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4th international Conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Lazzaro, Ilaria Milano, Marianna Department of Medical and Surgical Sciences University "Magna Græcia" Catanzaro88100 Italy Department of Experimental and Clinical Medicine University Magna Græcia Catanzaro88100 Italy

ISBN: (纸本)9783031812460

Network theory, in particular complex networks, has undergone considerable development finding its way into many real-world applications. However, they have several different types of relationships that cannot be represented by a mono layer network [11]. In fact, multilayer networks explicitly incorporate multiple channels, creating the right context to describe interconnected systems through different related layers, where nodes are the entities of the system and the edges represent the interactions between them. The application fields that may be modelled by multilayer networks range from human and social systems [8], to technological and transport systems, including biology and medicine [4]. Although various approaches for the visualization of multilayer networks have been suggested in recent years, this is an evolving field. In this work, we present an overview of the tools exploited for the visualization of multilayer networks. Then, we present a comparison among different tools through a case study represented by a real multilayer network. The network under investigation is a multilayer network composed of 8, 392 nodes and 128, 199 edges distributed over 2 layer that relate disease and drug. We propose methods for visualisation of the network that provide a topological analysis, attempting to derive the main measurement metrics. Through the visualisation carried out on the dataset, we will attempt to demonstrate the different layout configurations that a network can assume and the parameters that can be associated to each tool used in order to derive real scientific information. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multilayer neural networks

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Backdoor Attack Based on Privacy Inference against Federated Learning 7

Backdoor Attack Based on Privacy Inference against Federated...

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7th international symposium on Autonomous systems, ISAS 2024

作者： Wu, Duo Hao, Lingguang Wei, Bing Hao, Kuangrong Han, Tao He, Langping Donghua University College of Information Science and Technology Shanghai China China Telecom Research Institute College of Big Data and Artificial Intelligence Shanghai China

ISBN: (纸本)9798350363173

As a pioneering distributed learning framework, federated learning (FL) has gained widespread adoption. It operates collaboratively among participants, with communication limited to sharing model parameters between the server and participants. However, FL is also more susceptible to active attacks from malicious insiders. Poisoned updates submitted by attackers can degrade the performance of the global model. Previous research only considered using the naive data of malicious clients for backdoor poisoning, and therefore achieved limited backdoor attack success rates. In this paper, we propose a novel Federated Backdoor Attack based on Privacy Inference (FBA-PI). Combining privacy inference based on generative adversarial networks (GAN), the attacker first infers sensitive information from victim participants and then injects backdoor triggers into the naive and generated data. Finally, malicious clients can embed backdoor semantics into the global model by participating in regular federated aggregation. Extensive experiments on the MNIST dataset verify the effectiveness of our proposed method. © 2024 IEEE.

关键词： Generative adversarial networks

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Wavelet-Based Prototype Learning for Medical Image Classification 22

Wavelet-Based Prototype Learning for Medical Image Classific...

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22nd IEEE international symposium on Intelligent systems and Informatics, SISY 2024

作者： Lieb, Hanna-Georgina Kaszta, Tamas Csato, Lehel Babeş-Bolyai University Computer Science Department Cluj-Napoca Romania

ISBN: (纸本)9798350385601

The need for transparent decisions in safety-critical domains is a burning necessity, specifically given the widespread adoption of intelligent systems in diverse areas, like e.g, medical data processing. In this paper, we continue to explore the prototype-based networks, which provide interpretable predictions of the input image. The mechanism is via prototypes, a structural part of the decision-making system. We apply the interpretable architecture - named WaveProtoPNet - on medical image data and explore the prototype-based architecture. We show the interpretability and the reduced parameter size of the model, but also focus on its performance on the NCT-CRC-HE-IOOK dataset on human tissue types. By using wavelets, the parameters are reduced substantially, leading to significant saving in both memory and computation. As with all prototype-based methods, interpretability is a consequence of prototype locations, providing an 'explanatory' insight into the classification. Our model remains highly adaptable, as images with custom sizes could be learnt. © 2024 IEEE.

关键词： Intelligent systems

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Securing FANET using federated learning through homomorphic matrix factorization

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international Journal of Information Technology (Singapore) 2025年第1期17卷 17-36页

作者： Banerjee, Aiswaryya Mahato, Ganesh Kumar Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote 791113 India

As Flying Ad Hoc networks (FANETs) continue to advance, ensuring robust security, privacy, and data reliability remains a significant challenge. This research presents a novel framework known as HE-FSMF-short for Homomorphic Encrypted Federated Secure Matrix Factorization-specifically designed to tackle these challenges. HE-FSMF integrates matrix factorization with federated learning and homomorphic encryption to enhance both security and efficiency in FANET environments. Matrix factorization, commonly used in recommendation systems, is adapted here to address the unique complexities of FANETs. By leveraging detailed feature extraction through the VGG-16 model, HE-FSMF ensures precise and secure data processing even in dynamic and high-mobility settings. The incorporation of homomorphic encryption protects data throughout cloud-based computations, maintaining privacy and integrity without compromising performance. Additionally, HE-FSMF features mechanisms to verify the accuracy and authenticity of results, which is crucial for establishing trust in distributed systems. This framework not only enhances learning efficiency and improves data transmission rates but also provides strong safeguards for sensitive information. HE-FSMF offers a robust solution for advancing FANET capabilities, making it a valuable tool for secure and efficient communication in the increasingly interconnected and rapidly evolving landscape of networked systems. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： FANET Federated learning Homomorphic encryption UAV

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A New Scheme for Abnormal Data Transmission Behavior Detection with Network-Wide Perspective 10

A New Scheme for Abnormal Data Transmission Behavior Detecti...

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10th international symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Xiao, Weikang Xie, Yi Sun Yat-sen University School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350362930

distributed network threat events are characterized by large scale and wide coverage, and seriously threaten the stable operation of data communication networks. Therefore, achieving early detection of distributed network events (DNEs) has been one of the major challenges faced by global network operators. Recently, due to the enhanced utilization of victim contextual information in event detection, graph anomaly detection methods have garnered widespread attention for their superior performance in event detection compared to traditional single-point methods. However, these methods usually only focus on local anomalies and have difficulties in fully exploiting the interconnected nature of networks for the early detection of DNEs. To overcome these limitations, in this study we detect DNEs by exploiting the spatiotemporal morphology in the forwarding behavior of the network during their propagation. The proposed approach uses a novel Hidden Markov Random Field (HMRF) to characterize the temporal variations in the transmission behavior of the network. In order to comprehensively delineate the differences in the impact among hidden states, this model employs both Continuous Bag of Words (CBOW) and a potential model with dynamic weights. These components are utilized to characterize the relationships between the hidden states of network entities and links and their spatiotemporal neighbors. To adapt to the statistical distributions of traffic in different scenarios, a Deep Neural Network (DNN) is used to represent the probabilistic relationships between each hidden state and its associated traffic features. By using this model, it is possible to calculate the current hidden state field of the target network. The early detection of events is achieved based on the spatiotemporal morphology of the hidden state field and its correlation with DNEs. The experimental results demonstrate that our proposed framework outperforms the existing baseline methods in real datasets and

关键词： Morphology

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Digital Twin and federated learning enabled cyberthreat detection system for IoT networks

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FUTURE GENERATION computer systems-THE international JOURNAL OF ESCIENCE 2024年 161卷 701-713页

作者： Salim, Mikail Mohammed Camacho, David Park, Jong Hyuk Seoul Natl Univ Sci & Technol Dept Comp Sci & Engn 232 Gongneung Ro Seoul 01811 South Korea Univ Politecn Madrid Sch Comp Syst Engn Madrid 28038 Spain

The widespread deployment of Internet of Things (IoT) devices across various smart city applications presents significant security challenges, increased by the rapidly evolving landscape of cyber threats. Traditional security solutions, including those using Federated Learning with federated averaging, suffer from inefficiencies due to random node selection and partial data sampling, which can hinder the detection of comprehensive networkwide attacks. This paper introduces a novel Cyberthreat Detection System for IoT networks that leverages Digital Twin technology and an optimized Federated Learning approach. Our hypothesis implies integrating Digital Twin models within an IoT security framework to improve real-time cyberthreat detection capabilities. We implement a 'Adaptive Thresholding with Early Stopping method' based methodology in Federated Learning to systematically train and aggregate local models based on predefined training rounds, thereby ensuring that all local models contribute to the global model until a target accuracy is achieved. This method significantly improves the detection of zero-day attacks by reducing dependency on random selections and partial data. The system architecture features Digital Twins of IoT medical infrastructure components-such as radiology, intensive care, and outpatient care-positioned at the network edge to minimize latency and bandwidth usage. Comparative evaluations of our model against traditional federated averaging methods demonstrate superior performance, with enhancements in model aggregation efficiency evidenced by higher F1 scores and reduced CPU usage. Specifically, our distributed digital twin environment at the edge layer shows 14% and 33% latency reductions compared to fog and cloud-based implementations, respectively. This study highlights the potential of Digital Twin and advanced Federated Learning methodologies to secure IoT networks against evolving and growing cyber threats.

关键词： Digital twins Internet of things Energy-efficient federated learning Intrusion detection Zero-day attack detection

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An AI-integrated Enhanced Anatomical Brain Tumor Segmentation in Substantia Nigra for Early Disease Identification using Deep Resnet50-CNN

An AI-integrated Enhanced Anatomical Brain Tumor Segmentatio...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Ganeshkumar, P.V. Prasanna, S. Vels Institute of Science Technology and Advanced Studies Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Application Chennai India

ISBN: (纸本)9798350375442

The early identification of brain tumours is crucial for improving patient prognosis and treatment planning. Recent advancements in neuroimaging techniques have paved the way for the enhanced detection and characterization of these lesions through CT or MRI image scans. During medical image analysis, the feature analysis is important factor to know the object relations, entity resolution information and disease relational properties, from the prior analysis, machine learning models are mostly covered to segment the disease region by retaining non scaling factors. Due to lower precision class, poor segmentation leads low accuracy to identifying the disease regions to produce higher false rates. In this research explores a novel approach to brain tumour identification utilizing Neuromelanin-Sensitive Magnetic Resonance Imaging (NMS-MRI). We propose a novel preprocessing methodology leveraging the Weigner filter to enhance image normalization and overall quality, facilitating more accurate subsequent analyses. Following preprocessing, we employ Cascading Slice Window Volumetry Segmentation (CSWVM) to achieve refined tumor delineation in three-dimensional MRI scans. The segmentation results are further refined through Cross Modality Particle Swarm Optimization (CMPSO)-based feature selection, which effectively identifies the most relevant features across various imaging modalities to optimize the classification process. To classify the detected tumors, further to integrate a ResNet 50-Convolutional Neural Network (ResNet50-CNN), leveraging its deep learning capabilities for robust pattern recognition in neuroimaging data. The proposed system proves higher performance in precision, recall, f1 score by attaining feature selection, and deep learning algorithms, ultimately contributing to enhanced accuracy and effectiveness in brain tumor identification. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Online education course recommendation algorithm based on multimodal graph attention network 3

Online education course recommendation algorithm based on mu...

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3rd international symposium on computer Applications and Information systems, ISCAIS 2024

作者： Li, Gang Hou, Yuchen School of Computer and Communication Engineering Beijing University of Science and Technolgy Beijing100083 China

ISBN: (纸本)9781510681316

With the rapid development of online learning platforms, learners have more opportunities to choose courses. However, many mainstream education platforms currently lack personalized recommendation process, and due to the diversity of course quality and content, many students find it difficult to choose more suitable courses for themselves. Therefore, personalized course recommendation plays a core role in online education platforms. The existing art education curriculum has a variety of attributes, and these attributes also include a variety of modal forms (such as course video, course pictures and course introduction);Learners themselves also have a variety of attributes, their age, educational background have an impact on the recommendation results;There is also information exchange between learners and the course, such as rating on the course, which can reflect the degree of learners' interest in the course and connect them to each other. Therefore, this paper constructs a knowledge graph, which regards learners and courses as entities and the multi-modal form of courses as course attributes. Taking learners' personal information as learner attributes and different information exchanges between learners and courses as relationships .We use the applicability of graph networks to graph structures to capture learners' preferences for different modal attributes, so as to make more accurate personalized recommendations. In this paper, MOOCCube is used as the main data set, and experiments show that the proposed algorithm is better than the baseline. © 2024 SPIE.

关键词： E-learning

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An Epigraph Approach for distributed Economic Dispatch in General Dynamic Directed networks 6

An Epigraph Approach for Distributed Economic Dispatch in Ge...

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6th international Conference on Electronic Engineering and Informatics, EEI 2024

作者： Lü, Qingguo Zhou, Qixing Zhang, Keke Wang, Zheng Li, Huaqing College of Computer Science Chongqing University Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (纸本)9798350353594

The intricate complexities of modern energy systems highlight the cruciality of nimble and trustworthy methodologies for the smooth functioning of intelligent grids. This article delves into the economic dispatch problem (EDP) within smart grids, with the objective of maximizing generator scheduling efficiency to meet total demand while minimizing expenditure. We present a comprehensively decentralized algorithm, tailored specifically for addressing the EDP in directed networks, while accounting for the dynamics of communication environments. This algorithm integrates the epigraphic methodology and relies solely on row-stochastic weighting matrices for its execution. We provide theoretical justifications to ensure the convergence of our proposed algorithm, thereby exhibiting its ability to efficiently dispatch the optimal economic solution. To substantiate the efficacy of our algorithm, we conduct simulations, furnishing empirical proof of its performance. © 2024 IEEE.

关键词： Stochastic systems

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A Multi-objective Planning Method Based on Transmission and Distribution Coordination Considering Economy and Green 14

A Multi-objective Planning Method Based on Transmission and ...

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14th IEEE international symposium on Power Electronics for distributed Generation systems, PEDG 2023

作者： Xiaoguang, Qi Peng, Xi Lu, Liu Ying, Wang Lijie, Zhang Aowei, Li State Grid Hebei Electric Power Co. Ltd Institute of Economics and Technology Shijiazhuang050011 China Key Laboratory of Control of Power Transmission and Conversion Shanghai200240 China

ISBN: (纸本)9798350328233

In new power system with new energy as the main body, economy and green requirements are essential, while the coupling degree of transmission and distribution networks is increasing, the traditional independent and split planning methods are difficult to achieve global optimization. In order to meet economy and green, a multi-objective planning method based on transmission and distribution coordination is proposed. The model objectives are to minimize the total cost of investment and operation, to minimize the curtailment rate of new energy, and to minimize the carbon emission of transmission and distribution networks. AC power flow is used to account for node power balance, unit output and ramp-up constraints, N-1 constraint and so on under temporal scenarios. The decomposition optimization modeling idea is adopted so as to decompose the model into a transmission network planning sub-problem and multiple distribution network planning sub-problems. The model is solved effectively by NSGA-III embedded with ATC algorithm. Finally, the optimal solution set and the optimal compromise result are given by case study, which verifies the equilibrium and effectiveness of the method. © 2023 IEEE.

关键词： Electric power transmission networks

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