387 results found
Du J, Gelenbe E, Jiang C, et al., 2019, Peer Prediction-Based Trustworthiness Evaluation and Trustworthy Service Rating in Social Networks, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, Vol: 14, Pages: 1582-1594, ISSN: 1556-6013
Nalin M, Baroni I, Faiella G, et al., 2019, The European cross-border health data exchange roadmap: case study in the Italian setting, Journal of Biomedical Informatics, Vol: 94, ISSN: 1532-0464
Health data exchange is a major challenge due to the sensitive information and the privacy issues entailed. Considering the European context, in which health data must be exchanged between different European Union (EU) Member States, each having a different national regulatory framework as well as different national healthcare structures, the challenge appears even greater. Europe has tried to address this challenge via the epSOS (“Smart Open Services for European Patients”) project in 2008, a European large-scale pilot on cross-border sharing of specific health data and services. The adoption of the framework is an ongoing activity, with most Member States planning its implementation by 2020. Yet, this framework is quite generic and leaves a wide space to each EU Member State regarding the definition of roles, processes, workflows and especially the specific integration with the National Infrastructures for eHealth. The aim of this paper is to present the current landscape of the evolving eHealth infrastructure for cross-border health data exchange in Europe, as a result of past and ongoing initiatives, and illustrate challenges, open issues and limitations through a specific case study describing how Italy is approaching its adoption and accommodates the identified barriers. To this end, the paper discusses ethical, regulatory and organizational issues, also focusing on technical aspects, such as interoperability and cybersecurity. Regarding cybersecurity aspects per se, we present the approach of the KONFIDO EU-funded project, which aims to reinforce trust and security in European cross-border health data exchange by leveraging novel approaches and cutting-edge technologies, such as homomorphic encryption, photonic Physical Unclonable Functions (p-PUF), a Security Information and Event Management (SIEM) system, and blockchain-based auditing. In particular, we explain how KONFIDO will test its outcomes through a dedicated pilot based on a realistic sce
Bi H, Gelenbe E, 2019, A Survey of Algorithms and Systems for Evacuating People in Confined Spaces, ELECTRONICS, Vol: 8, ISSN: 2079-9292
Du J, Jiang C, Gelenbe E, et al., 2019, Double Auction Mechanism Design for Video Caching in Heterogeneous Ultra-Dense Networks, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, Vol: 18, Pages: 1669-1683, ISSN: 1536-1276
Gelenbe E, Kadioglu YM, 2019, Product-form solution for cascade networks with intermittent energy, IEEE Systems Journal, Vol: 13, Pages: 918-927, ISSN: 1932-8184
The power needs of digital devices, their installation in locations where it is difficult to connect them to the power grid and the difficulty of frequently replacing batteries, create the need to operate digital systems with harvested energy. In such cases, local storage batteries must overcome the intermittent nature of the energy supply. System performance then depends on the intermittent energy supply, possible energy leakage, and system workload. Queueing networks with product-form solution (PFS) are standard tools for analyzing the performance of interconnected systems, and predicting relevant performance metrics including job queue lengths, throughput, and system turnaround times and delays. However, existing queueing network models assume unlimited energy availability, whereas intermittently harvested energy can affect system performance due to insufficient energy supply. Thus, this paper develops a new PFS for the joint probability distribution of energy availability, and job queue length for an N-node tandem system. Such models can represent production lines in manufacturing systems, supply chains, cascaded repeaters for optical links, or a data link with multiple input data ports that feeds into a switch or server. Our result enables the rigorous computation of the relevant performance metrics of such systems operating with intermittent energy.
Gelenbe E, Zhang Y, Performance optimization with energy packets, IEEE Systems Journal, ISSN: 1932-8184
We investigate how the flow of energy and the flow of jobs in a service system can be used to minimize the average response time to jobs that arrive according to random arrival processes at the servers. An interconnected system of workstations and energy storage units that are fed with randomly arriving harvested energy is analyzed by means of the Energy Packet Network (EPN) model. The system state is discretized, and uses discrete units to represent the backlog of jobs at the workstations, and the amount of energy that is available at the energy storage units. An Energy Packet (EP) which is the unit of energy, can be used to process one or more jobs at a workstation, and an EP can also be expended to move a job from one workstation to another one. The system is modeled as a probabilistic network that has a product-form solution for the equilibrium probability distribution of system state. The EPN model is used to solve two problems related to using the flow of energy and jobs in a multi-server system, so as to minimize the average response time experienced by the jobs that arrive at the system.
Buyya R, Srirama S, Casale G, et al., 2019, A manifesto for future generation cloud computing: research directions for the next decade, ACM Computing Surveys, Vol: 51, ISSN: 0360-0300
The Cloud computing paradigm has revolutionised the computer science horizon during the past decade andhas enabled the emergence of computing as the fifth utility. It has captured significant attention of academia,industries, and government bodies. Now, it has emerged as the backbone of modern economy by offeringsubscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorterestablishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-valueassociativity for scientific and high performance computing applications, and (4) different invocation/executionmodels for pervasive and ubiquitous applications. The recent technological developments and paradigms suchas serverless computing, software-defined networking, Internet of Things, and processing at network edgeare creating new opportunities for Cloud computing. However, they are also posing several new challengesand creating the need for new approaches and research strategies, as well as the re-evaluation of the modelsthat were developed to address issues such as scalability, elasticity, reliability, security, sustainability, andapplication models. The proposed manifesto addresses them by identifying the major open challenges inCloud computing, emerging trends, and impact areas. It then offers research directions for the next decade,thus helping in the realisation of Future Generation Cloud Computing.
Du J, Jiang C, Gelenbe E, et al., 2018, DISTRIBUTED DATA PRIVACY PRESERVATION IN IOT APPLICATIONS, IEEE WIRELESS COMMUNICATIONS, Vol: 25, Pages: 68-76, ISSN: 1536-1284
Natsiavas P, Rasmussen J, Voss-Knude M, et al., 2018, Comprehensive user requirements engineering methodology for secure and interoperable health data exchange, BMC Medical Informatics and Decision Making, Vol: 18, ISSN: 1472-6947
BackgroundIncreased digitalization of healthcare comes along with the cost of cybercrime proliferation. This results to patients’ and healthcare providers' skepticism to adopt Health Information Technologies (HIT). In Europe, this shortcoming hampers efficient cross-border health data exchange, which requires a holistic, secure and interoperable framework. This study aimed to provide the foundations for designing a secure and interoperable toolkit for cross-border health data exchange within the European Union (EU), conducted in the scope of the KONFIDO project. Particularly, we present our user requirements engineering methodology and the obtained results, driving the technical design of the KONFIDO toolkit.MethodsOur methodology relied on four pillars: (a) a gap analysis study, reviewing a range of relevant projects/initiatives, technologies as well as cybersecurity strategies for HIT interoperability and cybersecurity; (b) the definition of user scenarios with major focus on cross-border health data exchange in the three pilot countries of the project; (c) a user requirements elicitation phase containing a threat analysis of the business processes entailed in the user scenarios, and (d) surveying and discussing with key stakeholders, aiming to validate the obtained outcomes and identify barriers and facilitators for HIT adoption linked with cybersecurity and interoperability.ResultsAccording to the gap analysis outcomes, full adherence with information security standards is currently not universally met. Sustainability plans shall be defined for adapting existing/evolving frameworks to the state-of-the-art. Overall, lack of integration in a holistic security approach was clearly identified. For each user scenario, we concluded with a comprehensive workflow, highlighting challenges and open issues for their application in our pilot sites. The threat analysis resulted in a set of 30 user goals in total, documented in detail. Finally, indicative barriers of HI
Grenet I, Yin Y, Comet J-P, et al., 2018, Machine learning to predict toxicity of compounds, 27th International Conference on Artificial Neural Networks (ICANN), Publisher: Springer, Pages: 335-345, ISSN: 0302-9743
Toxicology studies are subject to several concerns, and they raise the importance of an early detection of the potential for toxicity of chemical compounds which is currently evaluated through in vitro assays assessing their bioactivity, or using costly and ethically questionable in vivo tests on animals. Thus we investigate the prediction of the bioactivity of chemical compounds from their physico-chemical structure, and propose that it be automated using machine learning (ML) techniques based on data from in vitro assessment of several hundred chemical compounds. We provide the results of tests with this approach using several ML techniques, using both a restricted dataset and a larger one. Since the available empirical data is unbalanced, we also use data augmentation techniques to improve the classification accuracy, and present the resulting improvements.
Staffa M, Sgaglione L, Mazzeo G, et al., 2018, An OpenNCP-based Solution for Secure eHealth Data Exchange, JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, Vol: 116, Pages: 65-85, ISSN: 1084-8045
Kadioglu YM, Gelenbe E, Product-Form Solution for Cascade Networks With Intermittent Energy, IEEE Systems Journal, ISSN: 1932-8184
Gelenbe E, Abdelrahman OH, An Energy Packet Network Model for Mobile Networks with Energy Harvesting, NOLTA, Nonlinear Theory and Its Applications, IEICE (Japan), ISSN: 2185-4106
Domanska J, Gelenbe E, Czachorski T, et al., 2018, Research and innovation action for the security of the internet of things: The SerIoT project, 32nd International Symposium on Computer and Information Sciences (ISCIS) on Cybersecurity (Euro-CYBERSEC), Publisher: Springer-Verlag Berlin, Pages: 101-118, ISSN: 1865-0929
The Internet of Things (IoT) was born in the mid 2010’s, when the threshold of connecting more objects than people to the Internet, was crossed. Thus, attacks and threats on the content and quality of service of the IoT platforms can have economic, energetic and physical security consequences that go way beyond the traditional Internet’s lack of security, and way beyond the threats posed by attacks to mobile telephony. Thus, this paper describes the H2020 project “Secure and Safe Internet of Things” (SerIoT) which will optimize the information security in IoT platforms and networks in a holistic, cross-layered manner (i.e. IoT platforms and devices, honeypots, SDN routers and operator’s controller) in order to offer a secure SerIoT platform that can be used to implement secure IoT platforms and networks anywhere and everywhere.
Staffa M, Coppolino L, Sgaglione L, et al., 2018, KONFIDO: An OpenNCP-based secure eHealth data exchange system, 32nd International Symposium on Computer and Information Sciences (ISCIS) on Cybersecurity (Euro-CYBERSEC), Publisher: Springer-Verlag Berlin, Pages: 11-27, ISSN: 1865-0929
Allowing cross-border health-care data exchange by establishing a uniform QoS level of health-care systems across European states, represents one of the current main goals of the European Commission. For this purpose epSOS project was funded with the objective to overcome interoperability issues in patients health information exchange among European healthcare systems. A main achievement of the project was the OpenNCP platform. Settled over the results of the epSOS project, KONFIDO aims at increasing trust and security of eHealth data exchange by adopting a holistic approach, as well as at increasing awareness of security issues among the healthcare community. In this light, the paper describes the KONFIDO project’s approach and discusses its design and its representation as a system of interacting agents. It finally discusses the deployment of the provided platform.
Collen A, Nijdam NA, Augusto-Gonzalez J, et al., 2018, GHOST - safe-guarding home IoT environments with personalised real-time risk control, 32nd International Symposium on Computer and Information Sciences (ISCIS) on Cybersecurity (Euro-CYBERSEC), Publisher: Springer-Verlag Berlin, Pages: 68-78, ISSN: 1865-0929
We present the European research project GHOST, (Safe-guarding home IoT environments with personalised real-time risk control), which challenges the traditional cyber security solutions for the IoT by proposing a novel reference architecture that is embedded in an adequately adapted smart home network gateway, and designed to be vendor-independent. GHOST proposes to lead a paradigm shift in consumer cyber security by coupling usable security with transparency and behavioural engineering.
Siavvas M, Gelenbe E, Kehagias D, et al., 2018, Static analysis-based approaches for secure software development, 32nd International Symposium on Computer and Information Sciences (ISCIS) on Cybersecurity (Euro-CYBERSEC), Publisher: Springer-Verlag Berlin, Pages: 142-157, ISSN: 1865-0929
Software security is a matter of major concern for software development enterprises that wish to deliver highly secure software products to their customers. Static analysis is considered one of the most effective mechanisms for adding security to software products. The multitude of static analysis tools that are available provide a large number of raw results that may contain security-relevant information, which may be useful for the production of secure software. Several mechanisms that can facilitate the production of both secure and reliable software applications have been proposed over the years. In this paper, two such mechanisms, particularly the vulnerability prediction models (VPMs) and the optimum checkpoint recommendation (OCR) mechanisms, are theoretically examined, while their potential improvement by using static analysis is also investigated. In particular, we review the most significant contributions regarding these mechanisms, identify their most important open issues, and propose directions for future research, emphasizing on the potential adoption of static analysis for addressing the identified open issues. Hence, this paper can act as a reference for researchers that wish to contribute in these subfields, in order to gain solid understanding of the existing solutions and their open issues that require further research.
Serrano W, Gelenbe E, 2018, The Random Neural Network in a neurocomputing application for Web search, NEUROCOMPUTING, Vol: 280, Pages: 123-134, ISSN: 0925-2312
Du J, Gelenbe E, Jiang C, et al., 2018, Data transaction modeling in mobile networks: contract mechanism and performance analysis, IEEE Global Communications Conference (GLOBECOM), Publisher: IEEE, ISSN: 2334-0983
We consider auction mechanism design and performance analysis for data transactions in mobile social networks. Existing mobile network plans can result in some users ending a monthly plan with excess data, while others may have to pay a costly fee to buy more data. Thus we suggest data auctions with a single seller, or a multiple-seller networked data auction, that operate in mobile social networks, to deal with the asymmetry between extra unused data resources and urgent data demands. Based on earlier work on the analysis of auctions, we design the data transaction mechanism, and summarise the analysis on state transmission, stationary probabilities of the system, and the expected income for data sellers. To improve the efficiency and performance of the system, socially- aware mobility models are also proposed. The proposed data auction mechanisms and friendship-based mobility model are then simulated as operating on Flickr, a real-world online social network database. Results show that the number of data bidders in different auctions can be balanced through the proposed mobility model, and also increase the income per unit time of sellers in the networked data auction.
Brun O, Yin Y, Gelenbe E, et al., 2018, Deep Learning with Dense Random Neural Networks for Detecting Attacks Against IoT-Connected Home Environments, 32nd International Symposium on Computer and Information Sciences (ISCIS) on Cybersecurity (Euro-CYBERSEC), Publisher: SPRINGER-VERLAG BERLIN, Pages: 79-89, ISSN: 1865-0929
Serrano W, Gelenbe E, 2018, The Deep Learning Random Neural Network with a Management Cluster, 9th KES International Conference on Intelligent Decision Technologies (KES-IDT), Publisher: SPRINGER-VERLAG BERLIN, Pages: 185-195, ISSN: 2190-3018
Gelenbe E, Yin Y, 2018, Deep Learning with Random Neural Networks, SAI Annual Conference on Areas of Intelligent Systems and Artificial Intelligence and their Applications to the Real World (IntelliSys), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 450-462, ISSN: 2367-3370
Du J, Gelenbe E, Jiang C, et al., 2018, Cognitive Data Allocation for Auction-based Data Transaction in Mobile Networks, 14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), Publisher: IEEE, Pages: 207-212, ISSN: 2376-6492
Du J, Jiang C, Gelenbe E, et al., 2018, Networked Data Transaction in Mobile Networks: A Prediction-based Approach Using Auction, 14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), Publisher: IEEE, Pages: 201-206, ISSN: 2376-6492
Gelenbe E, Kadioglu YM, 2018, Energy Life-Time of Wireless Nodes with Network Attacks and Mitigation, IEEE International Conference on Communications (ICC), Publisher: IEEE, ISSN: 2164-7038
Du J, Gelenbe E, Jiang C, et al., 2017, Contract design for traffic offloading and resource allocation in heterogeneous ultra-dense networks, IEEE Journal on Selected Areas in Communications, Vol: 35, Pages: 2457-2467, ISSN: 0733-8716
In heterogeneous ultra-dense networks (HetUDNs), the software-defined wireless network (SDWN) separates resource management from geo-distributed resources belonging to different service providers. A centralized SDWN controller can manage the entire network globally. In this work, we focus on mobile traffic offloading and resource allocation in SDWN-based HetUDNs, constituted of different macro base stations (MBSs) and small-cell base stations (SBSs). We explore a scenario where SBSs’ capacities are available, but their offloading performance is unknown to the SDWN controller: this is the information asymmetric case. To address this asymmetry, incentivized traffic offloading contracts are designed to encourage each SBS to select the contract that achieves its own maximum utility. The characteristics of large numbers of SBSs in HetUDNs are aggregated in an analytical model, allowing us to select the SBS types that provide the off-loading, based on different contracts which offer rationality and incentive compatibility to different SBS types. This leads to a closed-form expression for selecting the SBS types involved, and we prove the monotonicity and incentive compatibility of the resulting contracts. The effectiveness and efficiency of the proposed contract-based traffic offloading mechanism, and its overall system performance, are validated using simulations.
Gelenbe E, Yin Y, 2017, Deep learning with dense random neural networks, 5th International Conference on Man-Machine Interactions (ICMMI), Publisher: Springer, Pages: 3-18, ISSN: 2194-5357
We exploit the dense structure of nuclei to postulate that in such clusters, the neuronal cells will communicate via soma-to-soma interactions, aswell as through synapses. Using the mathematical structure of the spiking Random Neural Network, we construct a multi-layer architecture for Deep Learning. An efficient training procedure is proposed for this architecture. It is then specialized to multi-channel datasets, and applied to images and sensor-based data.
Fourneau J-M, Gelenbe E, 2017, G-Networks with Adders, Future Internet, Vol: 9, ISSN: 1999-5903
Queueing networks are used to model the performance of the Internet, of manufacturing and job-shop systems, supply chains, and other networked systems in transportation or emergency management. Composed of service stations where customers receive service, and then move to another service station till they leave the network, queueing networks are based on probabilistic assumptions concerning service times and customer movement that represent the variability of system workloads. Subject to restrictive assumptions regarding external arrivals, Markovian movement of customers, and service time distributions, such networks can be solved efficiently with “product form solutions” that reduce the need for software simulators requiring lengthy computations. G-networks generalise these models to include the effect of “signals” that re-route customer traffic, or negative customers that reject service requests, and also have a convenient product form solution. This paper extends G-networks by including a new type of signal, that we call an “Adder”, which probabilistically changes the queue length at the service center that it visits, acting as a load regulator. We show that this generalisation of G-networks has a product form solution.
Kadioglu YM, Gelenbe E, 2017, Wireless sensor with data and Energy Packets, 2017 IEEE International Conference on Communications Workshops (ICC Workshops), Publisher: IEEE, Pages: 564-569, ISSN: 2474-9133
This paper develops a mathematical model to determine the balance of energy input and data sensing and transmission in a wireless sensing node. Since the node acquires energy through harvesting from an intermittent source, and sensing is also carried out intermittently, the node is modelled with random arrivals of both energy and data. A buffer in the node stores data packets while energy is stored in a battery acting as an energy buffer. The approach uses the “Energy Packet Network” paradigm so that both energy and data packets can be modelled as discrete quantities. We assume that for each data packet, the sensor consumes K e energy packets for node electronics including sensing, processing, and storing and K t energy packets for transmission. We model the node's energy and data flow by a two-dimensional random walk which represents the backlog of data and energy packets. We then simplify the model using companion matrices and matrix algebra techniques that allow us to obtain a closed-form solution for the stationary probability distribution for the random walk which allows us to compute important performance measures, including the energy consumed by the node, and its throughput in data packets transmitted as a function of the amount of power that it receives. The model also allows us to evaluate the effect of ambient noise and the needs for data retransmissions, including for the case where M sensors operate in proximity and create interference for each other.
Yin Y, Gelenbe E, 2017, Single-Cell Based Random Neural Network for Deep Learning, International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, Pages: 86-93, ISSN: 2161-4393
Recent work demonstrated the value of multi clusters of spiking Random Neural Networks (RNN) with dense soma-to-soma interactions in deep learning. In this paper we go back to the original simpler structure and we investigate the power of single RNN cells for deep learning. First, we consider three approaches with the single cells, twin cells and multi-cell clusters. This first part shows that RNNs with only positive parameter can conduct convolution operations similar to those of the convolutional neural network. We then develop a multi-layer architecture of single cell RNNs (MLSRNN), and show that this architecture achieves comparable or better classification at lower computation cost than conventional deep-learning methods.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.