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Energy Efficient Decentralized Authentication in Internet of Underwater Things Using Blockchain

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Energy Efficient Decentralized Authentication in Internet of...

IEEE Globecom Workshops

作者： Abbas Yazdinejad Reza M. Parizi Gautam Srivastava Ali Dehghantanha Kim-Kwang Raymond Choo Cyber Science Lab School of Computer Science University of Guelph Ontario Canada College of Computing and Software Engineering Kennesaw State University GA USA Department of Mathematics and Computer Science Brandon University Manitoba Canada Department of Information Systems and Cyber Security University of Texas at San Antonio TX USA

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

In recent years, there has been rapid growth in developing smart cities. Nearly 70% of the Earthâ€™s surface is covered by water and a large proportion of underwater environments are still unknown and have not been explored. In this context, Internet of things (IoT) is one of the most important technologies used in smart cities. Due to the growth of IoT and its influence in all areas of human life, including the underwater environment, a new class of IoT, called Internet of underwater things (IoUT) has emerged. IoUT includes a network of underwater smart devices that are connected to each other and has applications in environmental monitoring, underwater exploration, disaster prevention and military. In autonomous interactions of underwater devices, objects must be authenticated and securely interconnected to avoid security risks by malicious nodes. Most authentication methods and security mechanisms are centralized and often require a trustful third party in communications, which may well increase the computation cost and energy consumption due to the subsequent overhead, especially for underwater communications. On the other hand, there are restrictions on devices in the underwater environment, the most important of which are energy constraints. In this paper, we propose a robust, transparent, and energy-efficient decentralized authentication mechanism for IoUT using blockchain technology. We show through results that the proposed method is suitable for underwater devices with limited memory, energy, and computational power. The proposed modelâ€™s decentralized authentication in a cluster network has a significant effect on reducing the energy consumption of the devices by 74.63% compared to classic authentication methods. Moreover, using the proposed method allows a savings of more than 41.9% in end-to-end delay and increases delivery rate by 21.6%.

关键词： Authentication Internet of Things Smart cities Energy consumption Cryptography

software module clustering based on the fuzzy adaptive teaching learning based optimization algorithm

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arXiv 2019年

作者： Zamli, Kamal Z. Din, Fakhrud Ramli, Nazirah Ahmed, Bestoun S. Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Gambang Kuantan Pahang26300 Malaysia Department of Computer Science and IT Faculty of Information Technology University of Malakand KPK Pakistan Department of Mathematics and Statistics Faculty of Information Technology and Quantitative Science Universiti Teknologi MARA Pahang Malaysia Faculty of Electrical Engineering Department of Computer Science Czech Technical University Czech Republic

Although showing competitive performances in many real world optimization problems, Teaching Learning based Optimization Algorithm (TLBO) has been criticized for having poor control on exploration and exploitation. Addressing these issues, a new variant of TLBO called Adaptive Fuzzy Teaching Learning based Optimization (ATLBO) has been developed in the literature. This paper describes the adoption of Fuzzy Adaptive Fuzzy Teaching Learning based Optimization (ATLBO) for software module clustering problem. Comparative studies with the original Teaching Learning based Optimization (TLBO) and other Fuzzy TLBO variant demonstrate that ATLBO gives superior performance owing to its adaptive selection of search operators based on the need of the current search. Copyright © 2019, The Authors. All rights reserved.

关键词： Optimization

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

Code-aware combinatorial interaction testing

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arXiv 2019年

作者： Ahmed, Bestoun S. Gargantini, Angelo Zamli, Kamal Z. Yilmaz, Cemal Bures, Miroslav Szeles, Marek Department of Mathematics and Computer Science Karlstad University Karlstad651 88 Sweden Department of Management Information and Production Engineering University of Bergamo Italy Faculty of Computer Systems and Software Engineering University Malaysia Pahang Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Karlovo nam. 13 Praha 2121 35 Czech Republic

Combinatorial interaction testing (CIT) is a useful testing technique to ad- dress the interaction of input parameters in software systems. In many ap- plications, the technique has been used as a systematic sampling technique to sample the enormous possibilities of test cases. In the last decade, most of the research activities focused on the generation of CIT test suites as it is a computationally complex problem. Although promising, less effort has been paid for the application of CIT. In general, to apply the CIT, practitioners must identify the input parameters for the software-under-test (SUT), feed these parameters to the CIT tool to generate the test suite, and then run those tests on the application with some pass and fail criteria for verification. Using this approach, CIT is used as a black-box testing technique without knowing the effect of the internal code. Although useful, practically, not all the parameters having the same impact on the SUT. This paper intro- duces a different approach to use the CIT as a gray-box testing technique by considering the internal code structure of the SUT to know the impact of each input parameter and thus use this impact in the test generation stage. We applied our approach to five reliable case studies. The results showed that this approach would help to detect new faults as compared to the equal impact parameter approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Black-box testing

The occupation kernel method for nonlinear system identification

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arXiv 2019年

作者： Rosenfeld, Joel A. Russo, Benjamin Kamalapurkar, Rushikesh Johnson, Taylor T. Department of Mathematics and Statistics University of South Florida TampaFL33620 United States Department of Mathematics Farmingdale State College FarmingdaleNY11735 United States Department of Mechanical and Aerospace Engineering Oklahoma State University StillwaterOK74078 United States Institute for Software Integrated Systems Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37212 United States

This manuscript presents a novel approach to nonlinear system identification leveraging densely defined Liouville operators and a new "kernel" function that represents an integration functional over a reproducing kernel Hilbert space (RKHS) dubbed an occupation kernel. The manuscript thoroughly explores the concept of occupation kernels in the contexts of RKHSs of continuous functions, and establishes Liouville operators over RKHS where several dense domains are found for specific examples of this unbounded operator. The combination of these two concepts allow for the embedding of a dynamical system into a RKHS, where function theoretic tools may be leveraged for the examination of such systems. This framework allows for trajectories of a nonlinear dynamical system to be treated as a fundamental unit of data for nonlinear system identification routine. The approach to nonlinear system identification is demonstrated to identify parameters of a dynamical system accurately, while also exhibiting a certain robustness to noise. Copyright © 2019, The Authors. All rights reserved.

关键词： Employment

Incident detection based on mobile crowd sensing for smart city

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Journal of computers (Taiwan) 2019年第1期30卷 96-104页

作者： Zhang, Peng Zhang, Zhenjiang Chao, Han-Chieh Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China Department of Software Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Computer Science and Information Engineering National Ilan University Yilan County260 Taiwan

In recent years, mobile devices have taken a significant role in improving the quality of people’s life. In order to enhance the usability of those devices, more and more sensors have been built in. Furthermore, the data storage and data processing capacity are becoming larger and larger. To capture more information about the city conditions and make full use of the computing resource, mobile crowd sensing has been proposed. Incident detection is an important component of the safety monitoring system, however, existing incident detection methods are mostly based on computer vision which is vulnerability to environmental impacts. This paper proposes an automatic incident detection model based on Mobile Crowd Sensing to deal with the problem above. The incident detection algorithm in the model is based on the human activity recognition and the crowd density. The simulation results show that this model is feasible and effective. 2019 © computer Society of the Republic of China. All Rights Reserved.

关键词： Environmental impact

Ensemble of Learning Project Productivity in software Effort Based on Use Case Points

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arXiv 2018年

作者： Azzeh, Mohammad Nassif, Ali Bou Banitaan, Shadi López-Martín, Cuauhtémoc Department of Software Engineering Applied Science Private University Amman Jordan Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy United States Department of Information Systems Universidad de Guadalajara Mexico

It is well recognized that the project productivity is a key driver in estimating software project effort from Use Case Point size metric at early software development stages. Although, there are few proposed models for predicting productivity, there is no consistent conclusion regarding which model is the superior. Therefore, instead of building a new productivity prediction model, this paper presents a new ensemble construction mechanism applied for software project productivity prediction. Ensemble is an effective technique when performance of base models is poor. We proposed a weighted mean method to aggregate predicted productivities based on average of errors produced by training model. The obtained results show that the using ensemble is a good alternative approach when accuracies of base models are not consistently accurate over different datasets, and when models behave diversely. Copyright © 2018, The Authors. All rights reserved.

关键词： Productivity

A Proof-of-Concept Model Demonstration of System Level Cyber Attacks on Availability

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A Proof-of-Concept Model Demonstration of System Level Cyber...

IEEE SoutheastCon

作者： Uma Kannan Rajendran Swamidurai David Umphress Department of Computer Information Systems Alabama State University Montgomery AL USA Department of Mathematics and Computer Science Alabama State University Montgomery AL USA Department of Computer Science and Software Engineering Auburn University Auburn AL USA

ISBN: (纸本)9781538661345

In this study we have modeled a cyber security situation and demonstrated the system level effects using system dynamics PoC (Proof-of-Concept) model. We modeled a hypothetical Company's Information Technology infrastructure and simulated a Distributed Denial-of-Service attack on one part of the system in an effort to see how other parts of the system were affected. To validate our simulation model, we developed a cybersecurity testbed and conducted a cyber-attack on one application and observed its impact on the whole system. The results indicate that by using known vulnerabilities and the current knowledge about infrastructure and security controls, the system dynamic cyber security simulation modeling allows an organization to imitate the attacker activities and helps to assess and mitigate the system's risk exposure.

关键词： Cybersecurity system dynamics denial-of-service attack continuous simulation cybersecurity simulation

ν-SVR polynomial kernel for predicting the defect density in new software projects

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arXiv 2018年

作者： López-Martín, Cuauhtémoc Azzeh, Mohammad Nassif, Ali Bou Banitaan, Shadi Department of Information Systems Universidad de Guadalajara Mexico Department of Software Engineering Applied Science Private University Amman Jordan Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States

An important product measure to determine the effectiveness of software processes is the defect density (DD). In this study, we propose the application of support vector regression (SVR) to predict the DD of new software projects obtained from the International software Benchmarking Standards Group (ISBSG) Release 2018 data set. Two types of SVR (i.e., Ε-SVR and ν-SVR) were applied to train and test these projects. Each SVR used four types of kernels. The prediction accuracy of each SVR was compared to that of a statistical regression (i.e., a simple linear regression, SLR). Statistical significance test showed that ν-SVR with polynomial kernel was better than that of SLR when new software projects were developed on mainframes and coded in programming languages of third generation. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting