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7th IEEE Eurasian Conference on Educational Innovation, ECEI 2024

作者： Yang, Sheng-Yuan Lee, Hsiu-Chia Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan

ISBN: (纸本)9798350307207

Owing to the development of the Internet of Things (IoT), smart electrical appliances bring convenience to life. However, smart home appliances are too cumbersome as their interface operations are mostly not intuitive with less practicability than expected. Taiwan has an island climate, and the summer weather is scorching. An easy-to-use and practical electric fan is indispensable for cooling off the heat, which is the research object of this study to make users feel comfortable. In this study, image recognition technology was introduced. The user turns on the fan manually or through the mobile APP, and selects the tracking mode through the mode function. The camera with the fan detects the user's position and then starts to track the user. The device is equipped with a smoke sensor and a camera to monitor the user's home. The fan proves the feasibility of the conceptual architecture. © 2024 IEEE.

关键词： Image recognition

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PROMIPL: A Probabilistic Generative Model for Multi-Instance Partial-Label Learning 24

PROMIPL: A Probabilistic Generative Model for Multi-Instance...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Yang, Yin-Fang Tang, Wei Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education Key Laboratory of Computer Network China

ISBN: (纸本)9798331506681

Multi-instance partial-label learning (MIPL) tackles scenarios where each training sample is represented as a multiinstance bag associated with a candidate label set. This set contains one true label and several false positives. Existing MIPL algorithms have predominantly focused on mapping multiinstance bags to candidate label sets for disambiguation. However, these algorithms may not be adequately generalizable in intricate real-world situations due to their reliance on heuristic methods for identifying true labels. In this paper, we propose PROMIPL, i.e., a PRObabilistic generative model for Multi-instance partiallabel learning, to address these challenges. PROMIPL is the first attempt to explore the probabilistic generative model to infer latent ground-truth labeling information from the data generation process in multi-instance partial-label learning. Besides, the discovered underlying structures also provide improved explanations of the classification predictions. To circumvent the computationally intensive process of training the generative model, we formulate a unified variational lower bound within the stochastic gradient variational Bayesian framework for the model parameters. Experimental results from benchmark and realworld datasets show that our proposed PROMIPL is competitive or superior to the state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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Multi-class Bitcoin mixing service identification based on graph classification

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Digital Communications and networks 2024年第6期10卷 1881-1893页

作者： Xiaoyan Hu Meiqun Gui Guang Cheng Ruidong Li Hua Wu School of Cyber Science&Engineering Southeast UniversityNanjing 211189China Purple Mountain Laboratories for Network and Communication Security Nanjing 211111China Jiangsu Provincial Engineering Research Center of Security for Ubiquitous Network Nanjing 211189China Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationChina Research Base of International Cyberspace Governance Southeast UniversityNanjing 211189China Institute of Science and Engineering Kanazawa UniversityKakumaKanazawa920-1192Japan

Due to its anonymity and decentralization,Bitcoin has long been a haven for various illegal *** generally legalize illicit funds by Bitcoin mixing ***,it is critical to investigate the mixing services in cryptocurrency anti-money *** studies treat different mixing services as a class of suspicious Bitcoin ***,they are limited by relying on expert experience or needing to deal with large-scale *** far,multi-class mixing service identification has not been explored *** is challenging since mixing services share a similar procedure,presenting no sharp ***,mixing service identification facilitates the healthy development of Bitcoin,supports financial forensics for cryptocurrency regulation and legislation,and provides technical means for fine-grained blockchain *** paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification(BMSI-GC)***,BMSI-GC constructs 2-hop ego networks(2-egonets)of mixing services based on their historical ***,it applies graph2vec,a graph classification model mainly used to calculate the similarity between graphs,to automatically extract address features from the constructed ***,it trains a multilayer perceptron classifier to perform classification based on the extracted ***-GC is flexible without handling the full-size network and handcrafting address ***,the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for *** experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification,achieving an average identification F1-score of 95.08%.

关键词： Bitcoin Anti-money laundering Mixing service Ego network Graph classification

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A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

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computers, Materials & Continua 2024年第1期78卷 569-588页

作者： Haizhen Wang Jinying Yan Na Jia College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqiharChina

Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured *** extraction of encrypted traffic attributes and their subsequent identification presents a formidable *** existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s *** the present study,a new model,referred to as UD-VLD(Unbalanced Dataset-VAE-LSTM-DRN),was proposed to address above *** proposed model is an encrypted traffic identification model for handling unbalanced *** encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced *** enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related ***,the data is classified and *** UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced *** UD-VLD model was tested using the publicly available Tor dataset and VPN *** UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC *** results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced ***,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic *** can serve as a novel solution for encrypted traffic identification.

关键词： Data enhancement LSTM deep residual network VAE

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A Resource-Efficient Multi-core Multi-thread RISC-V-based System-on-Chip 21

A Resource-Efficient Multi-core Multi-thread RISC-V-based Sy...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Kieu-Do-Nguyen, Binh Nguyen, Khai-Duy Binh, Nguyen The Dang, Tuan-Kiet Le, Duc-Hung Pham-Quoc, Cuong Tran, Ngoc-Thinh Pham, Cong-Kha Hoang, Trong-Thuc Department of Computer and Network Engineering Tokyo Japan Faculty of Computer Science and Engineering Hochiminh Viet Nam Faculty of Electronics and Telecommunications Hochiminh Viet Nam

ISBN: (纸本)9798350377088

In the past, a small size microcontroller was needed to control and gather data from resource-constrained electronic devices aimed at Internet of Things (IoT) applications. While the server nodes receive the requests with a high demand for calculation. However, to minimize network traffic transactions and the overall workload that the server node must handle, a portion of these operations must now be completed locally. Additionally, multiple tasks must be processed simultaneously by IoT devices. In this study, we suggested a multi-core, multi-thread enable System-on-Chip (SoC) that consume small resources. The suggested architecture achieves a notable speed increase over single-core design by utilizing the advantages of RISC-V Instruction Set Architecture (ISA). © 2024 IEEE.

关键词： System-on-chip

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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Cache Allocation in Multi-Tenant Edge Computing: An Online Model-Based Reinforcement Learning Approach

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IEEE Transactions on Cloud Computing 2025年第2期13卷 459-472页

作者： Ben-Ameur, Ayoub Araldo, Andrea Chahed, Tijani Dán, György Institut Polytechnique de Paris SAMOVAR Telecom SudParis Palaiseau91120 France KTH Royal Institute of Technology Division of Network and Systems Engineering School of Electrical Engineering and Computer Science Stockholm11428 Sweden

We consider a network Operator (NO) that owns Edge Computing (EC) resources, virtualizes them and lets third party Service Providers (SPs) run their services, using the allocated slice of resources. We focus on one specific resource, i.e., cache space, and on the problem of how to allocate it among several SPs in order to minimize the backhaul traffic. Due to confidentiality guarantees, the NO cannot observe the nature of the traffic of SPs, which is encrypted. Allocation decisions are thus challenging, since they must be taken solely based on observed monitoring information. Another challenge is that not all the traffic is cacheable. We propose a data-driven cache allocation strategy, based on Reinforcement Learning (RL). Unlike most RL applications, in which the decision policy is learned offline on a simulator, we assume no previous knowledge is available to build such a simulator. We thus apply RL in an online fashion, i.e., the model and the policy are learned by directly perturbing and monitoring the actual system. Since perturbations generate spurious traffic, we thus need to limit perturbations. This requires learning to be extremely efficient. To this aim, we devise a strategy that learns an approximation of the cost function, while interacting with the system. We then use such an approximation in a Model-Based RL (MB-RL) to speed up convergence. We prove analytically that our strategy brings cache allocation boundedly close to the optimum and stably remains in such an allocation. We show in simulations that such convergence is obtained within few minutes. We also study its fairness, its sensitivity to several scenario characteristics and compare it with a method from the state-of-the-art. © 2013 IEEE.

关键词： Contrastive Learning

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Key Agreement and Management Scheme Based on Blockchain for 5G-Enabled Vehicular networks

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China Communications 2025年第3期22卷 270-287页

作者： Wang Zhihua Wang Shuaibo Wang Haofan Li Jiaze Yao Yizhe Wang Yongjian Yang Xiaolong School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Cyber Science and Engineering Zhengzhou UniversityZhengzhou 449001China Lab of National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029China

5G technology has endowed mobile communication terminals with features such as ultrawideband access,low latency,and high reliability transmission,which can complete the network access and interconnection of a large number of devices,thus realizing richer application scenarios and constructing 5G-enabled vehicular ***,due to the vulnerability of wireless communication,vehicle privacy and communication security have become the key problems to be solved in vehicular ***,the large-scale communication in the vehicular networks also makes the higher communication efficiency an inevitable *** order to achieve efficient and secure communication while protecting vehicle privacy,this paper proposes a lightweight key agreement and key update scheme for 5G vehicular networks based on ***,the key agreement is accomplished using certificateless public key cryptography,and based on the aggregate signature and the cooperation between the vehicle and the trusted authority,an efficient key updating method is proposed,which reduces the overhead and protects the privacy of the vehicle while ensuring the communication ***,by introducing blockchain and using smart contracts to load the vehicle public key table for key management,this meets the requirements of vehicle traceability and can dynamically track and revoke misbehaving ***,the formal security proof under the eck security model and the informal security analysis is conducted,it turns out that our scheme is more secure than other authentication schemes in the vehicular *** analysis shows that our scheme has lower overhead than existing schemes in terms of communication and computation.

关键词： blockchain certificateless public key cryptography 5G vehicular networks key agreement key management

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Dynamic Reinforcement Learning for Optimal Go AI Training: Adaptive Adjustment and Optimization

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 292-302页

作者： Zhang, Chunjiong Shan, Gaoyang Lim, Junghyun Roh, Byeong-Hee Ajou University Department of AI Convergence Network Suwon16499 Korea Republic of Ajou University Department of Software and Computer Engineering Suwon16499 Korea Republic of

Go is a popular strategy game today, but due to its large search space and task complexity, ensuring stable AI implementation is challenging. Specifically, Go AI training requires setting a fixed optimal learning rate and schedule, which demands significant TPU and GPU resources. To facilitate Go-AI learning, this research explores adaptive adjustment and optimization techniques for dynamic reinforcement learning neural networks. First, we introduce a dynamic batch size technique that adjusts data volume at each training phase and incorporates dynamic network structure search, considering the number of network layers and residual blocks. Second, we propose a dynamic network topology that automatically modifies the learning rate based on the training batch size at various training phases. Our approach outperforms the baseline in terms of stability and model convergence speed. In 100 games, the Go-AI model achieved a 100% victory rate below the 7th rank and a 98% win rate at the 9th rank and higher. © 1975-2011 IEEE.

关键词： Adversarial machine learning

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Method of Fundamental Solutions for Doubly Periodic Potential Problems 10th

Method of Fundamental Solutions for Doubly Periodic Potentia...

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10th International Conference on Mathematics and Computing, ICMC 2024

作者： Ogata, Hidenori Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Chofu182-8585 Japan

ISBN: (纸本)9789819720651

In this paper, we propose a method of fundamental solution for two-dimensional doubly periodic problems, especially potential flow problems with a doubly periodic array of obstacles. In the proposed method, we approximate the solution, which involves doubly periodic functions, by a linear combination of the logarithmic potentials consisting of the theta functions. The method inherits the efficiency of the ordinary method of fundamental solutions and gives an approximate solution that has the same periodicity as the one of the exact solution. Numerical examples show the efficiency of the presented method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Efficiency

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