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25th International Conference on Information Security Applications, WISA 2024

作者： Lee, Taeyang Department of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul Korea Republic of

ISBN: (纸本)9789819616237

Smart meters play a crucial role in accurately measuring power usage and distribution data in microgrids. However, fine-grained data collection raises significant privacy concerns. To address this, it is essential to develop a distributed power trading system that is both trustworthy and privacy-preserving. In this paper, we propose a peer-to-peer power trading mechanism that ensures privacy through identity verification based on decentralized identifiers, zero-knowledge proofs, and pseudonyms. Besides, we introduce an efficient claims verification scheme with Boneh-Lynn-Shacham aggregate signature. We also validate the security of our proposed mechanism through the Real-or-Random model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Groundbreaking taxonomy of metaverse characteristics

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Artificial Intelligence Review 2025年第8期58卷 1-50页

作者： Sadeghi-Niaraki, Abolghasem Rahimi, Fatema Azlan, Nur Alya Emanuelle Binti Song, Houbing Ali, Farman Choi, Soo-Mi Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea Republic of BaltimoreMD21250 United States Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

The Metaverse, a dynamic and immersive virtual realm, has captured the imagination of researchers and enthusiasts worldwide. This survey paper aims to introduce a groundbreaking taxonomy for the characteristics of the Metaverse offering a structured and adaptable framework that extends beyond existing categorizations by incorporating dynamic transformations. Unlike prior taxonomies, which often focus on fixed attributes, our approach emphasizes the dynamic evolution of Metaverse characteristics. Through an extensive review of published literature, this study explores key technological, social, economic, and ethical dimensions of the Metaverse. It introduces a process-oriented classification based on 23 distinct characteristics, including immersification, spatiotemporalification, interactification, persistentification, presentification, personification, unification, imaginification, economification, uncertaintification, and credification. By mapping these evolving aspects, we provide a structured and future-proof foundation for understanding the Metaverse’s continuous development. This survey establishes a new standard for comprehensiveness and innovation, shedding light on the diverse facets that have been explored in literature. Through this novel taxonomy, we provide a detailed map of the current landscape and offer insights that pave the way for future research and development in this burgeoning digital frontier. © The Author(s) 2025.

关键词： Comprehensive analysis Futuristic exploration Metaverse characteristics Paradigm-shifting taxonomy Revolutionary categorization

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Discovery of 6G Services and Resources in Edge-Cloud-Continuum

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IEEE Network 2025年第3期39卷 223-232页

作者： Farhoudi, Mohammad Shokrnezhad, Masoud Taleb, Tarik Li, Richard Song, JaeSeung Oulu University Center of Wireless Communications Oulu90570 Finland Electrical Engineering and Information Technology Bochum44801 Germany Sejong University Department of Convergence Engineering for Intelligent Drone Seoul05006 Korea Republic of Southeast University School of Computer Science and Engineer Nanjing210096 China

The advent of 6G networks will present a pivotal juncture in the evolution of telecommunications, marked by the proliferation of devices, dynamic service requests, and the integration of edge and cloud computing. In response to these transformative shifts, this paper proposes a service and resource discovery architecture as part of service provisioning for the future 6G edge-cloud-continuum. Through the architecture’s orchestration and platform components, users will have access to services efficiently and on time. Blockchain underpins trust in this inherently trustless environment, while semantic networking dynamically extracts context from service requests, fostering efficient communication and service delivery. A key innovation lies in dynamic overlay zoning, which not only optimizes resource allocation but also endows our architecture with scalability, adaptability, and resilience. Notably, our architecture excels at predictive capabilities, harnessing learning algorithms to anticipate user and service instance behavior, thereby enhancing network responsiveness and preserving service continuity. This comprehensive architecture paves the way for unparalleled resource optimization, latency reduction, and seamless service delivery, positioning it as an instrumental pillar in the unfolding 6G landscape. Simulation results show that our architecture provides near-optimal timely responses that significantly improve the network’s potential, offering scalable and efficient service and resource discovery. © 1986-2012 IEEE.

关键词： Semantics

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Security Function for 5G-Advanced and 6G: Malicious UE Detection in Network Slicing 25th

Security Function for 5G-Advanced and 6G: Malicious UE Det...

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25th International Conference on Information Security Applications, WISA 2024

作者： Park, Jaehyoung Kim, Jihye Lee, Jong-Hyouk Department of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of

ISBN: (纸本)9789819616237

Starting with Software Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies, network slicing is emerging as a critical technology in 5G-Advanced and 6G mobile communications to allocate logical networks end-to-end, meeting diverse service requirements. However, the advent of network slicing introduces new security threats, including inter-slice security, intra-slice security, and lifecycle security of network slicing. While recent research has focused on security orchestration technologies using SDN/NFV for network slicing and AI-based attack detection, there remains a gap in research on security by design within the components of 3GPP 5G systems. Therefore, this paper investigates security by design in the 5G-Advanced and 6G architectures. To address these issues, we propose a malicious User Equipment (UE) detection function to identify attacks within the network-slicing environment. We illustrate the interaction between the detection function and other 3GPP 5G system components for network slicing security with its algorithm for detecting malicious UEs in network slices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Malicious UE Network Slicing Security by Design

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ARP Spoofing Mitigation for the E2 Interface in Open RAN: An xApp Approach 25th

ARP Spoofing Mitigation for the E2 Interface in Open RAN:...

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25th International Conference on Information Security Applications, WISA 2024

作者： Kim, Jihye Park, Jaehyoung Lee, Jong-Hyouk Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of Department of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of

ISBN: (纸本)9789819616237

In the 5G-Advanced and 6G era, the Open Radio Access Network (Open RAN) will support hardware and software interoperability between different vendors. The O-RAN Alliance, which leads the Open RAN initiative, has released standards addressing potential threats and mitigation requirements to the framework. While the standards specify Man-in-the-Middle (MitM) attacks on the E2 interface and recommend using IPsec, a Layer 3 security protocol, they do not cover defenses against Layer 2 attacks, such as Address Resolution Protocol (ARP) spoofing. This paper proposes an xApp with preventive and reactive mechanisms to protect the E2 interface from ARP spoofing in Open RAN. The xApp proactively prevents attacks through a static ARP table and continuously monitors the E2 interface to detect and react to the ARP spoofing, offering better network resilience than existing methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Radio access networks

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Multi-modal Interaction with Virtual Agents: A Pick-up and Placement Case Study

Multi-modal Interaction with Virtual Agents: A Pick-up and P...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Jalal Safari Bazargani Soo-Mi Choi Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This paper presents a multi-modal human-agent interaction framework that integrates hand tracking, voice commands, user intention interpretation, and scene understanding using large language and vision models for object pick-up and placement tasks in VR. Scene understanding integrates object tags and ChatGPT-4V’s object detection. User commands are processed via the LLaMA-3.2 model, which interprets user intention, identifies task and reference objects, and spatial relationships for task execution. An initial experiment with 15 participants showed that the multimodal approach achieved the fastest task completion times and highest user satisfaction. While hand-only interaction was comparable, voice-only interaction was significantly slower.

关键词： Hands Solid modeling Three-dimensional displays Conferences Virtual reality Object detection User interfaces Object recognition

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Multi-modal Interaction with Virtual Agents: A Pick-up and Placement Case Study

Multi-modal Interaction with Virtual Agents: A Pick-up and P...

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Bazargani, Jalal Safari Choi, Soo-Mi Sejong University Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone Xr Research Center Seoul South Korea

ISBN: (纸本)9798331514846

This paper presents a multi-modal human-agent interaction framework that integrates hand tracking, voice commands, user intention interpretation, and scene understanding using large language and vision models for object pick-up and placement tasks in VR. Scene understanding integrates object tags and ChatGPT-4V's object detection. User commands are processed via the LLaMA-3.2 model, which interprets user intention, identifies task and reference objects, and spatial relationships for task execution. An initial experiment with 15 participants showed that the multimodal approach achieved the fastest task completion times and highest user satisfaction. While hand-only interaction was comparable, voice-only interaction was significantly slower. © 2025 IEEE.

关键词： Multi-modal Interaction Pick-up and Placement Virtual Agent Virtual Reality

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V-DCRNN: Virtual Network-Based Diffusion Convolutional Recurrent Neural Network for Estimating Unobserved Traffic Data

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IEEE Transactions on intelligent Transportation Systems 2025年

作者： Yoon, Chanyoung Yim, Soobin Yoo, Sangbong Jung, Chanyoung Yeon, Hanbyul Jang, Yun Sejong University Department of Convergence Engineering for Intelligent Drone and Convergence Engineering for Artificial Intelligence Seoul Gwangjin05006 Korea Republic of Laboratory Seoul Seongbuk02792 Korea Republic of Sejong University Department of Convergence Engineering for Intelligent Drone and Computer Science and Engineering Seoul Gwangjin05006 Korea Republic of Hyundai Glovis Seoul Gangnam06172 Korea Republic of

Several studies have analyzed traffic patterns using Vehicle Detector (VD) and Global Positioning System (GPS) data. VD records the speed of vehicles passing through detectors, GPS data captures traffic speed on the roads. However, unobserved data gaps may arise due to physical malfunctions of sensors in VD data or interruptions in satellite signal reception for GPS data. Unobserved data adds complexity to the analysis and prediction of urban traffic networks. To tackle this challenge, researchers have attempted to estimate unobserved data using spatiotemporal patterns, but approaches that rely solely on past time points are inherently less reliable. In this study, we propose a Virtual network-based Diffusion Convolutional Recurrent Neural Network (V-DCRNN) for estimating unobserved speed data in urban traffic networks using a virtual network. The virtual network is created by adding nodes and edges in virtual directions based on observed nodes at intersections, thereby enhancing the traffic network. The V-DCRNN, which utilizes the diffusion convolution process in the virtual network, uses the augmented traffic network as input to predict traffic speed. Unobserved speed data is estimated based on the values of virtual nodes predicted by V-DCRNN. We evaluate the proposed V-DCRNN model through unobserved speed data estimation experiments in the urban traffic network, where we randomly mask individual nodes and entire intersections. The main contributions of this work are as follows: 1) the design of a virtual network to model additional traffic dynamics at intersections, facilitating the estimation of unobserved speed data;2) the development of the V-DCRNN model, which leverages a self-attention mechanism to capture spatiotemporal dependencies in urban traffic networks by incorporating the virtual network as input;and 3) an evaluation of the V-DCRNN's ability to estimate unobserved speed data in urban traffic networks with up to 20% unobserved nodes, demonstrating rob

关键词： Recurrent neural networks

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SAFE-IDS: A privacy-preserving framework for overcoming non-IID challenges in federated intrusion detection

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computers and Security 2025年 155卷

作者： ugli, Alimov Abdulboriy Abdulkhay Shin, Ji Sun Department of Computer and Information Security and Convergence Engineering for Intelligent Drone Seoul Korea Republic of Department of Computer and Information Security Sejong University Seoul Korea Republic of

Federated learning has advanced intrusion detection systems (IDS) by enabling collaborative model training without requiring direct data sharing. This approach allows multiple institutions to contribute to and benefit from a shared model, enhancing detection capabilities. Despite these advances, the security of model updates remains a significant concern, as malicious actors may reverse-engineer the underlying data from these updates. Additionally, existing federated learning techniques struggle with non-IID (non-Independent and Identically Distributed) data distributions and are vulnerable to inference attacks on model updates. For example, methods like SignSGD, while providing some privacy benefits through gradient sign manipulation, suffer from accuracy degradation, especially when dealing with non-IID data. Similarly, FedAvg, while effective in handling non-IID data, is prone to privacy breaches as it transmits full model updates, potentially revealing sensitive information. To address these challenges, we propose SAFE-IDS, a novel framework combining gradient sign-based aggregation with the zSignFedAvg optimizer. Unlike SignSGD, it incorporates a unified learning rate and weighted loss function to mitigate accuracy loss in non-IID settings. Additionally, while FedAvg shares full model updates, SAFE-IDS only shares gradient signs, enhancing privacy. The integration of zSignFedAvg balances privacy and convergence speed, accelerating convergence and improving robustness, particularly for class imbalance. Notably, SAFE-IDS is the first federated network intrusion detection system that effectively maintains privacy while adeptly managing non-IID data. Our empirical evaluation demonstrates that SAFE-IDS achieves an impressive accuracy of up to 99.74% across various IDS datasets and a varying number of clients, proving its effectiveness in both securing client data and maintaining high model performance. © 2025

关键词： Federated learning

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Trusted and Privacy-Preserving Billing System for Smart Grids

Trusted and Privacy-Preserving Billing System for Smart Grid...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Kayumov Abduaziz Shincheol Lee Ji Sun Shin Department of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul South Korea Department of Computer and Information Security Sejong University Seoul South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

With the advent of smart meters in recent years, privacy has become one of the biggest concerns in smart grids. While privacy is achieved by designing smart grid services while maintaining the anonymous participation of consumers, several technical issues must be solved by electricity companies, such as confirmation of payments or assurance of consumption data integrity and quality. In this paper, we propose a new system model for data aggregation in smart grid environments that balances the needs of consumers and electricity companies. To decentralize and achieve transparent data processing, we apply a permissioned blockchain network and smart contracts. To support consumer privacy, we propose a privacy-preserving smart contract that runs on a trusted execution environment. Payments are processed and confirmed at the end of a billing period by the smart contract to enforce data consistency and integrity. We implement our smart contract design for Hyperledger Fabric, a permissioned blockchain network, and analyze its privacy, data, and blockchain benefits.

关键词： Privacy Data privacy Electricity Data integrity Smart contracts Companies Smart meters Smart grids Blockchains Reliability

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