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A novel hybrid authentication protocol utilizing lattice-based cryptography for IoT devices in fog networks

Digital Communications and Networks 2024年第4期10卷 989-1000页

作者： Kumar Sekhar Roy Subhrajyoti Deb Hemanta Kumar Kalita Department of Computer Science and Engineering Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal576104KarnatakaIndia Department of Computer Science&Engineering ICFAI UniversityAgartala799210India Department of Computer Science&Engineering Central Institute of TechnologyKokrajhar783370India

The Internet of Things(IoT)has taken the interconnected world by *** to their immense applicability,IoT devices are being scaled at exponential proportions ***,very little focus has been given to securing such *** these devices are constrained in numerous aspects,it leaves network designers and administrators with no choice but to deploy them with minimal or no security at *** have seen distributed denial-ofservice attacks being raised using such devices during the infamous Mirai botnet attack in *** we propose a lightweight authentication protocol to provide proper access to such *** have considered several aspects while designing our authentication protocol,such as scalability,movement,user registration,device registration,*** define the architecture we used a three-layered model consisting of cloud,fog,and edge *** have also proposed several pre-existing cipher suites based on post-quantum cryptography for evaluation and *** also provide a fail-safe mechanism for a situation where an authenticating server might fail,and the deployed IoT devices can self-organize to keep providing services with no human *** find that our protocol works the fastest when using ring learning with *** prove the safety of our authentication protocol using the automated validation of Internet security protocols and applications *** conclusion,we propose a safe,hybrid,and fast authentication protocol for authenticating IoT devices in a fog computing environment.

关键词： Internet of things Authentication Post-quantum cryptography Lattice-based cryptography Cloud computing Fog computing Fail-safe

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Domain generalization with semi-supervised learning for people-centric activity recognition

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science China(Information sciences) 2025年第1期68卷 171-188页

作者： Jing LIU Wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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WiFi for Privacy-Friendly Surveillance 15

WiFi for Privacy-Friendly Surveillance

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ghosh, Akhilbaran Chouksey, Arjun Academy Of Technology Department of Computer Science and Engineering Adisaptagram West Bengal Hooghly India Jabalpur Engineering College Department of Electronics & Communications Engineering Madhya Pradesh Jabalpur India

ISBN: (纸本)9798350370249

In the ongoing debate between surveillance necessity and privacy concerns, WiFi sensing emerges as a promising solution for efficient data monitoring while safeguarding personal privacy. Unlike traditional surveillance methods relying on camera recordings, WiFi sensing utilizes Channel State Information (CSI) data to detect specific movements, offering advantages such as reduced data storage and processing time. This paper explores the effectiveness of WiFi CSI data in detecting human interactions within radio waves, highlighting its practical usability and enhanced accuracy compared to Received Signal Strength (RSS) measurements. Leveraging Convolutional Neural Networks (CNNs), a blind machine learning paradigm, this study demonstrates the potential of WiFi sensing for human activity recognition (HAR). While previous HAR research predominantly focused on camera feeds and wearable devices, this study pioneers the application of CNNs to WiFi CSI data analysis, showing promising results for occupancy detection, activity recognition, and gesture identification. Although there is room for further improvement in accuracy, this research lays a solid foundation for future studies in the burgeoning field of WiFi sensing-based surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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A New MCGDM Technique to Detect Best Solar Panel Installation Provider Using Hybrid Aggregation Operator Under Triangular Neutrosophic Arena

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International Journal of Applied and Computational Mathematics 2025年第1期11卷 1-56页

作者： Chatterjee, Tanmoy Mondal, Subhabrata Chakraborty, Avishek Department of Mathematics Swami Vivekananda University Barrackpore Kolkata 700121 India Department of Engineering Science Academy of Technology Adisaptagram Hooghly 712502 India

This research work is conducted to propose a novel strategy of acceptable solution of multi -criteria group decision making (MCGDM) problems in triangular fuzzy neutrosophic number environment. The paper represents an innovative and acceptable strategy that is applicable in dealing with the situation of uncertainty, indecisiveness and contradictory information with the aid of triangular fuzzy neutrosophic numbers (TFNNs). In this article, we have analysed the applicability of arithmetic and geometric hybrid aggregation operations (AOs) in triangular fuzzy neutrosophic number (TFNN) environment by extending the operational principles of hybrid aggregation operators already proposed in single valued neutrosophic number environment (SVNN). We have proposed an innovative aggregation operator namely triangular fuzzy neutrosophic number hybrid weighted arithmetic geometric aggregation operator (TFNNHWAGA). We have illustrated some of its basic properties with proofs. The task of solar panel selection is very crucial in the acute situation of growing demand of solar energy as non-conventional power source. The task is quite difficult as there are so many solar panel installation companies in the market with a strong maketing competition between them. Our proposed TFNNHWAGA Operator in TFNN environment is of great help in dealing the situation significantly. We have illustrated a numerical example to figure out an innovative application of TFNNHWAGA to solve a MCGDM problem of selecting best solar panel installation company to illustrate the applicability and effectiveness of the proposed strategy dealing with contradictory criteria in uncertain environment. We have presented unique accuracy and score function for deneutrosophication purposes to select the best alternative out of four solar panel installation companies. We have compared the results with the results obtained using triangular fuzzy neutrosophic weighted arithmetic aggregation operator (TFNWAA) and triangular

关键词： AOs MCGDM SVNN TFNN TFNNHWAGA

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Assessing the Effectiveness of Anomaly Detection in IoT Data Streams with Machine Learning

Assessing the Effectiveness of Anomaly Detection in IoT Data...

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International Conference on computer, Communication, Control and Information technology (C3IT)

作者： Subir Panja Suman Das Arindrajit Pal Computer Science and Engineering Academy of Technology Adisaptagram Hooghly India Computer Science and Business System Academy of Technology) Adisaptagram Hooghly India

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

The proliferation of cyber-attacks and network infiltration has emerged as significant apprehensions in the contemporary utilization of the Internet of Things (IoT). Researchers have introduced several machine learning algorithms, which exhibit deficiencies in terms of binary classification accuracy or multi-class classification performance. This study presents an anomaly-based approach that encompasses major eight distinct machine learning algorithms, (i) Decision Tree (DT), (ii) SVMPolynomial Kernal (SVM-P) (iii) SVM- RBF Kernel(SVM-R) (iv) SVM-Linear Kernal (SVM-L) (v) K-Nearest Neighbours (KNN) (vi) Gaussian Naive Bayes (GNB) (vii) Bernoulli Naive Bayes (BNB) and (viii) AdaBoost with Decision Tree (Ada-DT). The experiment utilized the dataset “UNSW 2018 IoT Botnet Final 10 best Testing” for our research. Performance measures, including as accuracy, precision, recall, and F1-Score are employed to assess and evaluate the performance of the each machine learning algorithm. The DT Classifier demonstrated exceptional accuracy of 99.99%, surpassing expectations in terms of precision, recall, and f1-score for both classes. The significant macro-average Receiver Operating Characteristic Area Under the Curve (ROCAUC) score of 0.91 exhibited by the model reveals its robust overall performance. In contrast, the GNB and BNB models had limits, as evidenced by their lower ROC-AUC scores.

关键词： Accuracy Machine learning algorithms Botnet Gaussian processes Nearest neighbor methods Bayes methods Internet of Things Decision trees Anomaly detection Testing

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Predicting circRNA-Disease Associations by Using Multi-Biomolecular Networks Based on Variational Graph Auto-Encoder with Attention Mechanism

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Chinese Journal of Electronics 2024年第6期33卷 1526-1537页

作者： Jing YANG Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology

CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.

关键词： Knowledge engineering Attention mechanisms Computational modeling Biological system modeling Predictive models Feature extraction Heterogeneous networks Topology Data mining Diseases

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Efficient Brain Tumor Segmentation with Mamba 7

Efficient Brain Tumor Segmentation with Mamba

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7th International Conference of computer and Informatics engineering, IC2IE 2024

作者： Ghosh, Akhilbaran Chakraborty, Subhadeep Academy of Technology Department of Computer Science and Engineering Adisaptagram West Bengal India Techno International New Town Department of Electronics and Communication Engineering West Bengal Kolkata India

ISBN: (纸本)9798331505530

This research investigates the application of Structured State Space Sequence Models (SSMs), particularly the Mamba architecture, for brain tumor segmentation in MRI scans. The Mamba model uses selective state spaces to demonstrate efficient and effective segmentation while maintaining linear scalability. Comparative experiments with state-of-the-art models reveal compelling performance in both model size and segmentation accuracy. Our findings indicate the potential of Mamba-based approaches to surpass transformer-based models for this critical medical imaging task, offering a promising alternative in resource-constrained environments. Further refinements and optimizations of Mamba-based models, along with exploration of interpretability and robustness, present avenues for future research and clinical deployment. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Need of Public-Private Healthcare Collaboration for Managing Seasonal Dengue Fever in West Bengal 22

Need of Public-Private Healthcare Collaboration for Managing...

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22nd IEEE/ACIS International Conference on Software engineering Research, Management and Applications, SERA 2024

作者： Nag, Anwesha Goto, Takaaki Roy, Subhankar Ghosh, Partha Adamas University Kolkata India Toyo University Faculty of Information Sciences and Arts Saitama Japan Academy of Technology Department of Computer Science and Engineering Adisaptagram India

ISBN: (纸本)9798350391343

Dengue fever is mostly prevalent in tropical and subtropical regions, where Aedes mosquitoes, the primary vectors for the virus, thrive in warm and humid environments. In West Bengal, a province in India, the typical duration of the dengue disease spans two to three months, necessitating substantial infrastructure for dengue patients during this period. If the government heavily invests in developing this infrastructure, there's a risk of these facilities remaining underutilized during periods of low dengue incidence. Conversely, without adequate investment, dengue could potentially escalate into an epidemic. This research seeks to identify regions where insufficient infrastructure impedes public healthcare systems from catering to dengue patients. The primary focus of this paper is to evaluate the necessary degree of public-private collaborations needed to address seasonal dengue epidemics and pinpoint specific durations within the healthcare system that require attention. © 2024 IEEE.

关键词： Investments

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Online Retail Customer Segmentation using RFM Quantiles and Clustering Technique 4

Online Retail Customer Segmentation using RFM Quantiles and ...

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4th International Conference on computer, Communication, Control and Information technology, C3IT 2024

作者： Majilya, Mouli Maji, Giridhar Ghosh, Partha Sen, Soumya A.K.Choudhury School of Information Technology University of Calcutta Kolkata India Asansol Polytechnic Department of Electrical Engineering Asansol India Academy of Technology Department of Computer Science and Engineering Adisaptagram India

ISBN: (纸本)9798350389128

In the fiercely competitive landscape of modern business, making effective decisions is imperative for generating substantial revenue. With organizations employing diverse strategies to stay ahead, leveraging data for informed decision-making is crucial. Understanding individual preferences and purchasing behaviors can be challenging, but machine learning techniques offer a solution by organizing and analyzing vast datasets. Customer segmentation, particularly through K-means clustering, is instrumental in identifying target groups with similar attributes and interests. The present study focuses on customer clustering method-ologies to give businesses a competitive edge. By collecting, analyzing, and visualizing customer data, we aim to develop a data science model that utilizes K-means clustering and the RFM model (Recency, Frequency, Monetary) for existing customers. Utilizing the UK's e-commerce dataset, which delves into customer purchasing behaviors, we aim to group customers into clusters such as high-value customers, mid-range, and those at risk of churn based on RFM scores. © 2024 IEEE.

关键词： Sales

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