.
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| 4. |
- Mahmoud, Sara, 1988-, et al.
(författare)
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Where to from here? : On the future development of autonomous vehicles from a cognitive systems perspective
- 2022
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Ingår i: Cognitive Systems Research. - : Elsevier. - 2214-4366 .- 1389-0417. ; 76, s. 63-77
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Tidskriftsartikel (refereegranskat)abstract
- Self-driving cars not only solve the problem of navigating safely from location A to location B; they also have to deal with an abundance of (sometimes unpredictable) factors, such as traffic rules, weather conditions, and interactions with humans. Over the last decades, different approaches have been proposed to design intelligent driving systems for self-driving cars that can deal with an uncontrolled environment. Some of them are derived from computationalist paradigms, formulating mathematical models that define the driving agent, while other approaches take inspiration from biological cognition. However, despite the extensive work in the field of self-driving cars, many open questions remain. Here, we discuss the different approaches for implementing driving systems for self-driving cars, as well as the computational paradigms from which they originate. In doing so, we highlight two key messages: First, further progress in the field might depend on adapting new paradigms as opposed to pushing technical innovations in those currently used. Specifically, we discuss how paradigms from cognitive systems research can be a source of inspiration for further development in modeling driving systems, highlighting emergent approaches as a possible starting point. Second, self-driving cars can themselves be considered cognitive systems in a meaningful sense, and are therefore a relevant, yet underutilised resource in the study of cognitive mechanisms. Overall, we argue for a stronger synergy between the fields of cognitive systems and self-driving vehicles.
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| 5. |
- Ujvari, Sandor, 1972-, et al.
(författare)
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Simulation and emulation of sensor systems for intelligent vehicles
- 1998
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Ingår i: Mechatronics '98. - : Pergamon Press. - 0080433391 ; , s. 385-390
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Konferensbidrag (refereegranskat)abstract
- Simulation of sensor systems for mobile robots are described in this paper. By simulation of smart sensor systems, the performance of semi-autonomous vehicles / mobile robots can be enhanced. Smart sensor systems used in the field of mobile robotics can utilise adaptive algorithms. e. g. artificial neural nets, fuzzy logic or hybrid variants of these systems. The development, training and evaluation of adaptive algorithms for sensor systems can be done within a virtual environment in which graphical models are built to simulate an intelligent vehicle, its sensors, and its environment. The virtual sensors are validated by comparing the characteristics of the virtual sensors with those of the real devices.
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| 6. |
- Igelmo, Victor, 1992-, et al.
(författare)
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Enabling Industrial Mixed Reality Using Digital Continuity : An Experiment Within Remanufacturing
- 2022
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Ingår i: SPS2022. - Amsterdam; Berlin; Washington, DC : IOS Press. - 9781643682686 - 9781643682693 ; , s. 497-507
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Konferensbidrag (refereegranskat)abstract
- In the digitalisation era, overlaying digital, contextualised information on top of the physical world is essential for an efficient operation. Mixed reality (MR) is a technology designed for this purpose, and it is considered one of the critical drivers of Industry 4.0. This technology has proven to have multiple benefits in the manufacturing area, including improving flexibility, efficacy, and efficiency. Among the challenges that prevent the big-scale implementation of this technology, there is the authoring challenge, which we address by answering the following research questions: (1) “how can we fasten MR authoring in a manufacturing context?” and (2) “how can we reduce the deployment time of industrial MR experiences?”. This paper presents an experiment performed in collaboration with Volvo within the remanufacturing of truck engines. MR seems to be more valuable for remanufacturing than for many other applications in the manufacturing industry, and the authoring challenge appears to be accentuated. In this experiment, product lifecycle management (PLM) tools are used along with internet of things (IoT) platforms and MR devices. This joint system is designed to keep the information up-to-date and ready to be used when needed. Having all the necessary data cascading from the PLM platform to the MR device using IoT prevents information silos and improves the system’s overall reliability. Results from the experiment show how the interconnection of information systems can significantly reduce development and deployment time. Experiment findings include a considerable increment in the complexity of the overall IT system, the need for substantial investment in it, and the necessity of having highly qualified IT staff. The main contribution of this paper is a systematic approach to the design of industrial MR experiences.
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| 7. |
- Li, Xiaoxia, et al.
(författare)
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Review on Learning-based Methods for shop Scheduling problems
- 2022
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Ingår i: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022. - : IEEE. - 9781665492447 - 9781665492454 ; , s. 294-298
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Konferensbidrag (refereegranskat)abstract
- Shop scheduling is an effective way for manufacturers to improve their manufacturing performances. However, due to its complexity, it is difficult to deal with shop scheduling problems (SSP). Thus, SSP has received a lot of attention from industry and academia. Various kinds of methods have been proposed to solve SSP. Learning-based method is just one of the most representative methods for SSP. This paper focuses on reviewing the learning-based methods for SSP. Firstly, the methods for SSP are briefly introduced. Then, its description and model are provided and its classification is discussed. Next, the learning-based methods for SSP are classified according to the machine learning technique used in the methods. Based on the classification, the related work on each type of learning-based methods for SSP is summarized and further analyzed and compared with other traditional methods. Finally, the future research opportunities and challenges of the learning-based methods for SSP are summarized.
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| 8. |
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| 9. |
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| 10. |
- Hassan, Mahdi Mohammad, 1977-, et al.
(författare)
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Testability and Software Robustness : A Systematic Literature Review
- 2015
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Ingår i: 2015 41st Euromicro Conference on Software Engineering and Advanced Applications. - Funchal, Madeira, Portugal : IEEE. - 9781467375856 ; , s. 341-348
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Konferensbidrag (refereegranskat)abstract
- The concept of software testability has been researched in several different dimensions, however the relation of this important concept with other quality attributes is a grey area where existing evidence is scattered. The objective of this study is to present a state-of-the-art with respect to issues of importance concerning software testability and an important quality attribute: software robustness. The objective is achieved by conducting a systematic literature review (SLR) on the topic. Our results show that a variety of testability issues are in focus with observability and controllability issues being most researched. Fault tolerance, exception handling and handling external influence are prominent robustness issues in focus.
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