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  • 1.
    Aklilu, Yohannes T.
    et al.
    University of Skövde, SWE.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Survey on blockchain for smart grid management, control, and operation2022In: Energies, E-ISSN 1996-1073, Vol. 15, no 1, article id 193Article in journal (Refereed)
    Abstract [en]

    Power generation, distribution, transmission, and consumption face ongoing challenges such as smart grid management, control, and operation, resulting from high energy demand, the diversity of energy sources, and environmental or regulatory issues. This paper provides a comprehensive overview of blockchain-based solutions for smart grid management, control, and operations. We systematically summarize existing work on the use and implementation of blockchain technology in various smart grid domains. The paper compares related reviews and highlights the challenges in the management, control, and operation for a blockchain-based smart grid as well as future research directions in the five categories: collaboration among stakeholders; data analysis and data manage-ment; control of grid imbalances; decentralization of grid management and operations; and security and privacy. All these aspects have not been covered in previous reviews. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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  • 2.
    Atif, Yacine
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Jeusfeld, Manfred A.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Internet of Things Approach to Cloud-Based Smart Car Parking2016In: The 7th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2016)/The 6th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2016)/Affiliated Workshops / [ed] Elhadi Shakshuki, Elsevier , 2016, p. 193-198Conference paper (Refereed)
    Abstract [en]

    Concerns for parking are becoming imminent to best support the urban core. These persistent parking problems could be turned into new opportunities, brought by current trends in meeting the globally connected continuum. This paper reveals a work-in- progress to capitalize on private land properties for parking, in order to relieve stress on public agencies, create new sources of revenue, and enlist new entities in the intermediary market. These intermediaries, labelled as Parking Service Providers (or PSPs) play a broker role through advertising parking lots on a shared cloud platform. To streamline these business collaborations and related processes, physical parking lots are augmented with Internet connectivity allowing cloud-provided applications to congregate these lots into a larger inventory. The Internet of Things (IoT) paradigm expands the scope of cloud-based intelligent car parking services in smart cities, with novel applications that better regulate car-parking related traffic. This paper presents a work-in-progress agenda that contributes to new business solutions and state-of-the-art research impacts. We reveal a multi- layered system of PSP-business model through interdisciplinary research blocks where original results are expected to be made at each layer.

  • 3.
    Atif, Yacine
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Lindström, Birgitta
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Jeusfeld, Manfred
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Yuning, Jiang
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Brax, Christoffer
    CombiTech AB, Skövde, Sweden.
    Gustavsson, Per M.
    CombiTech AB, Skövde, Sweden.
    Cyber-Threat Intelligence Architecture for Smart-Grid Critical Infrastructures Protection2017Conference paper (Refereed)
    Abstract [en]

    Critical infrastructures (CIs) are becoming increasingly sophisticated with embedded cyber-physical systems (CPSs) that provide managerial automation and autonomic controls. Yet these advances expose CI components to new cyber-threats, leading to a chain of dysfunctionalities with catastrophic socio-economical implications. We propose a comprehensive architectural model to support the development of incident management tools that provide situation-awareness and cyber-threats intelligence for CI protection, with a special focus on smart-grid CI. The goal is to unleash forensic data from CPS-based CIs to perform some predictive analytics. In doing so, we use some AI (Artificial Intelligence) paradigms for both data collection, threat detection, and cascade-effects prediction. 

  • 4.
    Atif, Yacine
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Jiang, Yuning
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Jeusfeld, Manfred A.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Lindström, Birgitta
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Brax, Christoffer
    Combitech.
    Haglund, Daniel
    Combitech.
    Lindström, Björn
    Combitech.
    Cyber-threat analysis for Cyber-Physical Systems: Technical report for Package 4, Activity 3 of ELVIRA project2018Report (Other academic)
    Abstract [en]

    Smart grid employs ICT infrastructure and network connectivity to optimize efficiency and deliver new functionalities. This evolu- tion is associated with an increased risk for cybersecurity threats that may hamper smart grid operations. Power utility providers need tools for assessing risk of prevailing cyberthreats over ICT infrastructures. The need for frameworks to guide the develop- ment of these tools is essential to define and reveal vulnerability analysis indicators. We propose a data-driven approach for design- ing testbeds to evaluate the vulnerability of cyberphysical systems against cyberthreats. The proposed framework uses data reported from multiple components of cyberphysical system architecture layers, including physical, control, and cyber layers. At the phys- ical layer, we consider component inventory and related physi- cal flows. At the control level, we consider control data, such as SCADA data flows in industrial and critical infrastructure control systems. Finally, at the cyber layer level, we consider existing secu- rity and monitoring data from cyber-incident event management tools, which are increasingly embedded into the control fabrics of cyberphysical systems.

  • 5.
    Atif, Yacine
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Jiang, Yuning
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Lindström, Birgitta
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Jeusfeld, Manfred
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Andler, Sten
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Nero, Eva
    Combitech, Sweden.
    Brax, Christoffer
    Combitech, Sweden.
    Haglund, Daniel
    Combitech, Sweden.
    Multi-agent Systems for Power Grid Monitoring: Technical report for Package 4.1 of ELVIRA project2018Report (Other academic)
    Abstract [en]

    This document reports a technical description of ELVIRA project results obtained as part of Work- package 4.1 entitled “Multi-agent systems for power Grid monitoring”. ELVIRA project is a collaboration between researchers in School of IT at University of Skövde and Combitech Technical Consulting Company in Sweden, with the aim to design, develop and test a testbed simulator for critical infrastructures cybersecurity. This report outlines intelligent approaches that continuously analyze data flows generated by Supervisory Control And Data Acquisition (SCADA) systems, which monitor contemporary power grid infrastructures. However, cybersecurity threats and security mechanisms cannot be analyzed and tested on actual systems, and thus testbed simulators are necessary to assess vulnerabilities and evaluate the infrastructure resilience against cyberattacks. This report suggests an agent-based model to simulate SCADA- like cyber-components behaviour when facing cyber-infection in order to experiment and test intelligent mitigation mechanisms. 

  • 6.
    Atif, Yacine
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Kharrazi, Sogol
    National Road Transport Research Institute, Linköping, Sweden.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Internet of Things data analytics for parking availability prediction and guidance2020In: European transactions on telecommunications, ISSN 1124-318X, E-ISSN 2161-3915, Vol. 31, article id e3862Article in journal (Refereed)
    Abstract [en]

    Cutting-edge sensors and devices are increasingly deployed within urban areas to make-up the fabric of transmission control protocol/internet protocol con- nectivity driven by Internet of Things (IoT). This immersion into physical urban environments creates new data streams, which could be exploited to deliver novel cloud-based services. Connected vehicles and road-infrastructure data are leveraged in this article to build applications that alleviate notorious parking and induced traffic-congestion issues. To optimize the utility of parking lots, our proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot, by the time a vehicle is expected to reach it. The algorithm features three modular sections. First, a search pro- cess is triggered to identify the expected arrival-time periods to all parking lots in the targeted central business district (CBD) area. This process utilizes smart-pole data streams reporting congestion rates across parking area junc- tions. Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state-transition matrix, showing the transfor- mation of available spots in a parking lot over short periods of time. Finally, this matrix is projected against similar future seasonal periods to figure out the actual vacancy-expectation of a lot. The performance evaluation over an actual busy CBD area in Stockholm (Sweden) shows increased scalability capa- bilities, when further parking resources are made available, compared to a baseline case algorithm. Using standard urban-mobility simulation packages, the traffic-congestion-aware SmartPark is also shown to minimize the journey duration to the selected parking lot while maximizing the chances to find an available spot at the selected lot.

  • 7.
    Chaparadza, Ranganai
    et al.
    ETSI AFI & IPv6Forum.
    Ben Meriem, Tayeb
    Orange, AFI.
    Radier, Benoit
    Orange, AFI.
    Szott, Szymon
    AGH University , AFI.
    Wodczak, Michal
    IT Department of Poznan University of Economics, AFI.
    Prakash, Arun
    FOKUS, AFI.
    Ding, Jianguo
    Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Soulhi, Said
    Ericsson, AFI.
    Mihailovic, Andrej
    KCL, AFI.
    Implementation Guide for the ETSI AFI GANA Model: a Standardized Reference Model for Autonomic Networking, Cognitive Networking and Self-Management2013In: Proceedings of 2013 IEEE Globecom Workshops, IEEE Computer Society , 2013, p. 935-940Conference paper (Refereed)
    Abstract [en]

    This paper describes an Implementation Guide for an emerging standard for autonomic management &control of networks and services, namely the ETSI AFI GANA Reference Model for Autonomic Networking, Cognitive Networking and Self-Management (an emerging standard from ETSI). The implementation guide also takes into consideration the impact of emerging paradigms such as SDN and Virtualization. This is because as the standardized Reference Model has been published, it becomes important to provide an associated Implementation Guide that can be followed in implementing autonomic management & control in network architectures.

  • 8.
    Chaparadza, Ranganai
    et al.
    ETSI AFI & IPv6Forum.
    Ben Meriem, Tayeb
    Orange, AFI.
    Radier, Benoit
    Orange, AFI.
    Szott, Szymon
    AGH University, AFI.
    Wodczak, Michal
    IT Department of Poznan University of Economics, AFI.
    Prakash, Arun
    FOKUS, AFI.
    Ding, Jianguo
    Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Soulhi, Said
    Ericsson, AFI.
    Mihailovic, Andrej
    KCL, AFI.
    SDN Enablers in the ETSI AFI GANA Reference Model for Autonomic Management & Control (emerging standard), and Virtualization Impact2013In: Proceedings of 2013 IEEE Globecom Workshops, IEEE Computer Society , 2013, p. 818-823Conference paper (Refereed)
    Abstract [en]

    This standardization oriented paper describes the SDN (Software-Driven Networking) Enablers in the ETSI AFI GANA Reference Model for Autonomic Management & Control (an emerging standard from ETSI), and impact of Virtualization. This is because in this study we see that Autonomic Management & Control and SDN (Software-Driven Networking) share the same objective of enabling programmable, manageable, dynamically self-adaptable and cost-effective networks and services. SDN enablers in the AFI GANA Model are: (1) Modularization of Logically centralized Control Software (the GANA Network Level DEs in the GANA Knowledge Plane) and Reference Points Definitions; (2) Primitives for Programmability at various layers; (3) Use of Runtime Executable Behavioral Models to complement the use of Policy-Control and dynamic policies; (4) The role and value the GANA MBTS (Model Based Translation Service) brings in SDN; (5) The role and value the GANA ONIX (Overlay Network for Information eXchange) brings in SDN; (6) Interworking GANA Knowledge Plane Decision Elements and SDN Controllers; (7) GANA “Decision-Making-Elements” logics as “software” that can be loaded into nodes and network (enabling “software-empowered networks”). The study is important because it is now becoming critical to study and explore the relationships between Autonomic Management & Control and SDN paradigms, as well as Virtualization, identify complementarities between the paradigms and close the gaps by unifying SDN concepts and associated frameworks with the emerging ETSI AFI GANA Reference Model standard for Autonomic Networking, Cognitive Networking and Self-Management, a hybrid model enabling to combine both centralized and distributed control.

  • 9.
    Chaparadza, Ranganai
    et al.
    IPv6Forum / ETSI AFI.
    Ben Meriem, Tayeb
    Orange / ETSI AFI.
    Strassner, John
    Huawei / TMF ZOOM.
    Radier, Benoit
    Orange / ETSI NTECH/AFI.
    Soulhi, Said
    Ericsson / ETSI/NTECH AFI.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Yan, Zhiwei
    CNNIC / Chinese Academy of Sciences.
    Industry Harmonization for Unified Standards on Autonomic Management & Control (AMC) of Networks and Services, SDN and NFV2014In: Globecom Workshops (GC Wkshps), 2014, IEEE conference proceedings , 2014, p. 155-160Conference paper (Refereed)
  • 10.
    Chaparadza, Ranganai
    et al.
    IPv6Forum & ETSI AFI, Berlin, Germany.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Djenouri, Djamel
    CERIST Research Center, Algiers, Algeria.
    Preface of the 6th IEEE International Workshop on Management of Emerging Networks and Services (IEEE Globecom MENS 2014)2014In: 2014 IEEE Globecom Workshops (GC Wkshps), IEEE conference proceedings , 2014, p. 150-154Conference paper (Refereed)
  • 11.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Intrusion Detection, Prevention, and Response System (IDPRS) for Cyber-Physical Systems (CPSs)2015In: Securing Cyber Physical Systems / [ed] Al-Sakib Khan Pathan, Boca Raton, US: Taylor & Francis Group , 2015, p. 371-392Chapter in book (Refereed)
    Abstract [en]

    Cyber-physical systems (CPSs) are integrated physical, engineered, andsocial systems whose operations are monitored, coordinated, controlled, and integratedby a computing and communication core. Due to the dynamic structure ofCPSs, the security measurements are often complex. Given this fact, the objectiveof this chapter is to present the intrusion detection, prevention, and response system(IDPRS) for such a dynamic environment.

  • 12.
    Ding, Jianguo
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Aklilu, Yohannes Tadesse
    University of Skövde.
    Blockchain for Smart Grid Operations,Control and Management2022Report (Other academic)
    Abstract [en]

    A comprehensive overview of blockchain-based smart grid management, control, and operation solutions. The report compares with related reviews andhighlights the challenges in management, control, and operation for a blockchain-based smart grid, as well as future research directions in five categories: collaboration between actors, data analytics and management, control ofnetwork imbalances, decentralization of network management and operation,security and privacy.The report reviews how blockchain technology can potentially solve thechallenges of decentralized solutions for future renewable energy systems. Asa result, several applications of blockchain for renewable energy are discussed,such as electric vehicles, decentralized P2P energy transactions, carbon certification and trading, physical information security, energy transfer, Energy-to-X,and the Internet of Energy.A guideline for the implementation of blockchain to corresponding applications for future renewable energy is also presented in this report. This includesthe different blockchain system architectures, the data flow from the powergrid processed and recorded, the choice of the appropriate consensus, and thedifferent blockchain frameworks. 

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  • 13.
    Ding, Jianguo
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Atif, Yacine
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Lindström, Birgitta
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Jeusfeld, Manfred
    Högskolan i Skövde, Institutionen för informationsteknologi.
    CPS-based Threat Modeling for Critical Infrastructure Protection2017In: Performance Evaluation Review, ISSN 0163-5999, E-ISSN 1557-9484, Vol. 45, no 2, p. 129-132Article in journal (Refereed)
    Abstract [en]

    Cyber-Physical Systems (CPSs) are augmenting traditionalCritical Infrastructures (CIs) with data-rich operations. Thisintegration creates complex interdependencies that exposeCIs and their components to new threats. A systematicapproach to threat modeling is necessary to assess CIs’ vulnerabilityto cyber, physical, or social attacks. We suggest anew threat modeling approach to systematically synthesizeknowledge about the safety management of complex CIs andsituational awareness that helps understanding the nature ofa threat and its potential cascading-effects implications.

  • 14.
    Ding, Jianguo
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Chaparadza, Ranganai
    IPv6 Forum, ETSI-AFI , Berlin , Germany.
    Network Management2016In: Encyclopedia of Information Systems and Technology / [ed] Phillip A. Laplante, CRC Press , 2016, p. 881-899Chapter in book (Refereed)
  • 15.
    Ding, Jianguo
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Lindström, Birgitta
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Mathiason, Gunnar
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Towards Threat Modeling for CPS-based Critical Infrastructure Protection2015In: Proceedings of the International Emergency Management Society (TIEMS), 22nd TIEMS Annual Conference: Evolving threats and vulnerability landscape: new challenges for the emergency management / [ed] Snjezana Knezic & Meen Poudyal Chhetri, Brussels: TIEMS, The International Emergency Management Society , 2015Conference paper (Refereed)
    Abstract [en]

    With the evolution of modern Critical Infrastructures (CI), more Cyber-Physical systems are integrated into the traditional CIs. This makes the CIs a multidimensional complex system, which is characterized by integrating cyber-physical systems into CI sectors (e.g., transportation, energy or food & agriculture). This integration creates complex interdependencies and dynamics among the system and its components. We suggest using a model with a multi-dimensional operational specification to allow detection of operational threats. Embedded (and distributed) information systems are critical parts of the CI where disruption can lead to serious consequences. Embedded information system protection is therefore crucial. As there are many different stakeholders of a CI, comprehensive protection must be viewed as a cross-sector activity to identify and monitor the critical elements, evaluate and determine the threat, and eliminate potential vulnerabilities in the CI. A systematic approach to threat modeling is necessary to support the CI threat and vulnerability assessment. We suggest a Threat Graph Model (TGM) to systematically model the complex CIs. Such modeling is expected to help the understanding of the nature of a threat and its impact on throughout the system. In order to handle threat cascading, the model must capture local vulnerabilities as well as how a threat might propagate to other components. The model can be used for improving the resilience of the CI by encouraging a design that enhances the system's ability to predict threats and mitigate their damages. This paper surveys and investigates the various threats and current approaches to threat modeling of CI. We suggest integrating both a vulnerability model and an attack model, and we incorporate the interdependencies within CI cross CI sectors. Finally, we present a multi-dimensional threat modeling approach for critical infrastructure protection.

  • 16.
    Ding, Jianguo
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Naserinia, Vahid
    University of Skövde, SWE.
    Blockchain for future renewable energy2022In: Decentralized Frameworks for Future Power Systems: Operation, Planning and Control Perspectives / [ed] Mohsen Parsa Moghaddam, Reza Zamani, Hassan Haes Alhelou, Pierluigi Siano, Academic Press, 2022, p. 129-146Chapter in book (Refereed)
    Abstract [en]

    To better optimize and control the renewable energy system and its integration with traditional grid systems and other energy systems, corresponding technologies are needed to meet its growing practical application requirements: decentralized management and control, support for decentralized decision-making, fine-grained and timely data sharing, maintain data and business privacy, support fast and low-cost electricity market transactions, maintain the security and reliability of system operation data, and prevent malicious cyberattacks. Blockchain is based on core technologies such as distributed ledgers, asymmetric encryption, consensus mechanisms, and smart contracts and has some excellent features such as decentralization, openness, independence, security, and anonymity. These characteristics seem to meet the technical requirements of future renewable energy systems partially. This chapter will systematically review how blockchain technology can potentially solve the challenges with decentralized solutions for future renewable energy systems and show a guideline to implement blockchain-based corresponding applications for future renewable energy. © 2022 Elsevier Inc. All rights reserved.

  • 17.
    Ding, Jianguo
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Qammar, Attia
    Univ Sci & Technol Beijing, CHN.
    Zhang, Zhimin
    Univ Sci & Technol Beijing, CHN.
    Karim, Ahmad
    Bahauddin Zakariya Univ, PAK.
    Ning, Huansheng
    Univ Sci & Technol Beijing, CHN.
    Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions2022In: Energies, E-ISSN 1996-1073, Vol. 15, no 18, article id 6799Article, review/survey (Refereed)
    Abstract [en]

    Smart Grids (SGs) are governed by advanced computing, control technologies, and networking infrastructure. However, compromised cybersecurity of the smart grid not only affects the security of existing energy systems but also directly impacts national security. The increasing number of cyberattacks against the smart grid urgently necessitates more robust security protection technologies to maintain the security of the grid system and its operations. The purpose of this review paper is to provide a thorough understanding of the incumbent cyberattacks' influence on the entire smart grid ecosystem. In this paper, we review the various threats in the smart grid, which have two core domains: the intrinsic vulnerability of the system and the external cyberattacks. Similarly, we analyze the vulnerabilities of all components of the smart grid (hardware, software, and data communication), data management, services and applications, running environment, and evolving and complex smart grids. A structured smart grid architecture and global smart grid cyberattacks with their impact from 2010 to July 2022 are presented. Then, we investigated the the thematic taxonomy of cyberattacks on smart grids to highlight the attack strategies, consequences, and related studies analyzed. In addition, potential cybersecurity solutions to smart grids are explained in the context of the implementation of blockchain and Artificial Intelligence (AI) techniques. Finally, technical future directions based on the analysis are provided against cyberattacks on SGs.

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  • 18.
    Geremew, Getahun Wassie
    et al.
    Addis Ababa University, Ethiopia.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder2023In: Journal of Computer Networks and Communications, ISSN 2090-7141, E-ISSN 2090-715X, Vol. 2023, article id 1495642Article in journal (Refereed)
    Abstract [en]

    Currently, the widespread of real-time applications such as VoIP and videos-based applications require more data rates and reduced latency to ensure better quality of service (QoS). A well-designed traffic classification mechanism plays a major role for good QoS provision and network security verification. Port-based approaches and deep packet inspection (DPI) techniques have been used to classify and analyze network traffic flows. However, none of these methods can cope with the rapid growth of network traffic due to the increasing number of Internet users and the growth of real-time applications. As a result, these methods lead to network congestion, resulting in packet loss, delay, and inadequate QoS delivery. Recently, a deep learning approach has been explored to address the time-consumption and impracticality gaps of the abovementioned methods and maintain existing and future traffics of real-time applications. The aim of this research is then to design a dynamic traffic classifier that can detect elephant flows to prevent network congestion. Thus, we are motivated to provide efficient bandwidth and fast transmission requirements to many Internet users using SDN capability and the potential of deep learning. Specifically, DNN, CNN, LSTM, and Deep autoencoder are used to build elephant detection models that achieve an average accuracy of 99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the promising algorithms that do not require human class labeler. It achieves an accuracy of 97.95% with a loss of 0.13. Since the loss value is closer to zero, the performance of the model is good. Therefore, the study has a great importance to Internet service providers, Internet subscribers, as well as for future researchers in this area.

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  • 19.
    Jeusfeld, Manfred A.
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Jiang, Yuning
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Atif, Yacine
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Haglund, Daniel
    Combitech AB.
    Brax, Christoffer
    Combitech AB.
    Taxonomy of Events and Components in the Power Grid: Technical description for work packages 3.1 and 3.2 of the ELVIRA Project2018Report (Other academic)
    Abstract [en]

    This document reports a technical description of ELVIRA project results obtained as part of Work-package 3.1&3.2 entitled “Taxonomy of Critical Infrastructure (Taxonomy of events + Taxonomy of CI component and relationship)”. ELVIRA project is a collaboration between researchers in School of IT at University of Skövde and Combitech Technical Consulting Company in Sweden, with the aim to design, develop and test a testbed simulator for critical infrastructures cybersecurity.

  • 20.
    Jiang, Yuning
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Atif, Yacine
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Agent Based Testbed Design for Cyber Vulnerability Assessment in Smart-Grids2018Conference paper (Other academic)
    Abstract [en]

    Smart grid employs Information and Communication Technology (ICT) infrastructure and network connectivity to optimize efficiency and deliver new functionalities. This evolution is associated with an increased risk for cybersecurity threats that may hamper smart grid operations. Power utility providers need tools for assessing risk of prevailing cyberthreats over ICT infrastructures. The need for frameworks to guide the development of these tools is essential to define and reveal vulnerability analysis indicators. We propose a data-driven approach for designing testbeds to allow the simulation of cyberattacks in order to evaluate the vulnerability and the impact of cyber threat attacks. The proposed framework uses data reported from multiple smart grid components at different smart grid architecture layers, including physical, control, and cyber layers. The multi-agent based framework proposed in this paper would analyze the conglomeration of these data reports to assert malicious attacks.

  • 21.
    Jiang, Yuning
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Atif, Yacine
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Cyber-Physical Systems Security Based on A Cross-Linked and Correlated Vulnerability Database2019In: Critical Information Infrastructures Security: 14th International Conference, CRITIS 2019, Linköping, Sweden, September 23–25, 2019, Revised Selected Papers / [ed] Simin Nadjm-Tehrani, Springer , 2019, p. 71-82Conference paper (Refereed)
    Abstract [en]

    Recent advances in data analytics prompt dynamic datadriven vulnerability assessments whereby data contained from vulnerabilityalert repositories as well as from Cyber-physical System (CPS) layer networks and standardised enumerations. Yet, current vulnerability assessment processes are mostly conducted manually. However, the huge volume of scanned data requires substantial information processing and analytical reasoning, which could not be satisfied considering the imprecision of manual vulnerability analysis. In this paper, we propose to employ a cross-linked and correlated database to collect, extract, filter and visualise vulnerability data across multiple existing repositories, whereby CPS vulnerability information is inferred. Based on our locally-updated database, we provide an in-depth case study on gathered CPS vulnerability data, to explore the trends of CPS vulnerability. In doing so, we aim to support a higher level of automation in vulnerability awareness and back risk-analysis exercises in critical infrastructures (CIs) protection.

  • 22.
    Jiang, Yuning
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Atif, Yacine
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Data Fusion Framework for Cyber Vulnerability Assessment in Smart Grid2018Other (Other academic)
    Abstract [en]

    Smart grid adopts ICT to enhance power-delivery management. However, these advanced technologies also introduce an increasing amount of cyber threats. Cyber threats occur because of vulnerabilities throughout smart-grid layers. Each layer is distinguished by typical data flows. For example, power-data stream flows along the physical layer; command data are pushed to and pulled from sensor-control devices, such as RTUs and PLCs. Vulnerabilities expose these data flows to cyber threat via communication networks, such as local control network, vendor network, corporate network and the wider internet. Thus, these data could be used to analyse vulnerabilities against cyber threats. After data collection, data analysis and modelling techniques would be used for vulnerability assessment.

  • 23.
    Jiang, Yuning
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Atif, Yacine
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Multi-Level Vulnerability Modeling of Cyber-Physical Systems2018Conference paper (Refereed)
    Abstract [en]

    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.

  • 24.
    Jiang, Yuning
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Atif, Yacine
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Wang, Wei
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap.
    A Semantic Framework With Humans in the Loop for Vulnerability-Assessment in Cyber-Physical Production Systems2020In: Risks and Security of Internet and Systems: 14th International Conference, CRiSIS 2019, Hammamet, Tunisia, October 29–31, 2019, Proceedings / [ed] Slim Kallel, Frédéric Cuppens, Nora Cuppens-Boulahia, Ahmed Hadj Kacem, Springer , 2020, p. 128-143Conference paper (Refereed)
    Abstract [en]

    Criticalmanufacturingprocessesinsmartnetworkedsystems such as Cyber-Physical Production Systems (CPPSs) typically require guaranteed quality-of-service performances, which is supported by cyber- security management. Currently, most existing vulnerability-assessment techniques mostly rely on only the security department due to limited communication between di↵erent working groups. This poses a limitation to the security management of CPPSs, as malicious operations may use new exploits that occur between successive analysis milestones or across departmental managerial boundaries. Thus, it is important to study and analyse CPPS networks’ security, in terms of vulnerability analysis that accounts for humans in the production process loop, to prevent potential threats to infiltrate through cross-layer gaps and to reduce the magnitude of their impact. We propose a semantic framework that supports the col- laboration between di↵erent actors in the production process, to improve situation awareness for cyberthreats prevention. Stakeholders with dif- ferent expertise are contributing to vulnerability assessment, which can be further combined with attack-scenario analysis to provide more prac- tical analysis. In doing so, we show through a case study evaluation how our proposed framework leverages crucial relationships between vulner- abilities, threats and attacks, in order to narrow further the risk-window induced by discoverable vulnerabilities.

  • 25.
    Jiang, Yuning
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Atif, Yacine
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Jeusfeld, Manfred
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Andler, Sten
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Lindström, Birgitta
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Brax, Christoffer
    Combitech, Sweden.
    Haglund, Daniel
    Combitech, Sweden.
    Complex Dependencies Analysis: Technical Description of Complex Dependencies in Critical Infrastructures, i.e. Smart Grids. Work Package 2.1 of the ELVIRA Project2018Report (Other academic)
    Abstract [en]

    This document reports a technical description of ELVIRA project results obtained as part of Work-package 2.1 entitled “Complex Dependencies Analysis”. In this technical report, we review attempts in recent researches where connections are regarded as influencing factors to  IT systems monitoring critical infrastructure, based on which potential dependencies and resulting disturbances are identified and categorized. Each kind of dependence has been discussed based on our own entity based model. Among those dependencies, logical and functional connections have been analysed with more details on modelling and simulation techniques.

  • 26.
    Jiang, Yuning
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Jeusfeld, Manfred A.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Atif, Yacine
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Brax, Christoffer
    Combitech AB, Skövde, Sweden.
    Nero, Eva
    Combitech AB, Skövde, Sweden.
    A Language and Repository for Cyber Security of Smart Grids2018In: 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference (EDOC 2018) / [ed] Selmin Nurcan, Pontus Johnson, Los Alamitos, CA: IEEE , 2018, p. 164-170Conference paper (Refereed)
    Abstract [en]

    Power grids form the central critical infrastructure in all developed economies. Disruptions of power supply can cause major effects on the economy and the livelihood of citizens. At the same time, power grids are being targeted by sophisticated cyber attacks. To counter these threats, we propose a domain-specific language and a repository to represent power grids and related IT components that control the power grid. We apply our tool to a standard example used in the literature to assess its expressiveness.

  • 27.
    Jiang, Yuning
    et al.
    Högskolan i Skövde, Forskningsmiljön Informationsteknologi.
    Jeusfeld, Manfred A.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Evaluating the Data Inconsistency of Open-Source Vulnerability Repositories2021In: ARES 2021: The 16th International Conference on Availability, Reliability and Security, Association for Computing Machinery (ACM) , 2021, p. 1-10, article id 86Conference paper (Refereed)
    Abstract [en]

    Modern security practices promote quantitative methods to provide prioritisation insights and support predictive analysis, which is supported by open-source cybersecurity databases such as the Common Vulnerabilities and Exposures (CVE), the National Vulnerability Database (NVD), CERT, and vendor websites. These public repositories provide a way to standardise and share up-to-date vulnerability information, with the purpose to enhance cybersecurity awareness. However, data quality issues of these vulnerability repositories may lead to incorrect prioritisation and misemployment of resources. In this paper, we aim to empirically analyse the data quality impact of vulnerability repositories for actual information technology (IT) and operating technology (OT) systems, especially on data inconsistency. Our case study shows that data inconsistency may misdirect investment of cybersecurity resources. Instead, correlated vulnerability repositories and trustworthiness data verification bring substantial benefits for vulnerability management. 

  • 28.
    Jiang, Yuning
    et al.
    Nanyang Technological University, Singapore.
    Jeusfeld, Manfred A.
    University of Skövde.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Sandahl, Elin
    Norgald AB, Sweden.
    Model-Based Cybersecurity Analysis: Extending Enterprise Modeling to Critical Infrastructure Cybersecurity2023In: Business & Information Systems Engineering, ISSN 2363-7005, E-ISSN 1867-0202, Vol. 65, no 6, p. 643-676Article in journal (Refereed)
    Abstract [en]

    Critical infrastructure (CIs) such as power gridslink a plethora of physical components from many differentvendors to the software systems that control them. Thesesystems are constantly threatened by sophisticated cyberattacks. The need to improve the cybersecurity of such CIs,through holistic system modeling and vulnerability analysis,cannot be overstated. This is challenging since a CIincorporates complex data from multiple interconnectedphysical and computation systems. Meanwhile, exploitingvulnerabilities in different information technology (IT) andoperational technology (OT) systems leads to variouscascading effects due to interconnections between systems.The paper investigates the use of a comprehensive taxonomyto model such interconnections and the implieddependencies within complex CIs, bridging the knowledgegap between IT security and OT security. The complexityof CI dependence analysis is harnessed by partitioningcomplicated dependencies into cyber and cyber-physicalfunctional dependencies. These defined functionaldependencies further support cascade modeling for vulnerabilityseverity assessment and identification of criticalcomponents in a complex system. On top of the proposedtaxonomy, the paper further suggests power-grid referencemodels that enhance the reproducibility and applicability ofthe proposed method. The methodology followed wasdesign science research (DSR) to support the designing andvalidation of the proposed artifacts. More specifically, thestructural, functional adequacy, compatibility, and coveragecharacteristics of the proposed artifacts are evaluatedthrough a three-fold validation (two case studies and expertinterviews). The first study uses two instantiated powergridmodels extracted from existing architectures andframeworks like the IEC 62351 series. The second studyinvolves a real-world municipal power grid. © 2023, The Author(s).

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  • 29.
    Kebande, Victor R.
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Blockchain-Enabled Renewable Energy Traceability with a Crypto-based Arbitrage Pricing Model2023In: 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023 / [ed] Quwaider M., Awaysheh F.M., Jararweh Y., Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 34-41Conference paper (Refereed)
    Abstract [en]

    The need for Renewable Energy (RE) market decentralization and its rapid growth has led to new challenges related to energy traceability and pricing. While RE has undergone remarkable growth, the traditional methods of tracking renewable energy transactions lack transparency, making it difficult to ensure the authenticity of claims related to the source and quality of energy. Blockchain has been seen as a remarkable future technology capable of being integrated across many systems on the internet. This paper proposes a blockchainenabled solution to address these challenges by providing a secure and transparent traceability and pricing model for RE. The proposed approach uses blockchain technology to record and verify all energy transactions in a decentralized and tamper-proof manner. Additionally, the approach suggests the incorporation of smart contracts and Crypto-based Arbitrage techniques to automate the pricing of RE and the exploitation of a fair and efficient pricing mechanism. The paper goes the extra mile and presents an RE-based hypothetical case scenario that illustrates the implementation of the proposed model in an RE market while highlighting the benefits of using blockchain technology for energy traceability and pricing. The culminating discussions show that the blockchain-enabled RE traceability and pricing model offers a secure, transparent, and efficient pricing solution that can enhance trust in the RE market while promoting the transition to a sustainable energy future. © 2023 IEEE.

  • 30.
    Li, Rongyang
    et al.
    University of Science and Technology, China.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Ning, Huansheng
    University of Science and Technology, China.
    Emotion Arousal Assessment Based on Multimodal Physiological Signals for Game Users2023In: IEEE Transactions on Affective Computing, E-ISSN 1949-3045, Vol. 14, no 4, p. 2582-2594Article in journal (Refereed)
    Abstract [en]

    Emotional arousal, an essential dimension of game users' experience, plays a crucial role in determining whether a game is successful. Game users' emotion arousal assessment (GUEA) is of great importance. However, GUEA often faces challenges, such as selecting emotion-inducing games, labeling emotional arousal, and improving accuracy. In this study, the scheme for verifying the effectiveness of emotion-induced games is proposed so that the selected games can induce the target emotions. In addition, the personalized arousal label generation method is developed to reduce the errors caused by individual differences among subjects. Furthermore, to improve the accuracy of GUEA, the Breath Rate Variability (BRV) signal is used as a GUEA indicator along with commonly used physiological signals. Comparative experiments on GUEA based on multimodal physiological signals are conducted. The experimental result shows that the accuracy of GUEA is improved by adding the BRV signal, up to 92%. IEEE

  • 31.
    Modig, Dennis
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Performance impacts on container based virtualization in virtualized residential gateways2016In: Proceedings of 39th International Conference on Telecommunications and Signal Processing (TSP), 2016, IEEE , 2016, p. 27-32Conference paper (Refereed)
    Abstract [en]

    Over the past years the use of digital devices has increased and home networks continue to grow in size and complexity. By the use of virtualized residential gateways advanced functionality can be moved away from the home by extending the customers edge network to the Internet Service Provider (ISP) and thereby decrease the administrative burden for the home user. By employing edge computing and cloud applications at the operator by virtualizing residential gateways instead of using physical devices creates new challenges. This paper is looking at how the choice of virtualization technology impacts performance by investigating operating system level virtualization in contrast to full virtualization for use in virtualized residential gateways. Results show that container based virtualization uses fewer resources in terms of disk, memory, and processor in virtualized residential gateways.

  • 32.
    Mustapha, Khiati
    et al.
    University of Science and Technology Houari Boumediene, DZA.
    Djenouri, Djamel
    University of the West of England, GBR.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Djenouri, Youcef
    Sintef Digital, NOR.
    LSTM for Periodic Broadcasting in Green IoT Applications over Energy Harvesting Enabled Wireless Networks: Case Study on ADAPCAST2021In: 2021 17TH INTERNATIONAL CONFERENCE ON MOBILITY, SENSING AND NETWORKING (MSN 2021), IEEE, 2021, p. 694-699Conference paper (Refereed)
    Abstract [en]

    The present paper considers emerging Internet of Things (IoT) applications and proposes a Long Short Term Memory (LSTM) based neural network for predicting the end of the broadcasting period under slotted CSMA (Carrier Sense Multiple Access) based MAC protocol and Energy Harvesting enabled Wireless Networks (EHWNs). The goal is to explore LSTM for minimizing the number of missed nodes and the number of broadcasting time-slots required to reach all the nodes under periodic broadcast operations. The proposed LSTM model predicts the end of the current broadcast period relying on the Root Mean Square Error (RMSE) values generated by its output, which (the RMSE) is used as an indicator for the divergence of the model. As a case study, we enhance our already developed broadcast policy, ADAPCAST by applying the proposed LSTM. This allows to dynamically adjust the end of the broadcast periods, instead of statically fixing it beforehand. An artificial data-set of the historical data is used to feed the proposed LSTM with information about the amounts of incoming, consumed, and effective energy per time-slot, and the radio activity besides the average number of missed nodes per frame. The obtained results prove the efficiency of the proposed LSTM model in terms of minimizing both the number of missed nodes and the number of time-slots required for completing broadcast operations. © 2021 IEEE.

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  • 33.
    Ning, Huansheng
    et al.
    University of Science and Technology Beijing, CHN.
    Wang, Hang
    University of Science and Technology Beijing, CHN.
    Wang, Wenxi
    University of Science and Technology Beijing, CHN.
    Ye, Xiaozhen
    University of Science and Technology Beijing, CHN.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Backlund, Per
    University of Skovde, SWE.
    A Review on Serious Games in E-learning2021In: 2021 IEEE Symposium Series on Computational Intelligence, SSCI, IEEE, 2021Conference paper (Refereed)
    Abstract [en]

    E-Iearning is a widely used learning method, but with the development of society, traditional E-learning method has exposed some shortcomings, such as the boring way of teaching, so that it is difficult to increase the enthusiasm of students and raise their attention in class. The application of serious games in E-learning can make up for these shortcomings and effectively improve the quality of teaching. When applying serious games to E-learning, there are two main considerations: educational goals and game design. A successful serious game should organically combine the two aspects and balance the educational and entertaining nature of serious games. This paper mainly discusses the role of serious games in E-learning, various elements of game design, the classification of the educational goals of serious games and the relationship between educational goals and game design. In addition, we try to classify serious games and match educational goals with game types to provide guidance and assistance in the design of serious games. This paper also summarizes some shortcomings that serious games may have in the application of E-learning. © 2021 IEEE.

  • 34.
    Qammar, Attia
    et al.
    Univ Sci & Technol Beijing, CHN.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Ning, Huansheng
    Univ Sci & Technol Beijing, CHN.
    Federated learning attack surface: taxonomy, cyber defences, challenges, and future directions2022In: Artificial Intelligence Review, ISSN 0269-2821, E-ISSN 1573-7462, Vol. 55, no 5, p. 3569-3606Article in journal (Refereed)
    Abstract [en]

    Federated learning (FL) has received a great deal of research attention in the context of privacy protection restrictions. By jointly training deep learning models, a variety of training tasks can be competently performed with the help of invited participants. However, FL is concerned with a large number of attacks involving privacy and security aspects. This paper shows a federated learning workflow process and how a malicious client can exploit vulnerabilities in the FL system to attack the system. A systematic survey of existing research on the taxonomy of federated learning attack surface and the classification is presented. As with the FL attack surface, attackers compromise security, privacy, gain free incentives and abuse the Confidentiality, Integrity, and Availability (CIA) security triad. In addition, state-of-the-art defensive approaches against FL attacks are elaborated which help to protect and minimize the likelihood of attacks. FL models and tools for privacy attacks are explained, along with their best aspects and drawbacks. Finally, technical challenges and possible research guidelines are discussed as future work to build robust FL systems.

  • 35.
    Qammar, Attia
    et al.
    University of Science and Technology Beijing, CHN.
    Karim, Ahmad
    Bahauddin Zakariya University, PAK.
    Ning, Huansheng
    University of Science and Technology Beijing, CHN.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Securing federated learning with blockchain: a systematic literature review2023In: Artificial Intelligence Review, ISSN 0269-2821, E-ISSN 1573-7462, Vol. 56, no 5, p. 3951-3985Article, review/survey (Refereed)
    Abstract [en]

    Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. FL exploits the concept of collaborative learning and builds privacy-preserving models. Nevertheless, the integral features of FL are fraught with problems, such as the disclosure of private information, the unreliability of uploading model parameters to the server, the communication cost, etc. Blockchain, as a decentralized technology, is able to improve the performance of FL without requiring a centralized server and also solves the above problems. In this paper, a systematic literature review on the integration of Blockchain in federated learning was considered with the analysis of the existing FL problems that can be compensated. Through carefully screening, most relevant studies are included and research questions cover the potential security and privacy attacks in traditional federated learning that can be solved by blockchain as well as the characteristics of Blockchain-based FL. In addition, the latest Blockchain-based approaches to federated learning have been studied in-depth in terms of security and privacy, records and rewards, and verification and accountability. Furthermore, open issues related to the combination of Blockchain and FL are discussed. Finally, future research directions for the robust development of Blockchain-based FL systems are proposed.

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  • 36.
    Qammar, Attia
    et al.
    University of Science and Technology, Beijing,China.
    Naouri, Abdenacer
    University of Science and Technology, Beijing,China.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Ning, Huansheng
    University of Science and Technology, Beijing,China.
    Blockchain-based optimized edge node selection and privacy preserved framework for federated learning2023In: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543Article in journal (Refereed)
    Abstract [en]

    Federated learning is a distributed paradigm that trained large-scale neural network models with the participation of multiple edge nodes and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typical FL structure relies on the client–server architecture, which leads to the single-point-of-failure (SPoF) attack, and the random selection of edge devices for model training compromised the accuracy of the model. Furthermore, adversaries try to initiate inference attack i.e., attack on privacy leads to gradient leakage attack. Hence, we proposed a blockchain-based optimized edge node selection and privacy-preserved framework to address the aforementioned issues. We have designed three kinds of smart contracts (1) registration of edge nodes (2) forward bidding to select optimized edge devices for FL model training, and (3) payment settlement and reward smart contracts. Moreover, fully homomorphic encryption with the Cheon, Kim, Kim, and Song (CKKS) method is implemented before transmitting the local model updates to the server. Finally, we evaluated our proposed method on the benchmark dataset and compared it with other state-of-the-art studies. Consequently, we have achieved a higher accuracy and privacy-preserved FL framework with a decentralized nature. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

  • 37.
    Sarwatt, Doreen Sebastian
    et al.
    University of Science and Technology Beijing, China.
    Lin, Yujia
    University of Science and Technology Beijing, China.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Sun, Yunchuan
    Beijing Normal University, China.
    Ning, Huansheng
    University of Science and Technology Beijing, China.
    Metaverse for Intelligent Transportation Systems (ITS): A Comprehensive Review of Technologies, Applications, Implications, Challenges and Future Directions2024In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016Article in journal (Refereed)
    Abstract [en]

    Intelligent transportation systems (ITS) have made significant advancements in enhancing transportation safety, reliability, and efficiency. However, challenges persist in security, privacy, data management, and integration. Metaverse, an emerging technology enabling immersive and simulated experiences, presents promising solutions to overcome these challenges. By establishing secure communication channels, facilitating virtual simulations for safe testing and training, and enabling centralized data management with real-time analytics, metaverse offers a transformative approach to address these challenges. While metaverse has found extensive applications across industries, its potential in transportation remains largely untapped. This comprehensive review delves into the integration of the metaverse in ITS, exploring key technologies like virtual reality, digital twin, blockchain, and artificial intelligence, and their specific applications in the context of ITS. Real-world case studies, research projects, and initiatives are compiled to showcase the metaverse’s potential for ITS. It also examines the societal, economic, and technological implications of metaverse integration in ITS and highlights the associated integration challenges. Lastly, future research directions are identified to unlock the metaverse’s full potential in enhancing transportation systems. IEEE

  • 38.
    Wang, Hang
    et al.
    University of Science and Technology Beijing, China.
    Ning, Huansheng
    University of Science and Technology Beijing, China.
    Lin, Yuija
    University of Science and Technology Beijing, China.
    Wang, Wenxi
    University of Science and Technology Beijing, China.
    Dhelim, Sahraoui
    University College Dublin, Ireland.
    Farha, Fadi
    Aleppo University, Syria.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Daneshmand, Mahmoud
    Stevens Institute of Technology, USA.
    A Survey on the Metaverse: The State-of-the-Art, Technologies, Applications, and Challenges2023In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, no 16, p. 14671-14688Article in journal (Refereed)
    Abstract [en]

    In recent years, the concept of the Metaverse has attracted considerable attention. This paper provides a comprehensive overview of the Metaverse. First, the development status of the Metaverse is presented. We summarize the policies of various countries, companies, and organizations relevant to the Metaverse, as well as statistics on the number of Metaverse-related publications. Characteristics of the Metaverse are identified: 1) multi-technology convergence; 2) sociality; 3) hyper-spatio-temporality. For the multi-technology convergence of the Metaverse, we divide the technological framework of the Metaverse into five dimensions. For the sociality of the Metaverse, we focus on the Metaverse as a virtual social world. Regarding the characteristic of hyper-spatio-temporality, we introduce the Metaverse as an open, immersive, and interactive 3D virtual world which can break through the constraints of time and space in the real world. The challenges of the Metaverse are also discussed. IEEE

  • 39.
    Wassie, Getahun
    et al.
    Addis Ababa University, Ethiopia.
    Ding, Jianguo
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Wondie, Yihenew
    Addis Ababa University, Ethiopia.
    Traffic prediction in SDN for explainable QoS using deep learning approach2023In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1, article id 20607Article in journal (Refereed)
    Abstract [en]

    The radical increase of multimedia applications such as voice over Internet protocol (VOIP), image processing, and video-based applications require better quality of service (QoS). Therefore, traffic Predicting and explaining the prediction models is essential. However, elephant flows from those applications still needs to be improved to satisfy Internet users. Elephant flows lead to network congestion, resulting in packet loss, delay and inadequate QoS delivery. Recently, deep learning models become a good alternative for real-time traffic management. This research aims to design a traffic predicting model that can identify elephant flows to prevent network congestion in advance. Thus, we are motivated to develop elephant flow prediction models and explain those models explicitly for network administrators’ use in the SDN network. H2O, Deep Autoencoder, and autoML predicting algorithms, including XGBoost, GBM and GDF, were employed to develop the proposed model. The performance of Elephant flow prediction models scored 99.97%, 99.99%, and 100% in validation accuracy of under construction error of 0.0003952, 0.001697, and 0.00000408 using XGBoost, GBM, and GDF algorithms respectively. The models were also explicitly explained using Explainable Artificial Intelligence. Accordingly, packet size and byte size attributes need much attention to detect elephant flows. © 2023, The Author(s).

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  • 40.
    Ye, Xiaozhen
    et al.
    School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
    Backlund, Per
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ning, Huansheng
    School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
    Fidelity in Simulation-based Serious Games2020In: IEEE Transactions on Learning Technologies, ISSN 1939-1382, E-ISSN 1939-1382, Vol. 13, no 2, p. 340-353Article in journal (Refereed)
    Abstract [en]

    The extensive use of Simulation-based Serious Games (SSGs) has made a revolution in educational techniques. As a potentially significant feature for SSG design and evaluation, the term fidelity (the similarity between an SSG and its real reference) emerges and attracts increasing attention. The study of fidelity not only benefits the design, development, and analysis of an SSG with the consideration of improving the learning effect but also contributes to the investment reduction of an SSG. However, the term fidelity is used inconsistently in current literature. The introduction of new technologies (e.g. virtual reality) and the blend of multiform SSGs also facilitate the extension of fidelity with new connotations. All lead to confusing concepts and vague measure metrics. Besides, the relationship between fidelity and learning effect is still uncertain. A new vision and a comprehensive conceptual framework of fidelity for more general applications are in need. In this paper, further exploration and discussion of these issues in relation to fidelity of SSGs are presented through a systematic review. A general conceptual framework considering both aspects of the SSG system itself and the learners is developed and applied to analyze fidelity in SSGs. Based on that, a discussion on fidelity related issues of SSG design and development is presented.

  • 41.
    Ye, Xiaozhen
    et al.
    School of Computer and Communication Engineering, University of Science and Technology Beijing, China / Shunde Graduate School, University of Science and Technology Beijing, China.
    Ning, Huansheng
    School of Computer and Communication Engineering, University of Science and Technology Beijing, China / Shunde Graduate School, University of Science and Technology Beijing, China.
    Backlund, Per
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Flow Experience Detection and Analysis for Game Users by Wearable-Devices-Based Physiological Responses Capture2021In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 8, no 3, p. 1373-1387Article in journal (Refereed)
    Abstract [en]

    Relevant research has shown the potential to understand the game user experience (GUX) more accurately and reliably by measuring the user’s psychophysiological responses. However, the current studies are still very scarce and limited in scope and depth. Besides, the low-detection accuracy and the common use of the professional physiological signal apparatus make it difficult to be applied in practice. This article analyzes the GUX, particularly flow experience, based on users’ physiological responses, including the galvanic skin response (GSR) and heart rate (HR) signals, captured by low-cost wearable devices. Based on the collected data sets regarding two test games and the mixed data set, several classification models were constructed to detect the flow state automatically. Hereinto, two strategies were proposed and applied to improve classification performance. The results demonstrated that the flow experience of game users could be effectively classified from other experiences. The best accuracies of two-way classification and three-way classification under the support of the proposed strategies were over 90% and 80%, respectively. Specifically, the comparison test with the existing results showed that Strategy1 could significantly reduce the negative interference of individual differences in physiological signals and improve the classification accuracy. In addition, the results of the mixed data set identified the potential of a general classification model of flow experience.

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