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Systematic Assessment of the Interoperability Requirements and Challenges of Secure Blockchain-Based Electronic Health Records

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Security and Communication Networks 2022年第0期2022卷

作者： Reegu, Faheem Ahmad Abas, Hafiza Hakami, Zaid Tiwari, Sanatan Akmam, Rudzidatul Muda, Iskandar Almashqbeh, Hashem Ali Jain, Rituraj Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur50480 Malaysia Department of Computer Science Jazan University Jazan45142 Saudi Arabia Faculty of Management Studies University of Delhi Delhi India Faculty Economic and Business Universitas Sumatera Utara Medan Indonesia Department of Business Qatar University Doha Qatar Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopia

A peer-to-peer (P2P) decentralized information-sharing network is used to share data and maintain security, privacy, and integrity standards called blockchain. In this case, information sharing and updating require regular simplification. The presented systematic review mainly focuses on the interoperability of electronic health records (EHRs) using blockchain. Correspondingly, 18 blockchain-based solutions were selected to address the interoperability challenges of EHRs. The limitation of solutions includes reliability, privacy, integrity, sharing, and standards. This systematic review contains six phase's research question, research phase, article selection, abstract-based keyword, data extraction, and progress tracking. Various Web resources such as Google Scholar, Web of Science, and IEEE are used to extract the relevant manuscripts. Primarily, 18 articles were selected to present the interoperable requirements of EHRs using blockchain, standards of blockchain-based EHRs, and solutions for interoperability of EHRs using blockchain. The conducted study explains the best available interoperable blockchain-based EHR standards, implementations, applications, and challenges. © 2022 Faheem Ahmad Reegu et al.

关键词： Blockchain

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Mutual information based logistic regression for phishing URL detection

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations (CCRI) Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Ronin Institute Montclair NJ United States Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut 1102 Lebanon Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India

Phishing is a cybersecurity problem that hackers employ to deceive individuals and organizations. Phishing is dynamic in nature;the hackers change several tricks to deceive the victims in multiple ways. It is important to track the tricks of hackers with recent technology. This study makes a notable contribution to enhancing cybersecurity defences by offering insights that aid in the detection and mitigation of phishing threats. Specifically, the study's analysis of URLs using mutual information and logistic regression techniques yielded a remarkably high accuracy rate of 99.97%, surpassing previous efforts. The identification of the most informative features for distinguishing phishing attempts provides valuable intelligence for cybersecurity professionals, enabling them to bolster defenses and stay ahead of evolving phishing tactics. © 2024

关键词： Feature selection Logistic regression Mutual information Phishing URL analysis

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Robustness modularity in complex networks

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Physical Review E 2022年第5期105卷 054308-054308页

作者： Filipi N. Silva Aiiad Albeshri Vijey Thayananthan Wadee Alhalabi Santo Fortunato Indiana University Network Science Institute (IUNI) Bloomington Indiana 47408 USA Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Kingdom of Saudi Arabia Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA

A basic question in network community detection is how modular a given network is. This is usually addressed by evaluating the quality of partitions detected in the network. The Girvan-Newman (GN) modularity function is the standard way to make this assessment, but it has a number of drawbacks. Most importantly, it is not clearly interpretable, given that the measure can take relatively large values on partitions of random networks without communities. Here we propose a measure based on the concept of robustness: modularity is the probability to find trivial partitions when the structure of the network is randomly perturbed. This concept can be implemented for any clustering algorithm capable of telling when a group structure is absent. Tests on artificial and real graphs reveal that robustness modularity can be used to assess and compare the strength of the community structure of different networks. We also introduce two other quality functions: modularity difference, a suitably normalized version of the GN modularity, and information modularity, a measure of distance based on information compression. Both measures are strongly correlated with robustness modularity, but have lower time complexity, so they could be used on networks whose size makes the calculation of robustness modularity too costly.

关键词： Community structure Complex systems Robustness

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Undetectable Attacks on Boolean Networks

Undetectable Attacks on Boolean Networks

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IEEE Conference on Decision and Control

作者： Shiyong Zhu Jianquan Lu Jinde Cao Lin Lin James Lam Michael Ng Shun-ichi Azuma Department of Systems Science School of Mathematics Southeast University Nanjing China School of Mathematics Southeast University Nanjing China Yonsei Frontier Lab Yonsei University Seoul South Korea Department of Computer Science and Engineering Yunnan University Kunming China Department of Mechanical Engineering The University of Hong Kong Hong Kong Department of Mathematics The University of Hong Kong Hong Kong Graduate School of Informatics Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Japan

In this paper, driven by the ever-increasing cyber-security threats, we study the undetectable attack problems for Boolean networks (BNs), which model distributed systems with a limited capacity of storage and bandwidth of communication. Given a consistent monitor, undetectable attacks are formalized for BNs as those do not yield an output sequence out of the nominal output sequence set. By the graph-theoretic approach, undetectable attacks are characterized by a reachability problem of a directed cycle in the augmented transition graph. On the other hand, the algebraic approach also derives a necessary and sufficient criterion for undetectable attacks by testing the existence of the nonzero elements in the constructed matrix. While all these derived results are only computationally efficient for relatively small-size BNs. The detection of attack signals is indeed NP-hard. In other words, there is no polynomial-time algorithm to check the detectability of an attack signal or an attack node set unless NP=P.

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StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems

arXiv

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arXiv 2024年

作者： Bouzinis, Pavlos S. Radoglou-Grammatikis, Panagiotis Makris, Ioannis Lagkas, Thomas Argyriou, Vasileios Papadopoulos, Georgios Th. Sarigiannidis, Panagiotis Karagiannidis, George K. MetaMind Innovation P.C. Kila Kozani Kozani50100 Greece The Department of Electrical and Computer Engineering University of Western Macedonia Campus ZEP Kozani Kozani50100 Greece K3Y Ltd. William Gladstone 31 Sofia1000 Bulgaria The Department of Computer Science Democritus University of Thrace Kavala Campus Kavala65404 Greece The Department of Networks and Digital Media Kingston University London Penrhyn Road Kingston upon Thames Surrey LondonKT1 2EE United Kingdom The Department of Informatics and Telematics Harokopio University of Athens Omirou 9 Tavros AthensGR17778 Greece The Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Beirut1102 2801 Lebanon

Federated learning (FL) is a decentralised learning technique that enables participating devices to collaboratively build a shared Machine Learning (ML) or Deep Learning (DL) model without revealing their raw data to a third party. Due to its privacy-preserving nature, FL has sparked widespread attention for building Intrusion Detection Systems (IDS) within the realm of cybersecurity. However, the data heterogeneity across participating domains and entities presents significant challenges for the reliable implementation of an FL-based IDS. In this paper, we propose an effective method called Statistical Averaging (StatAvg) to alleviate non-independently and identically (non-iid) distributed features across local clients’ data in FL. In particular, StatAvg allows the FL clients to share their individual data statistics with the server, which then aggregates this information to produce global statistics. The latter are shared with the clients and used for universal data normalisation. It is worth mentioning that StatAvg can seamlessly integrate with any FL aggregation strategy, as it occurs before the actual FL training process. The proposed method is evaluated against baseline approaches using datasets for network and host Artificial Intelligence (AI)powered IDS. The experimental results demonstrate the efficiency of StatAvg in mitigating non-iid feature distributions across the FL clients compared to the baseline methods. © 2024, CC BY.

关键词： Intrusion detection

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Fast light-field 3D microscopy with out-of-distribution detection and adaptation through Conditional Normalizing Flows

arXiv

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arXiv 2023年

作者： Vizcaíno, Josué Page Symvoulidis, Panagiotis Wang, Zeguan Jelten, Jonas Favaro, Paolo Boyden, Edward S. Lasser, Tobias Computational Imaging and Inverse Problems Department of Informatics School of Computation Information and Technology Technical University of Munich Germany Munich Institute of Biomedical Engineering Technical University of Munich Germany Synthetic Neurobiology Group Massachusetts Institute of Technology United States Computer Vision Group University of Bern Switzerland

Real-time 3D fluorescence microscopy is crucial for the spatiotemporal analysis of live organisms, such as neural activity monitoring. The eXtended field-of-view light field microscope (XLFM), also known as Fourier light field microscope, is a straightforward, single snapshot solution to achieve this. The XLFM acquires spatial-angular information in a single camera exposure. In a subsequent step, a 3D volume can be algorithmically reconstructed, making it exceptionally well-suited for real-time 3D acquisition and potential analysis. Unfortunately, traditional reconstruction methods (like deconvolution) require lengthy processing times (0.0220 Hz), hampering the speed advantages of the XLFM. Neural network architectures can overcome the speed constraints at the expense of lacking certainty metrics, which renders them untrustworthy for the biomedical realm. This work proposes a novel architecture to perform fast 3D reconstructions of live immobilized zebrafish neural activity based on a conditional normalizing flow. It reconstructs volumes at 8 Hz spanning 512x512x96 voxels, and it can be trained in under two hours due to the small dataset requirements (10 image-volume pairs). Furthermore, normalizing flows allow for exact Likelihood computation, enabling distribution monitoring, followed by out-of-distribution detection and retraining of the system when a novel sample is detected. We evaluate the proposed method on a cross-validation approach involving multiple in-distribution samples (genetically identical zebrafish) and various out-of-distribution ones. © 2023, CC BY.

关键词： Neurons

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Intelligent Spectrum Occupancy Prediction for Realistic Measurements: GRU based Approach

Intelligent Spectrum Occupancy Prediction for Realistic Meas...

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International Black Sea Conference on Communications and Networking (BlackSeaCom)

作者： Armed Tusha Batuhan Kaplan Hakan Ali Cı rpan Khalid Qaraqe Hü seyin Arslan Department of Electrical and Electronics Engineering Istanbul Medipol University Istanbul Turkey Department of Electrical and Computer Engineering Texas A&#x0026 M University at Qatar Doha Qatar Informatics and Information Security Research Center (B&#x0130 LGEM) T&#x00DC B&#x0130 TAK Kocaeli Turkey Department of Electronics and Communication Engineering Istanbul Technical University Istanbul Turkey Department of Electrical Engineering University of South Florida Tampa Florida USA

ISBN: (数字)9781665497497

ISBN: (纸本)9781665497503

Cognitive radio (CR) technology has always been a research hotspot in the wireless communications field as it has the potential to significantly improve system capacity at the cost of increased processing time and power consumption, which represent highly critical performance indicators (CPI) towards next-generation wireless networks. In particular, the main problem in the CR-based communication links resides in the prediction of spectrum availability in accordance with strict secondary user (SU) CPIs requirements, which is not achievable through the traditional approaches. In this work, we design a novel hierarchical spectrum prediction model, taking advantage from the recurrent neural network (RNN) with the focus on the gated recurrent unit network (GRU). Specifically, the proposed system architecture offers an accrue prediction on the spectrum availability for the SU considering the prior information of the primary user (PU). The performance of the proposed design is illustrated through extensive simulation results. Specifically, real spectrum measurements gathered from Doha, in Qatar are performed to assess the performance accuracy of the designed architecture. In particular different from the conventional scheme that uses a binary representation of spectrum occupancy (idle is “0” and occupied is “1”), we perform training and prediction over the minimum and maximum recorded measurements.

关键词： Training Recurrent neural networks Atmospheric measurements Wireless networks Simulation Urban areas Sea measurements

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Robust Recovery of Low Rank Matrix by Nonconvex Rank Regularization 11th

Robust Recovery of Low Rank Matrix by Nonconvex Rank Regular...

引用

11th International Conference on Image and Graphics, ICIG 2021

作者： Zhang, Hengmin Luo, Wei Du, Wenli Qian, Jianjun Yang, Jian Zhang, Bob Department of Computer and Information Science University of Macau 999078 China School of Information Science and Engineering Key Laboratory of Advanced Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783030873578

As we know, nuclear norm based regularization methods have the real-world applications in pattern recognition and computer vision. However, there exists a biased estimator when nuclear norm relaxes the rank function. To solve this issue, we focus on studying nonconvex rank regularization problems for both robust matrix completion (RMC) and low rank representation (LRR), respectively. By extending both to a general low rank matrix minimization problem, we develop a nonconvex alternating direction method of multipliers (ADMM). Moreover, the convergence results, i.e., the variable sequence generated by the nonconvex ADMM is bounded and its subsequence converges to a stationary point. Meanwhile, its limiting point satisfies the Karush-Kuhn-Tucher (KKT) conditions provided under some milder assumptions. Numerical experiments can verify the convergence properties of the theoretical results and the performance shows its superiority on both image inpainting and subspace clustering. © 2021, Springer Nature Switzerland AG.

关键词： Clustering algorithms

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Lightning-Fast Dual-Layer Lossless Coding for Radiance Format High Dynamic Range Images

arXiv

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arXiv 2023年

作者： Suzuki, Taizo Yukikata, Sara Yang, Kai Yoshida, Taichi The Institute of Engineering Information and Systems University of Tsukuba Ibaraki305-8573 Japan Sky Co. Ltd. Tokyo108-0075 Japan Da-Jiang Innovations Science and Technology Japan Co. Ltd. Tokyo108-0075 Japan The Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo182-8585 Japan

This paper proposes a fast dual-layer lossless coding for high dynamic range images (HDRIs) in the Radiance format. The coding, which consists of a base layer and a lossless enhancement layer, provides a standard dynamic range image (SDRI) without requiring an additional algorithm at the decoder and can losslessly decode the HDRI by adding the residual signals (residuals) between the HDRI and SDRI to the SDRI, if desired. To suppress the dynamic range of the residuals in the enhancement layer, the coding directly uses the mantissa and exponent information from the Radiance format. To further reduce the residual energy, each mantissa is modeled (estimated) as a linear function, i.e., a simple linear regression, of the encoded-decoded SDRI in each region with the same exponent. This is called simple linear regressive mantissa estimator. Experimental results show that, compared with existing methods, our coding reduces the average bitrate by approximately 1.57-6.68 % and significantly reduces the average encoder implementation time by approximately 87.13-98.96 %. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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MassSpecGym: A benchmark for the discovery and identification of molecules

arXiv

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arXiv 2024年

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of Computing Science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Germany Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag: Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym – the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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