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AMCB: enhancing the authentication process with blockchain integrated with PUF and machine learning

Multimedia Tools and Applications 2025年第8期84卷 4809-4835页

作者： Al-Ghuraybi, Hind A. AlZain, Mohammed A. Soh, Ben Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Department of Computer Science and Computer Engineering La Trobe University Bundoora Australia

The rapid advancement of technology has given rise to medical cyber-physical systems (MCPS), a subset of cyber-physical systems (CPS) specifically tailored for patient care and healthcare providers. MCPS generate substantial volumes of big data, posing challenges in terms of processing, storage, and safeguarding against unauthorized access. This paper introduces the Authenticated Medical Cyber-Physical Blockchain (AMCB) model within MCPS, leveraging Blockchain technology, machine learning, and physically unclonable functions (PUF) to fortify the authentication process, particularly for accessing electronic health records (EHR) stored on cloud servers. The model also involves analyzing data from authenticated devices to ensure its integrity. Experiments on the AMCB model evaluate its efficiency across four layers. In the physical layer, PUF keys are extracted using a built Static Random-Access Memory (SRAM) circuit and stored in a simulated Blockchain, yielding a Hamming Distance standard deviation of 6.60%. The inspection layer analyzes data from authenticated devices using machine learning, with the Random Forest achieving the highest accuracy at 99.21%. In the application layer, centered on user authentication, the average transaction time is 36.15 MS. Overall, the AMCB model requires an average of 49.82 ms to execute operations across all layers. These findings underscore the effectiveness of the proposed model in securing EHR in the dynamic landscape of MCPS. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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Noise4Denoise:Leveraging noise for unsupervised point cloud denoising

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Computational Visual Media 2024年第4期10卷 659-669页

作者： Weijia Wang Xiao Liu Hailing Zhou Lei Wei Zhigang Deng Manzur Murshed Xuequan Lu Deakin University GeelongVIC 3216Australia Department of Mechanical Engineering and Product Design Engineering Swinburne University of TechnologyMelbourneVIC 3122Australia Department of Computer Science University of HoustonHoustonTX 77204-3027USA Department of Computer Science and Information Technology La Trobe UniversityMelbourneVIC 3086Australia

Existing deep learning-based point cloud denoising methods are generally trained in a supervised manner that requires clean data as ground-truth ***,in practice,it is not always feasible to obtain clean point *** this paper,we introduce a novel unsupervised point cloud denoising method that eliminates the need to use clean point clouds as groundtruth labels during *** demonstrate that it is feasible for neural networks to only take noisy point clouds as input,and learn to approximate and restore their clean *** particular,we generate two noise levels for the original point clouds,requiring the second noise level to be twice the amount of the first noise *** this,we can deduce the relationship between the displacement information that recovers the clean surfaces across the two levels of noise,and thus learn the displacement of each noisy point in order to recover the corresponding clean *** experiments demonstrate that our method achieves outstanding denoising results across various datasets with synthetic and real-world noise,obtaining better performance than previous unsupervised methods and competitive performance to current supervised methods.

关键词： point clouds denoising geometry processing deep learning

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An artificial intelligence framework for explainable drift detection in energy forecasting

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Energy and AI 2024年第3期17卷 368-379页

作者： Chamod Samarajeewa Daswin De Silva Milos Manic Nishan Mills Harsha Moraliyage Damminda Alahakoon Andrew Jennings Centre for Data Analytics and Cognition La Trobe UniversityAustralia Department of Computer Science Virginia Commonwealth UniversityRichmondUSA

Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.

关键词： Building energy consumption forecasting Explainable drift detection Energy embedding Dimensional modeling

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GRA: Graph Representation Alignment for Semi-Supervised Action Recognition

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IEEE Transactions on Neural Networks and Learning Systems 2024年第9期35卷 11896-11905页

作者： Huang, Kuan-Hung Huang, Yao-Bang Lin, Yong-Xiang Hua, Kai-Lung Tanveer, M. Lu, Xuequan Razzak, Imran National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Indian Institute of Technology Indore Department of Mathematics Indore Simrol453552 India La Trobe University Department of Computer Science and IT MelbourneVIC3086 Australia University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

Graph convolutional networks (GCNs) have emerged as a powerful tool for action recognition, leveraging skeletal graphs to encapsulate human motion. Despite their efficacy, a significant challenge remains the dependency on huge labeled datasets. Acquiring such datasets is often prohibitive, and the frequent occurrence of incomplete skeleton data, typified by absent joints and frames, complicates the testing phase. To tackle these issues, we present graph representation alignment (GRA), a novel approach with two main contributions: 1) a self-training (ST) paradigm that substantially reduces the need for labeled data by generating high-quality pseudo-labels, ensuring model stability even with minimal labeled inputs and 2) a representation alignment (RA) technique that utilizes consistency regularization to effectively reduce the impact of missing data components. Our extensive evaluations on the NTU RGB+D and Northwestern-UCla (N-UCla) benchmarks demonstrate that GRA not only improves GCN performance in data-constrained environments but also retains impressive performance in the face of data incompleteness. © 2012 IEEE.

关键词： Alignment

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AMCB: Authenticated Medical Cyber-Physical Blockchain model

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Multimedia Tools and Applications 2024年第41期83卷 89079-89109页

作者： Al-Ghuraybi, Hind A. AlZain, Mohammed A. Soh, Ben Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science and Computer Engineering La Trobe University Bundora Australia

At a time when technology is spreading rapidly and widely, technology has become a necessity in daily life and practical life, and this led to the emergence of many cyber-physical systems (CPS), among which the medical cyber-physical systems (MCPS) have emerged, which is one application of CPS that is particularly concerned with patients and health care providers. These systems generate a large amount of data that may be difficult to process and store, in addition to unauthorized access to these systems, which affects their protection. This paper presents a proposed Authenticated Medical Cyber-Physical Blockchain (AMCB) model in Medical Cyber-Physical Systems (MCPS) using Blockchain technology, machine learning, and physically unclonable function (PUF) to enhance the authentication process by controlling the access to electronic health records (EHR) that stored on the cloud server and physical authentication. In addition, it analyzes the data generated from the authorized devices to ensure that the devices do not contain malicious. This paper presents an experiment based on the medical Internet of Things (IoMT) dataset using K-nearest neighbors (k-NN), Random Forest (RF), Naive Bayes and Supporting Vector Machine (SVM) for malicious detection to test the proposed model’s accuracy. The Random Forest (RF) classifier gave more accurate results based on the preliminary results with a slight difference from k-NN. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Data-driven Approach for State Prediction and Detection of False Data Injection Attacks in Smart Grid

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Journal of Modern Power Systems and Clean Energy 2023年第2期11卷 455-467页

作者： Haftu Tasew Reda Adnan Anwar Abdun Mahmood Naveen Chilamkurti Department of Computer Science and IT La Trobe UniversityBundooraVIC 3086Australia School of IT Deakin UniversityWaurn PondsVIC 3216Australia

In a smart grid,state estimation(SE)is a very important component of energy management *** main functions include system SE and detection of cyber ***,it has been shown that conventional SE techniques are vulnerable to false data injection(FDI)attack,which is a sophisticated new class of attacks on data integrity in smart *** main contribution of this paper is to propose a new FDI attack detection technique using a new data-driven SE model,which is different from the traditional weighted least square based SE *** SE model has a number of unique advantages compared with traditional SE ***,the prediction technique can better maintain the inherent temporal correlations among consecutive measurement ***,the proposed SE model can learn the actual power system ***,this paper shows that this SE model can be effectively used to detect FDI attacks that otherwise remain stealthy to traditional SE-based bad data *** proposed FDI attack detection technique is evaluated on a number of standard bus *** performance of state prediction and the accuracy of FDI attack detection are benchmarked against the state-ofthe-art *** results show that the proposed FDI attack detection technique has a higher detection rate compared with the existing techniques while reducing the false alarms significantly.

关键词： Data-driven false data injection machine learning power system security state estimation smart grid

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Differential flow space allocation scheme in SDN based fog computing for IoT applications

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1353-1363页

作者： Diro, Abebe Abeshu Reda, Haftu Tasew Chilamkurti, Naveen Department of Computer Science and IT La Trobe University Melbourne Australia Ethio Telecom Churchill Avenue Addis Ababa Ethiopia

Technological advancements in wireless communications and electronics have enabled the rapid evolution of smart things connected to the Internet. Traditional networks face the challenges of scalability, real-time data delivery, programmability and mobility to support these smart objects collectively known as Internet of Things (IoT). To solve these issues, the integration of two emerging network technologies, namely, software defined networking (SDN) and Fog computing have gained a momentum as a novel model that support IoT architecture for manageability and low latency. SDN has a logically centralized network control plane, which is used for implementing sophisticated mechanisms of traffic control and resource management. On the other hand, Fog computing enables IoT devices’ data to be processed and managed at the network edge, thus providing support for applications that require very low and predictable latency. Though the communication latency is substantially reduced by the adoption of distributed fog layer closer to IoT ends, the latency overhead in the IoT/fog network is not only because of long distance between IoT devices and the cloud, but it is also caused by flow entry installation delay, which comes from limitations in data and control space designs. Traditional fog networks lack priority based fine-grain control over allocation of flows, and this incurs unnecessary delay for critical packets. The impact of packet blocking on QoS delivery could be reduced if the programmability power of SDN approach is employed in IoT applications for priority oriented flow space management in fog networks. In this paper, we propose a converged SDN and IoT/fog architecture which employs differential flow space allocation for heterogeneous IoT applications per flow classes to satisfy priority based quality of service requirements. Our analytical results demonstrate that urgent flow classes are served more efficiently than Naïve approach without compromising fairness of al

关键词： Quality of service

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An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling

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Annals of Data science 2024年第5期11卷 1525-1544页

作者： Zubair, Md. Iqbal, MD. Asif Shil, Avijeet Chowdhury, M.J.M. Moni, Mohammad Ali Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & ampTechnology Chittagong4349 Bangladesh Department of Computer Science and Information Technology La Trobe University Victoria3086 Australia School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD4072 Australia

K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group. The minimum squared distance technique distributes each point to the nearest clusters or subgroups. One of the K-means algorithm’s main concerns is to find out the initial optimal centroids of clusters. It is the most challenging task to determine the optimum position of the initial clusters’ centroids at the very first iteration. This paper proposes an approach to find the optimal initial centroids efficiently to reduce the number of iterations and execution time. To analyze the effectiveness of our proposed method, we have utilized different real-world datasets to conduct experiments. We have first analyzed COVID-19 and patient datasets to show our proposed method’s efficiency. A synthetic dataset of 10M instances with 8 dimensions is also used to estimate the performance of the proposed algorithm. Experimental results show that our proposed method outperforms traditional kmeans++ and random centroids initialization methods regarding the computation time and the number of iterations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Principal component analysis

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An explainable lightweight parallel depth-wise separable model for lung infection detection from chest X-rays

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Neural Computing and Applications 2025年第6期37卷 4545-4566页

作者： Kibria, Hafsa Binte Hossain, Md Ali Rehman, Shazia Alahakoon, Damminda Rahman, Md Anisur Department of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Psychiatry Second Xiangya Hospital Central South University Hunan Changsha China La Trobe Business School La Trobe University Plenty Road Bundoora VIC3086 Australia

Respiratory diseases including severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), HIV, influenza A (H1N1), and COVID-19 can cause lung infections. Among these, COVID-19 has been considered as an epidemic in the past few years. According to the World Health Organization (WHO), about 2.7 million deaths occurred due to lung infection caused by COVID-19. Therefore, most of the countries implemented full or partial lockdown measures to slow down the spread of the disease. However, early detection of viral infection is crucial to reduce the spreading as well as minimizing the pandemic situation. Common lung infection detection methods include X-ray image analysis, reverse transcription–polymerase chain reaction (RT-PCR), and computed tomography (CT) scans. RT-PCR and CT scans have limitations such as being error-prone, slow, and requiring specialized machinery. CT scans are expensive and expose patients to radiation. Similarly, manual assessment of X-ray images is challenging due to the limited availability of skilled medical professionals. To mitigate this problem, convolutional neural networks (CNNs) have shown potential in automating the detection of lung infections from X-ray images. However, the traditional CNN models have a large number of parameters and require high computational resources. To address these issues, this research proposes a novel parallel lightweight diagnosis model based on depth-wise separable CNN (LW-PDS-CovidNet). Additionally, the proposed model’s explainability is enhanced by utilizing Shapley additive explanations (SHAP) and gradient-weighted class activation mapping (GRAD CAM) to highlight the most important features. The proposed LW-PDS-CovidNet approach was tested on real chest X-ray images to predict them as normal, COVID-19, viral pneumonia, and lung opacity. Experimental outcomes demonstrate that the proposed method outperforms the baseline methods and achieved an accuracy of 98.06, 97.43, and 92% for two

关键词： computerized tomography

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Super-resolved Estimation of White Matter Microstructure via 3D Conditional latent Diffusion Model

Super-resolved Estimation of White Matter Microstructure via...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jiquan Gao, Yihang Jiang, Haotian Gu, Jing Khairullina, Diliara Cui, Hui Chen, Geng Heilongjiang University Department of Computer Science and Technology Harbin China Northwestern Polytechnical University School of Computer Science and Engineering Xi'an China La Trobe University Department of Computer Science and Information Technology Melbourne Australia

ISBN: (纸本)9798350386226

As a powerful microstructural imaging technique, neurite orientation dispersion and density imaging (NODDI) provides detailed insights into brain microstructures. Its clinical application is often restricted by the necessity for high-quality scanning, which can be challenging to achieve in practical settings. To overcome this limitation, we propose an innovative 3D conditional latent diffusion model (3D-CLDM) to generate high-quality NODDI index maps from low-resolution diffusion magnetic resonance imaging data. The 3D-CLDM is a two-stage super-resolved microstructure estimation model that includes training a vector quantized generative adversarial network and a diffusion model. It leverages the sophisticated high-dimensional data modeling capabilities of the conditional latent diffusion model to effectively capture and represent intricate microstructural features that are difficult to detect with conventional techniques. We conducted comprehensive experiments using data from the human connectome project to rigorously assess our model's performance. The results reveal that our approach not only significantly improves the quality of super-resolved microstructural estimation but also surpasses current state-of-the-art models in both qualitative and quantitative evaluations. This highlights the potential of 3D-CLDM to advance brain microstructure imaging, making it more feasible and effective for clinical applications. © 2024 IEEE.

关键词： Magnetic resonance imaging

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