10,982 research outputs found

    Automation for network security configuration: state of the art and research trends

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    The size and complexity of modern computer networks are progressively increasing, as a consequence of novel architectural paradigms such as the Internet of Things and network virtualization. Consequently, a manual orchestration and configuration of network security functions is no more feasible, in an environment where cyber attacks can dramatically exploit breaches related to any minimum configuration error. A new frontier is then the introduction of automation in network security configuration, i.e., automatically designing the architecture of security services and the configurations of network security functions, such as firewalls, VPN gateways, etc. This opportunity has been enabled by modern computer networks technologies, such as virtualization. In view of these considerations, the motivations for the introduction of automation in network security configuration are first introduced, alongside with the key automation enablers. Then, the current state of the art in this context is surveyed, focusing on both the achieved improvements and the current limitations. Finally, possible future trends in the field are illustrated

    Graduate Catalog of Studies, 2023-2024

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    Research Assessment Exercise : Report 2023 : International evaluation of research at the University of Vaasa

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    The University of Vaasa is a business-oriented and multidisciplinary science university established in 1968. The university’s strategy focuses on three areas of research: management and change, finance and economic decision-making, and energy and sustainable development. It highlights multidisciplinary research with strong disciplinary knowledge integrated through research platforms to support solving important global challenges. The core mission is to advance new knowledge and to “Energise Business and Society.” The University of Vaasa has a core faculty of 584 and 5,203 students with 190 international students and 296 PhD students. International accreditations, unique research infrastructure, and partnerships with global businesses and organisations make the University of Vaasa a trusted and valued partner within both regional and international innovation ecosystems. The Universities Act (Section 87. Evaluation (Amendment 1302/2013)) stipulates that universities must evaluate their research activities. In line with the strategy of the University of Vaasa, the university evaluates its research activities every five years in order to strengthen the quality of the research internationally, to advance academic and societal impacts of the research, and to further develop the research activities and environment. The previous research evaluations were carried out in 2010 and in 2015. This third research evaluation covered research activities from 2015 to 2020. Diversity, meaningfulness, and focus on future were important features of the research assessment exercise (RAE). The RAE was carried out as a multilevel and multidimensional evaluation targeting research environment, research cooperation and funding, publications, and scientific activities including societal impact. In addition to research groups and the university as a whole, it focused on schools and platforms. The evaluation material and the expert panels’ interviews thus covered three different levels of the university organisation. A Steering Committee consisting of members of the Research Council of the University of Vaasa (2021–2023) was nominated to support and guide the research evaluation. The RAE Univaasa 2022 followed practices of responsible evaluation. Engagement of the research units and researchers was an important aspect of the evaluation process. The evaluation team designed, organised, and implemented the different phases of the RAE in collaboration with the heads of the schools, platforms, and research group leaders. All evaluated units got basic summaries of their research output and bibliometric reports before preparing their self-evaluation reports. The material and the bibliometric reports aimed to provide the units tools for self-reflection and further development of their research. In addition to the CWTS analysis prepared by Leiden University, SciVal analyses on Scopus publications were performed for each unit by the Tritonia Academic Library. Bibliometric analyses also included results from AI-analysis of the themes of open access publications (OSUVA, 2018-2021). The external evaluation was performed by five panels of independent scientific experts. Four of the panels were discipline-specific (based on the school’s disciplines). These school-based panels were asked to provide written comments by comparing each research group’s research to the international and national level of research in the respective field. Based on the research group level evaluations, each school-based panel was asked to offer an overall assessment of the school’s research activities and quality of research. A separate team of the panellists were responsible for the assessment of the three research platforms. The University Panel, consisting of the panel chair and the chairs of the school-based panels, was asked to provide an integrating evaluation of the quality of research activities and environment at the University of Vaasa and to offer recommendations for how the university should develop its research. The results of the assessment and the expert panels’ reports and recommendations will have an effect on the strategic development of research within the university from 2023 onwards. Evaluation indicated that several research groups are currently at a high international level. The areas represented at the University of Vaasa are ones where excellent researchers have many possibilities. The societal impact of research and the industrial cooperation with regional businesses and also the wider interaction with the society work very well at the University of Vaasa. The flexibility of the cooperation seems to be far greater than in many other universities. Many of the projects contribute clearly to the research and the education of the university and provide useful information for the companies the research groups partner with. However, building international research capacity will remain challenging. This is partly a product of the size of the University and the research groups, most of which are relatively small and rely on a small number of high performing professors. The international experts gave several recommendations on how to improve the quality of research at the University of Vaasa. Externally funded projects that support the university’s aim to become an international research university should be encouraged. The experts suggested that the strategy is augmented with more concrete goals on the research focus, quality, and volume. The implementation plan should specify at some level what would be the areas, or modes of operation, in which the university wants to excel, and how this excellence is going to be measured. Recruitment should be prioritised based on the strategy of the university and the availability of excellent people. The university also should consider using international Professors of Practice and inviting more international Visiting Professorships. Moreover, increased possibilities for faculty and PhD students to engage in international activities could boost production of top-level research. The panels also assessed the role of the evaluated units and the internal cooperation within the university. The research groups vary a lot in their size, but also in their cohesion. The panellists saw that in terms of organisation, some groups were tight clusters, while other groups did not seem to have a clear structure. They considered that it would be very useful if each researcher would have an intellectual home base at the university. The panellists perceived the relationship between research groups and platforms to be unclear. The model was considered complicated relative to the size of the schools and the university. The panellists suggested reviewing the role and form of the platforms. In particular, the panellists suggested that in relation to the service of schools and their research groups, the platforms should have a supporting role, instead of trying to form research identities of their own. However, the panellists also considered that there is no definite need to have all the platforms operate in the same way

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

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    In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

    Challenges in the Design and Implementation of IoT Testbeds in Smart-Cities : A Systematic Review

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    Advancements in wireless communication and the increased accessibility to low-cost sensing and data processing IoT technologies have increased the research and development of urban monitoring systems. Most smart city research projects rely on deploying proprietary IoT testbeds for indoor and outdoor data collection. Such testbeds typically rely on a three-tier architecture composed of the Endpoint, the Edge, and the Cloud. Managing the system's operation whilst considering the security and privacy challenges that emerge, such as data privacy controls, network security, and security updates on the devices, is challenging. This work presents a systematic study of the challenges of developing, deploying and managing urban monitoring testbeds, as experienced in a series of urban monitoring research projects, followed by an analysis of the relevant literature. By identifying the challenges in the various projects and organising them under the V-model development lifecycle levels, we provide a reference guide for future projects. Understanding the challenges early on will facilitate current and future smart-cities IoT research projects to reduce implementation time and deliver secure and resilient testbeds

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    LATEST ADVANCES ON SECURITY ARCHITECTURE FOR 5G TECHNOLOGY AND SERVICES

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    The roll out of the deployment of the 5G technology has been ongoing globally. The deployment of the technologies associated with 5G has seen mixed reaction as regards its prospects to improve communication services in all spares of life amid its security concerns. The security concerns of 5G network lies in its architecture and other technologies that optimize the performance of its architecture. There are many fractions of 5G security architecture in the literature, a holistic security architectural structure will go a long way in tackling the security challenges. In this paper, the review of the security challenges of the 5G technology based on its architecture is presented along with their proposed solutions. This review was carried out with some keywords relating to 5G securities and architecture; this was used to retrieve appropriate literature for fitness of purpose. The 5G security architectures are mojorly centered around the seven network security layers; thereby making each of the layers a source of security concern on the 5G network. Many of the 5G security challenges are related to authentication and authorization such as denial-of-service attacks, man in the middle attack and eavesdropping. Different methods both hardware (Unmanned Aerial Vehicles, field programmable logic arrays) and software (Artificial intelligence, Machine learning, Blockchain, Statistical Process Control) has been proposed for mitigating the threats. Other technologies applicable to 5G security concerns includes: Multi-radio access technology, smart-grid network and light fidelity. The implementation of these solutions should be reviewed on a timely basis because of the dynamic nature of threats which will greatly reduce the occurrence of security attacks on the 5G network

    Securing IoT Applications through Decentralised and Distributed IoT-Blockchain Architectures

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    The integration of blockchain into IoT can provide reliable control of the IoT network's ability to distribute computation over a large number of devices. It also allows the AI system to use trusted data for analysis and forecasts while utilising the available IoT hardware to coordinate the execution of tasks in parallel, using a fully distributed approach. This thesis's  rst contribution is a practical implementation of a real world IoT- blockchain application, ood detection use case, is demonstrated using Ethereum proof of authority (PoA). This includes performance measurements of the transaction con-  rmation time, the system end-to-end latency, and the average power consumption. The study showed that blockchain can be integrated into IoT applications, and that Ethereum PoA can be used within IoT for permissioned implementation. This can be achieved while the average energy consumption of running the ood detection system including the Ethereum Geth client is small (around 0.3J). The second contribution is a novel IoT-centric consensus protocol called honesty- based distributed proof of authority (HDPoA) via scalable work. HDPoA was analysed and then deployed and tested. Performance measurements and evaluation along with the security analyses of HDPoA were conducted using a total of 30 di erent IoT de- vices comprising Raspberry Pis, ESP32, and ESP8266 devices. These measurements included energy consumption, the devices' hash power, and the transaction con rma- tion time. The measured values of hash per joule (h/J) for mining were 13.8Kh/J, 54Kh/J, and 22.4Kh/J when using the Raspberry Pi, the ESP32 devices, and the ESP8266 devices, respectively, this achieved while there is limited impact on each de- vice's power. In HDPoA the transaction con rmation time was reduced to only one block compared to up to six blocks in bitcoin. The third contribution is a novel, secure, distributed and decentralised architecture for supporting the implementation of distributed arti cial intelligence (DAI) using hardware platforms provided by IoT. A trained DAI system was implemented over the IoT, where each IoT device hosts one or more neurons within the DAI layers. This is accomplished through the utilisation of blockchain technology that allows trusted interaction and information exchange between distributed neurons. Three di erent datasets were tested and the system achieved a similar accuracy as when testing on a standalone system; both achieved accuracies of 92%-98%. The system accomplished that while ensuring an overall latency of as low as two minutes. This showed the secure architecture capabilities of facilitating the implementation of DAI within IoT while ensuring the accuracy of the system is preserved. The fourth contribution is a novel and secure architecture that integrates the ad- vantages o ered by edge computing, arti cial intelligence (AI), IoT end-devices, and blockchain. This new architecture has the ability to monitor the environment, collect data, analyse it, process it using an AI-expert engine, provide predictions and action- able outcomes, and  nally share it on a public blockchain platform. The pandemic caused by the wide and rapid spread of the novel coronavirus COVID-19 was used as a use-case implementation to test and evaluate the proposed system. While providing the AI-engine trusted data, the system achieved an accuracy of 95%,. This is achieved while the AI-engine only requires a 7% increase in power consumption. This demon- strate the system's ability to protect the data and support the AI system, and improves the IoT overall security with limited impact on the IoT devices. The  fth and  nal contribution is enhancing the security of the HDPoA through the integration of a hardware secure module (HSM) and a hardware wallet (HW). A performance evaluation regarding the energy consumption of nodes that are equipped with HSM and HW and a security analysis were conducted. In addition to enhancing the nodes' security, the HSM can be used to sign more than 120 bytes/joule and encrypt up to 100 bytes/joule, while the HW can be used to sign up to 90 bytes/joule and encrypt up to 80 bytes/joule. The result and analyses demonstrated that the HSM and HW enhance the security of HDPoA, and also can be utilised within IoT-blockchain applications while providing much needed security in terms of con dentiality, trust in devices, and attack deterrence. The above contributions showed that blockchain can be integrated into IoT systems. It showed that blockchain can successfully support the integration of other technolo- gies such as AI, IoT end devices, and edge computing into one system thus allowing organisations and users to bene t greatly from a resilient, distributed, decentralised, self-managed, robust, and secure systems
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