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An access control scheme in IoT-enabled Smart-Grid systems using blockchain and PUF

Internet of Things (Netherlands) 2023年 22卷

作者： Zahoor, Amina Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Ayub, Muhammad Faizan Conti, Mauro Das, Ashok Kumar Department of Computer Science COMSATS University Islamabad Sahiwal Campus 57000 Pakistan Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Software Engineering The University of Lahore Lahore 54590 Pakistan Department of Computer Science University of Sahiwal Sahiwal 57000 Pakistan Department of Mathematics University of Padua Padua 35131 Italy Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India

IoT-enabled Smart Grid (IoT-SG) is an emerging paradigm that enables the bi-direction communication of IoT devices and hardware to efficiently collect and transmit the consumer's information over the Internet. However, the underlying open communication network and resource-constrained capabilities of devices invite manifold challenges and threats in terms of security and privacy. To alleviate such issues, we designed a private blockchain-based access control protocol for IoT-SG using Physically Unclonable Function (PUF). Our protocol allows the Service Provider (SP) and smart meters to transfer the data in an efficient and secure manner. The participating SPs form the Peer-to-Peer (P2P) network, and each peer node is responsible for securely creating the blocks from the gathered data. Thereafter, all the peer nodes employ a voting-based consensus mechanism to verify and add the recently created block into the blockchain network. We have rigorously validated the security of our protocol under the Random or Real (RoR) model. The testbed results and security feature analysis show that our protocol is more efficient than competing ones while it also provides significant security properties. © 2023 Elsevier B.V.

关键词： Authentication Blockchain Internet of Things (IoT) Key agreement security Smart power grids

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Deep Learning for the Recognition of Skin Cancer

Deep Learning for the Recognition of Skin Cancer

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Advanced computer Applications (ACA), International Conference on

作者： Israa Akram Fadhil Alzuabidi Layla safwat Jamil Amjed Abbas Ahmed Saif Aamer Fadhil Mohammad Kamrul Hasan Ahmed Salman Ibraheem Continuing of Education/Collage of Arts/University of Baghdad Baghdad Iraq College of agricultural engineering sciences/university of Baghdad Baghdad Iraq Department of Computer Engineering Imam Al-Kadhum College (IKC) Baghdad Iraq Faculty of Information Science and Technology Center for Cyber Security Universiti Kebangsaan Malaysia Bangi Malaysia Imam Al-Kadhum College (IKC) Baghdad Iraq

The health industry has kept growing over the years with breakthroughs. However, in skin disease recognition such as cancer, there is still a battle to improve how recognition is implemented to get the desired result. The paper focuses on skin cancer recognition using deep learning. In addition, a cloud-based model-based architecture that uses deep learning algorithms in its core applications to create models that help predict skin cancer with improved accuracy is considered. System analysis and specification are also done in conjunction with a description of procedures and requirements. Implementation and testing are also done and finalized with the conclusion and future work. The main outcomes of the results and conclusion will be drawn from the implementation and testing that are carried out on the data. In addition to the fact that the implications of the findings would become valuable and further used as a benchmark for future research work in the research domain.

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Approximately Counting Answers to Conjunctive Queries with Disequalities and Negations

arXiv

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arXiv 2021年

作者： Focke, Jacob Goldberg, Leslie Ann Roth, Marc Živný, Stanislav CISPA Helmholtz Center for Information Security Germany Department of Computer Science University of Oxford United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

We study the complexity of approximating the number of answers to a small query in a large database D. We establish an exhaustive classification into tractable and intractable cases if is a conjunctive query possibly including disequalities and negations: • If there is a constant bound on the arity of , and if the randomised Exponential Time Hypothesis (rETH) holds, then the problem has a fixed-parameter tractable approximation scheme (FPTRAS) if and only if the treewidth of is bounded. • If the arity is unbounded and does not have negations, then the problem has an FPTRAS if and only if the adaptive width of (a width measure strictly more general than treewidth) is bounded;the lower bound relies on the rETH as well. Additionally we show that our results cannot be strengthened to achieve a fully polynomial randomised approximation scheme (FPRAS): We observe that, unless NP = RP, there is no FPRAS even if the treewidth (and the adaptive width) is 1. However, if there are neither disequalities nor negations, we prove the existence of an FPRAS for queries of bounded fractional hypertreewidth, strictly generalising the recently established FPRAS for conjunctive queries with bounded hypertreewidth due to Arenas, Croquevielle, Jayaram and Riveros (STOC 2021). © 2021, CC BY.

关键词： Classification (of information)

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Integrated high-performance error correction for continuous-variable quantum key distribution

arXiv

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arXiv 2024年

作者： Zhou, Chuang Li, Yang Ma, Li Yang, Jie Huang, Wei Sun, Ao Wang, Heng Luo, Yujie Li, Yong Chen, Ziyang Lau, Francis C.M. Zhang, Yichen Yu, Song Guo, Hong Xu, Bingjie National Key Laboratory of Security Communication Institute of Southwestern Communication Chengdu610041 China College of Computer Science Chongqing University Chongqing400044 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Center for Quantum Information Technology Peking University Beijing100871 China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China

An integrated error-correction scheme with high throughput, low frame errors rate (FER) and high reconciliation efficiency under low signal to noise ratio (SNR) is one of the major bottlenecks to realize high-performance and low-cost continuous variable quantum key distribution (CV-QKD). To solve this long-standing problem, a novel two-stage error correction method with limited precision that is suitable for integration given limited on-chip hardware resource while maintaining excellent decoding performance is proposed, and experimentally verified on a commercial FPGA. Compared to state-of-art results, the error-correction throughput can be improved more than one order of magnitude given FER © 2024, CC BY.

关键词： Error correction

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Near-Field Beam Focusing Pattern and Grating Lobe Characterization for Modular XL-Array

arXiv

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arXiv 2023年

作者： Li, Xinrui Dong, Zhenjun Zeng, Yong Jin, Shi Zhang, Rui National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

In this paper, we investigate the near-field modelling and analyze the beam focusing pattern for modular extremely large-scale array (XL-array) communications. As modular XL-array is physically and electrically large in general, the accurate characterization of amplitude and phase variations across its array elements requires the non-uniform spherical wave (NUSW) model, which, however, is difficult for performance analysis and optimization. To address this issue, we first present two ways to simplify the NUSW model by exploiting the unique regular structure of modular XL-array, termed sub-array based uniform spherical wave (USW) models with different or common angles, respectively. Based on the developed models, the near-field beam focusing patterns of XL-array communications are derived. It is revealed that compared to the existing collocated XL-array with the same number of array elements, modular XL-array can significantly enhance the spatial resolution, but at the cost of generating undesired grating lobes. Fortunately, different from the conventional far-field uniform plane wave (UPW) model, the near-field USW model for modular XL-array exhibits a higher grating lobe suppression capability, thanks to the non-linear phase variations across the array elements. Finally, simulation results are provided to verify the near-field beam focusing pattern and grating lobe characteristics of modular XL-array. Copyright © 2023, The Authors. All rights reserved.

关键词： Focusing

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Semantic Learning and Emulation Based Cross-platform Binary Vulnerability Seeker

arXiv

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arXiv 2022年

作者： Gao, Jian Jiang, Yu Liu, Zhe Yang, Xin Wang, Cong Jiao, Xun Yang, Zijiang Sun, Jiaguang The School of Software Tsinghua University Beijing National Research Center for Information Science and Technology Key Laboratory for Information System Security Ministry of Education Beijing100084 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Department of Electrical and Computer Engineering Villanova University VillanovaPA19085 United States The Department of Computer Science Western Michigan University KalamazooMI49008 United States

Clone detection is widely exploited for software vulnerability search. The approaches based on source code analysis cannot be applied to binary clone detection because the same source code can produce significantly different binaries due to different operating systems, microprocessor architectures and compilers. In this paper, we present BinSeeker, a cross-platform binary seeker that integrates semantic learning and emulation. With the help of the labeled semantic flow graph, BinSeeker can quickly identify M candidate functions that are most similar to the vulnerability from the target binary. The value of M is relatively large so this semantic learning procedure essentially eliminates those functions that are very unlikely to have the vulnerability. Then, semantic emulation is conducted on these M candidates to obtain their dynamic signature sequences. By comparing signature sequences, BinSeeker produces top-N functions that exhibit most similar behavior to that of the vulnerability. With fast filtering of semantic learning and accurate comparison of semantic emulation, BinSeeker seeks vulnerability precisely with little overhead. The experiments on six widely used programs with fifteen known CVE vulnerabilities demonstrate that BinSeeker outperforms three state-of-the-art tools Genius, Gemini and CACompare. Regarding search accuracy, BinSeeker achieves an MRR value of 0.65 in the target programs, whereas the MRR values by Genius, Gemini and CACompare are 0.17, 0.07 and 0.42, respectively. If we consider ranking a function with the targeted vulnerability in the top-5 as accurate, BinSeeker achieves the accuracy of 93.33 percent, while the accuracy of the other three tools is merely 33.33, 13.33 and 53.33 percent, respectively. Such accuracy is achieved with 0.27s on average to determine whether the target binary function contains a known vulnerability, and the time for the other three tools are 1.57s, 0.15s and 0.98s, respectively. Compared to the time used to manu

关键词： Semantics

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Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data

arXiv

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arXiv 2024年

作者： Fang, Congyu Dziedzic, Adam Zhang, Lin Oliva, Laura Verma, Amol Razak, Fahad Papernot, Nicolas Wang, Bo Department of Computer Science University of Toronto Canada Peter Munk Cardiac Centre University Health Network Canada Vector Institute Toronto Canada CISPA Helmholtz Center for Information Security Germany Department of Electrical and Computer Engineering University of Toronto Canada Simon Fraser University Canada St. Michael’s Hospital Unity Health Toronto Canada Department of Medicine University of Toronto Canada Institute of Health Policy Management and Evaluation University of Toronto Canada Department of Laboratory Medicine and Pathobiology Temerty Faculty of Medicine University of Toronto Canada

Background: Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions or jurisdictions is challenging because of complex and varying privacy and regulatory requirements. Hence, it is hard but crucial to allow multiple parties to collaboratively train an ML model leveraging the private datasets available at each party without the need for direct sharing of those datasets or compromising the privacy of the datasets through collaboration. Methods: In this paper, we address this challenge by proposing Decentralized, Collaborative, and Privacy-preserving ML for Multi-Hospital Data (DeCaPH). This framework offers the following key benefits: (1) it allows different parties to collaboratively train an ML model without transferring their private datasets (i.e., no data centralization);(2) it safeguards patients’ privacy by limiting the potential privacy leakage arising from any contents shared across the parties during the training process;and (3) it facilitates the ML model training without relying on a centralized party/server. Findings: We demonstrate the generalizability and power of DeCaPH on three distinct tasks using real-world distributed medical datasets: patient mortality prediction using electronic health records, cell-type classification using single-cell human genomes, and pathology identification using chest radiology images. The ML models trained with DeCaPH framework have less than 3.2% drop in model performance comparing to those trained by the non-privacy-preserving collaborative framework. Meanwhile, the average vulnerability to privacy attacks of the models trained with DeCaPH decreased by up to 16%. In addition, models trained with our DeCaPH framework achieve better performance than those models trained solely with the private

关键词： Machine learning

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ZTEI: Zero-Trust and Edge Intelligence Empowered Continuous Authentication for Satellite Networks

ZTEI: Zero-Trust and Edge Intelligence Empowered Continuous ...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Peiyu Fu Jun Wu Xi Lin Ao Shen School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China Collaborative Innovation Center of Shanghai Industrial Internet Shanghai China Faculty of Science and Engineering Waseda University Japan Department of Computer Science Faculty of Engineering The University of Hong Kong Hong Kong China

ISBN: (纸本)9781665435413

The integration of satellite communication technology and terrestrial infrastructure has resulted in an un-precedented increase in network services covering the world. The main effect of the rapid growth of satellite networks is a broader range of data exchange and business interaction between the internal and external systems, making the network boundaries blur or even disappear. As a result, traditional passive security mechanisms based on dividing network boundaries cannot provide sufficient protection. To address this issue, in this paper, we propose a zero-trust and edge intelligence (ZTEI) empowered continuous authentication for satellite networks. We build an improved zero-trust architecture (ZTA) for satellite networks, which expands the traditional zero-trust concept to the multi-dimensional zero-trust that focuses on subject, object, environment, behavior, and physical entity. Then we propose a continuous authentication scheme in the proposed zero-trust architecture, enabling proactive and continuous authentication by periodically monitoring and re-evaluating variable attributes throughout the request lifecycle. Besides, in this scheme, we also design a Neural-Backed Decision Trees (NBDTs) based edge intelligence algorithm to improve the authentication accuracy. Finally, we build a testbed to evaluate the performance of the proposed architecture. Compared with the attribute-based access control (ABAC) under the traditional zero-trust architecture, our proposed architecture can improve the authentication accuracy of dynamic illegal requests by about 27%. In addition, according to standard network performance evaluation criteria, the loss of processing performance caused by our solution is also within an acceptable range.

关键词： Performance evaluation Satellites Heuristic algorithms Authentication Zero Trust Global communication Decision trees

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A novel binary modified beluga whale optimization algorithm using ring crossover and probabilistic state mutation for enhanced bladder cancer diagnosis

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Informatics in Medicine Unlocked 2024年 50卷

作者： Gharaibeh, Hasan Alawad, Noor Aldeen Nasayreh, Ahmad Al Mamlook, Rabia Emhamed Makhadmeh, Sharif Naser Bashkami, Ayah Abualigah, Laith Ezugwu, Absalom E. Computer Science Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan Department Medical Laboratory Sciences – Faculty of Science Al Balqa Applied University Salt 19117 Jordan Department of Cyber Security and Cloud Computing Faculty of Information Technology Applied Science Private University Amman 11937 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman P.O. Box 346 United Arab Emirates

Bladder cancer (BC) remains a significant global health challenge, requiring the development of accurate predictive models for diagnosis. In this study, a new Binary Modified White Whale Optimization (B-MBWO) algorithm is proposed to address the BC problem. The proposed method utilizes circular transitivity optimization and the Probabilistic State Mutation Algorithm (PSMA) to enhance its optimization performance. The new method is called the BBWORCPS algorithm. High-dimensional and complex medical datasets pose challenges to the original optimization algorithms in addressing the BC problem, motivating the proposed modifications to the original Beluga Whale Optimization algorithm. These enhancements, including quantum-inspired mutation and circular crossing, aim to improve solution space exploration and enhance the algorithm's effectiveness in handling intricate feature spaces. Through comprehensive experiments on BC datasets, the superiority of the BBWORCPS algorithm in terms of feature selection accuracy and computational efficiency is demonstrated compared to existing optimization methods. The obtained findings suggest that BBWORCPS offers a promising approach for developing more precise and reliable predictive models for bladder cancer analysis. © 2024 The Author(s)

关键词： Binary modified beluga whale optimization Bladder cancer Feature selection Machine learning Predictive modeling

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Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds

arXiv

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arXiv 2023年

作者： Li, Shikun Xia, Xiaobo Deng, Jiankang Ge, Shiming Liu, Tongliang The Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Sydney AI Center School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia The Department of Computing Imperial College London LondonSW7 2BX United Kingdom

Learning from crowds describes that the annotations of training data are obtained with crowd-sourcing services. Multiple annotators each complete their own small part of the annotations, where labeling mistakes that depend on annotators occur frequently. Modeling the label-noise generation process by the noise transition matrix is a powerful tool to tackle the label noise. In real-world crowd-sourcing scenarios, noise transition matrices are both annotator- and instance-dependent. However, due to the high complexity of annotator- and instance-dependent transition matrices (AIDTM), annotation sparsity, which means each annotator only labels a tiny part of instances, makes modeling AIDTM very challenging. Without prior knowledge, existing works simplify the problem by assuming the transition matrix is instance-independent or using simple parametric ways, which lose modeling generality. Motivated by this, we target a more realistic problem, estimating general AIDTM in practice. Without losing modeling generality, we parameterize AIDTM with deep neural networks. To alleviate the modeling challenge, we suppose every annotator shares its noise pattern with similar annotators, and estimate AIDTM via knowledge transfer. We hence first model the mixture of noise patterns by all annotators, and then transfer this modeling to individual annotators. Furthermore, considering that the transfer from the mixture of noise patterns to individuals may cause two annotators with highly different noise generations to perturb each other, we employ the knowledge transfer between identified neighboring annotators to calibrate the modeling. Theoretical analyses are derived to demonstrate that both the knowledge transfer from global to individuals and the knowledge transfer between neighboring individuals can effectively help mitigate the challenge of modeling general AIDTM. Experiments confirm the superiority of the proposed approach on synthetic and real-world crowd-sourcing data. The imple

关键词： Deep neural networks

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