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TSGN: Transaction Subgraph Networks Assisting Phishing Detection in Ethereum

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Jinhuan Chen, Pengtao Xu, Xinyao Wu, Jiajing Shen, Meng Xuan, Qi Yang, Xiaoniu Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou310023 China ZJUT Binjiang Institute of Artificial Intelligence Hangzhou310056 China Fudan University Adaptive Networks and Control Lab Department of Electronic Engineering School of Information Science and Technology Shanghai200433 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Shenzhen518066 China National Key Laboratory of Electromagnetic Space Security Jiaxing314033 China

Due to the decentralized and public nature of the blockchain ecosystem, malicious activities on the Ethereum platform impose immeasurable losses on users. At the same time, the transparency of cryptocurrency transactions provides a unique opportunity to analyze illegal activities, such as phishing scams, from a network perspective. Most existing phishing scam detection methods focus primarily on analyzing account interaction networks, which limits their ability to uncover transaction behavior patterns embedded within transaction interactions. To address this, we construct the Transaction SubGraph Network (TSGN) by using transaction subgraphs as basic elements and further propose a novel framework for Ethereum phishing account detection. Specifically, we rebuild the graph structures via three well-designed mapping mechanisms, yielding TSGN and its two variants, i.e., Directed-TSGN and Temporal-TSGN, to obtain direction-aware and time-aware transfer flow features. By further incorporating the mapping strategy into transaction multidigraphs, we develop the Multiple-TSGN, which could preserve more transaction flow features while concurrently reducing the time consumption of modeling large-scale networks. TSGN models based on transaction subgraph interactions can capture complex higher-order dependencies, which lay beyond the reach of models that exclusively capture pairwise account interactions. As a general framework, our model can incorporate various feature extraction methods to improve the performance of phishing detection. Extensive experimental results on Ethereum datasets show that our method achieves superior performance in phishing detection, yielding 3.27%∼6.71% relative improvement over previous state-of-the-art. © 2004-2012 IEEE.

关键词： Phishing

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Erratum to: Optimized design of a reinforced exhaust at low velocity (RELV) system for efficient viral aerosol removal in elevators (Building Simulation, (2025), 10.1007/s12273-025-1258-2)

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Building Simulation 2025年 1-1页

作者： Liu, Sumei Xu, Longhui Chen, Bingqian Deng, Zhipeng Du, Chengbo Li, Pin Zhao, Runmin Tianjin Key Laboratory of Indoor Air Environmental Quality Control School of Environmental Science and Engineering Tianjin University Tianjin300072 China School of Energy and Electrical Engineering Qinghai University Xining810016 China BYD Auto Industry Company Limited Shenzhen518119 China Department of Mechanical and Aerospace Engineering College of Engineering and Computer Science University of Central Florida OrlandoFL32816 United States School of Mechanical Engineering Purdue University West LafayetteIN47907 United States Shanghai Junyu Information Technology Co. Ltd Shanghai China

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Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

arXiv

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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A Switched Neural Process for Long-Term Postprandial Glucose Trajectory Prediction Based on Insulin Sensitivity Estimation

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Chen, Jing Shi, Dawei Cai, Deheng Liu, Wei Ji, Linong Shi, Ling Beijing Institute of Technology MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Peking University People’s Hospital Department of Endocrine and Metabolism Beijing100044 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong

Advanced sensing technology has enabled the real-time monitoring of blood glucose (BG), making it possible to explore the BG prediction problem based on continuous observations. In this work, we propose a novel switched neural process (SNP) approach for long-term postprandial BG prediction, which has the probabilistic inference ability conditioned on preprandial BG measurements. Distinct representative neural process (NP) models are constructed to capture the nonlinear BG dynamics based on a designed indicator. This indicator is developed to estimate insulin sensitivity (IS) at meal time by using physiological models, in which the effect of preprandial BG variation on IS estimation is evaluated. Besides, a robust model switching mechanism is designed to determine the appropriate NP model for prediction. The effectiveness of the proposed method is illustrated through the comparative results by using both the in silico data from the U.S. Food and Drug Administration (FDA)-accepted UVA/Padova type 1 diabetes mellitus (T1DM) simulator and the clinical data from 47 T1DM subjects. The obtained results indicate that the proposed method outperforms existing methods, especially for 3- and 4-h prediction, which shows promising potential in predicting postprandial BG levels. © 1963-2012 IEEE.

关键词： Glucose

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Highway self-contained multi-microgrid energy management strategy based on universal gravitation

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Energy 2025年 327卷

作者： Zhang, Lingzhi Shi, Ruifeng Ma, Xiaolei Jia, Limin Lee, Kwang Y. School of Control and Computer Engineering North China Electric Power University Beijing102206 China China Institute of Energy and Transportation Integrated Development North China Electric Power University Beijing102206 China School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Rail Traffic Control & Safety Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering Baylor University WacoTX76798 United States Yonsei Frontier Lab Yonsei University Seoul03722 Korea Republic of

Energy acts as a crucial driving force for transportation, while transportation functions as a significant role within the energy systems. Both sectors share the vital responsibility of enabling a low-carbon transition. The advancement of clean energy and the establishment of green transportation are the keys to achieving sustainable, low-carbon transformation of energy and transportation systems. This study addresses the challenges of energy management strategies within the nexus of energy and transportation, specifically targeting off-grid regions in Western China, where conventional power infrastructure is lacking and road traffic is critical. A two-tiered energy management approach is proposed for interconnected self-contained multi-microgrid (MMG) systems along road networks, inspired by the principles of gravitational law. The proposed framework consists of two phases: the energy exchange among microgrids (MGs) and the charging and discharging of individual MG energy storage systems (ESSs). This structure effectively mitigates the uncertainties inherent in renewable energy sources and load demands. A gravitational scheduling model is first developed by drawing analogies between the elements within the MGs and gravitational principles, determining energy exchanges at the MMG level. Following this, the ESS charging and discharging processes are then dictated by the remaining power imbalance. By optimizing the sequence and hierarchy of energy device interactions, this strategy minimizes operational costs while maximizing the system's self-contained capability. Simulation results provide compelling evidence of the effectiveness of the proposed energy management strategy in reducing operational costs and enhancing system self-contained performance, particularly in remote, off-grid areas of Western China. © 2025 Elsevier Ltd

关键词： Strategic planning

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Unleashing the Potentials of IoT with Focus on Energy and Path Loss for Internet of Medical Things

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Journal of Circuits, Systems and computers 2025年

作者： Hussain, Altaf Li, Shuaiyong Attar, Razaz Waheeb Al Dabel, Maryam M. Bashir, Ali Kashif Alhomoud, Ahmed Hussain, Tariq School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Management Department College of Business Administration Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Department of Computing and Mathematics Manchester Metropolitan University United Kingdom Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Computer Science College of Science Northern Border University Arar91431 Saudi Arabia School of Computer Science and Technology Zhejiang Gongshang University Hangzhou310018 China

The Internet of Things (IoT) encompasses a broad platform of sensor networks incorporating independent wireless networks. With advancements in sensor technology and IoT-enabled networks, their applications in the medical field have led to the development of the Internet of Medical Things (IoMT). In IoMT, sensor nodes monitor and evaluate patient conditions such as heartbeat, blood sugar levels, blood pressure and temperature, and can also remotely track patient activities through remote analysis. These IoMT systems utilize tiny sensors with limited communication ranges to gather essential patient information. Wireless devices are equipped with a short range and need a direct communication path. However, transmitting data from the source node to the destination node ultimately results in energy consumption and path loss. Path loss models and energy consumption models are essential to address these issues. In this paper, we propose a novel routing protocol named Energy Efficient and Path Loss Preserving (EEPLP) for IoMT. The EEPLP protocol focuses on energy efficiency and path loss preservation based on the relay approach. Two models are being proposed, one for path loss and the other for energy consumption. Finally, both models are merged since the major contribution is to avoid path loss and enhance the protocol's energy efficiency. The EEPLP evaluates the state-of-the-art existing approaches of IoMT. The protocol is evaluated by simulating conditions and compared with other similar routing protocols already deployed in the IoMT;it has been observed that the EEPLP scheme has the potential to be maneuvered in IoMT structures with core targets of energy efficiency as well as path loss preservation techniques. © 2025 World Scientific Publishing Company.

关键词： Sensor nodes

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Resilience-oriented intentional islanding of reconfigurable distribution power systems

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Journal of Modern Power Systems and Clean Energy 2019年第4期7卷 741-752页

作者： Mohammad Hossein OBOUDI Mohammad MOHAMMADI Mohammad RASTEGAR Department of Power and Control Engineering School of Electrical and Computer EngineeringShiraz UniversityShirazIran

The participation of distributed energy resources in the load restoration procedure,known as intentional islanding,can significantly improve the distribution system *** system reconfiguration can effectively alter islanding procedure and thus provide an opportunity to supply more demanded energy and reduce distribution system *** addition,high-impact events such as hurricanes and earthquakes may complicate the procedure of load restoration,due to the disconnection of the distribution system from the upstream grid or concurrent component *** paper presents a two-level method for intentional islanding of a reconfigurable distribution system,considering high-impact *** thefirst level,optimal islands are selected according to the graph model of the distribution *** the second level,an optimal power flow(OPF)problem is solved to meet the operation constraints of the islands by reactive power control and demand side *** proposed problem in the first level is solved by a combination of depth first search and particle swarm optimization *** OPF problem in the second level is solved in DIgSILENT *** proposed method is implemented in the IEEE 69-bus test system,and the results show the validity and effectiveness of the proposed algorithm.

关键词： Intentional islanding Active distribution system Distributed energy resource Distribution system reconfiguration Distribution system resilience

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Time-Constrained Model Predictive control for Autonomous Satellite Rendezvous, Proximity Operations, and Docking

arXiv

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arXiv 2025年

作者： Behrendt, Gabriel Hale, Matthew Soderlund, Alexander Phillips, Sean Kain, Evan Georgia Institute of Technology AtlantaGA30332 United States Air Force Research Laboratory Kirtland AFB NM87117 United States School of Electrical and Computer Engineering United States Space Control Branch United States Spacecraft Component Technology United States

This paper presents a time-constrained model predictive control strategy for the six degree-of-freedom autonomous rendezvous, proximity, operations and docking problem between a controllable "deputy" satellite and an uncontrolled "chief" satellite. The objective is to achieve a docking configuration defined by both the translational and attitudinal states of the deputy relative to the chief, whose dynamics are respectively governed by both the Clohessy-Wiltshire equations and Euler’s second law of motion. The proposed control strategy explicitly addresses computational time constraints that are common to state-of-the-art space vehicles. Thus, a time-constrained model predictive control strategy is implemented on a space-grade processor. Although suboptimal with regards to energy consumption when compared to conventional optimal RPO trajectories, it is empirically demonstrated via numerical simulations that the deputy spacecraft still achieves a successful docking configuration while subject to computational time constraints. © 2025, CC BY.

关键词： Predictive control systems

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Sensing With Random Communication Signals

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IEEE Network 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing210044 China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security China Southeast University MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025 IEEE.

关键词： Mobile telecommunication systems

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HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Wang, Xianbin Cheng, Zhipeng Hosseinalipour, Seyyedali Dai, Huaiyu Jiao, Zhenzhen School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Japan Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Suzhou China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering NC State University RaleighNC United States iF-Labs Beijing Teleinfo Technology Company Ltd. CAICT Beijing China

The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect which is underexplored in the literature on VEC-HFL is that vehicles often need to execute multiple ML tasks simultaneously, where this multi-model training environment introduces crucial challenges. First, improper aggregation rules can lead to model obsolescence and prolonged training times. Second, vehicular mobility may result in inefficient data utilization by preventing the vehicles from returning their models to the network edge. Third, achieving a balanced resource allocation across diverse tasks becomes of paramount importance as it majorly affects the effectiveness of collaborative training. We take one of the first steps towards addressing these challenges via proposing a framework for multi-model training in dynamic VEC-HFL with the goal of minimizing global training latency while ensuring balanced training across various tasks—a problem that turns out to be NP-hard. To facilitate timely model training, we introduce a hybrid synchronous-asynchronous aggregation rule. Building on this, we present a novel method called Hybrid Evolutionary And gReedy allocaTion (HEART). The framework operates in two stages: first, it achieves balanced task scheduling through a hybrid heuristic approach that combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms (GA);second, it employs a low-complexity greedy algorithm to determine the training priority of assigned tasks on vehicles. Experiments on real-world datasets demonstrate the superiority of HEART over existing methods. © 2025, CC BY-NC-ND.

关键词： Federated learning

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