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A Study on Improving the Performance of Dynamic Model Based Redundant Navigation System for Sensor Fault Scenarios

Journal of Institute of Control, Robotics and Systems 2025年第4期31卷 351-359页

作者： Ko, Bo Sung Choi, Hyeong Jun Jeon, Myung Jun Cho, Hyeon Jin Song, Jin Woo Department of Artificial Intelligence and Robotics and Convergence Engineering for Intelligent Drone AIRI Sejong University Korea Republic of Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone AIRI Sejong University Korea Republic of Agency for Defense Development Maritime Technology Research Institute Korea Republic of

This study proposes a redundant navigation system for UUV (Unmanned Underwater Vehicle) utilizing a 6 DOF (Degree of Freedom) VDM (Vehicle Dynamic Model). The system is designed to enable safe UUV asset return when all navigation sensors become inoperable. Since it is impossible to perfectly determine VDM coefficients through water tank experiments, a method to improve VDM output accuracy in real time during UUV mission is necessary. Here in this study, we introduce real-time adaptive 6 DOF VDM output error compensation algorithm to enhance the performance of redundant navigation systems. This research aims to establish the foundation for backup navigation solutions in situations when critical UUV sensor failures occur. © ICROS 2025.

关键词： Water tanks

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Privacy Enhanced P2P Power Trading Using DID-Based Identity Verification in a Microgrid 25th

Privacy Enhanced P2P Power Trading Using DID-Based Identity ...

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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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Plotting OSS-Based Supply Chain Attack Strategies and the Defense Failure 25th

Plotting OSS-Based Supply Chain Attack Strategies and the ...

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25th International Conference on Information Security Applications, WISA 2024

作者： Sarang, Arpita Dinesh Choi, Sang-Hoon Park, Ki-Woong SysCore Lab Department of Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of SysCore Lab Sejong University Seoul05006 Korea Republic of Department of Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of

ISBN: (纸本)9789819616237

The supply chain attack, which targets open-source software, is currently the most discussed cyberattack. This is due to the recent open-source XZ utils and PyPI projects based attacks where the attackers employed similar strategies. A backdoor was injected in the libraries developed by these projects for future exploitation when software users installed them on systems. These attacks were executed by the trusted developers of these projects and influenced by unknown promoters. Therefore, there is a need to scrutinize Open Source Software (OSS) Security to protect the legacy of Open Software development for the future. This paper provides an overview of these two attack strategies through their case studies, which aid in the creation of a generic attack framework for supply chain attacks on OSS. We present the existing detection methods for OSS security in this work and their lacunas taking into account at various stages of a supply chain attack. This makes it necessary to build platforms that allow the current OSS security detection modules to be utilized in sequence. Therefore, we introduce an OSS security Detection platform that does not completely address the limitations of the detection technique, but when combined, they can maximize the effectiveness of an OSS security quality check. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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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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Pest and Scratch Detection on Apple Surface Using TricubeNet

Pest and Scratch Detection on Apple Surface Using TricubeNet

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Ngoc Han, Nguyen Bui Lee, Ju-Hwan Kim, Jin-Young Kim, Hyoung-Gook Kwangwoon University Department of Electronic Convergence Engineering Seoul Korea Republic of Chonnam National University Department of Intelligent Electronics and Computer Engineering Gwangju Korea Republic of

ISBN: (纸本)9798331510756

From orchard to consumer, apples experience multiple static and dynamic stresses, leading to defects such as pest damage and surface scratches. Detecting and sorting defective apples post-harvest is crucial for enhancing market value and minimizing contamination risks. While machine vision-based sorting and grading methods have advanced, they often prioritize easily detectable defects, neglecting undetectable ones. In this work, we apply TricubeNet, a one-stage, anchor-free detector, to detect pests and scratches on the surface of apples. This empirical study addresses a gap in the literature, as TricubeN et has not been previously evaluated on fruit datasets. By carefully tuning the size hyperparameter of the Tricube kernel, we achieve the highest F1 scores of 65.34 %, demonstrating the model's potential for subtle defect detection. © 2025 IEEE.

关键词： Fruits

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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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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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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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Improving Localization Accuracy of Offline Navigation Algorithms for intelligent Pipeline Inspection Gauges and In-Line Inspection Robotic Systems

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Journal of Field Robotics 2025年第0期

作者： Yoo, Kwanghyun Kim, Dae-Kwang Kim, Jae-Jun Yang, Seung-Ung Yoo, Hui-Ryoung Song, HongSeok Jeong, Han-You Nguyen, Hoa-Hung Song, Jin Woo Kim, Dong-Kyu Incheon Korea Republic of Department of Electrical Engineering Pusan National University Busan Korea Republic of Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul Korea Republic of

Integrity management of pipeline networks is crucial for preemptive maintenance and preventing accidents. In this study, various methods for improving the localization accuracy of the offline navigation algorithm for an intelligent pipeline inspection gauge (PIG) and in-line inspection (ILI) robotic system are proposed. The digital mapping algorithm utilizes the extended Kalman filter (EKF) with a Rauch–Tung–Striebel (RTS) smoother. Two ILI tools are introduced—magnetic flux leakage (MFL) PIG, which is a typical intelligent PIG for detecting corrosion defects in pipelines, and a new low-friction geometry robot (LFGR) for inspecting the mechanical defects in low-pressure, low-flow pipelines. The MFL PIG has only three odometers to measure the speed of the PIG along the moving direction. Hence, a compensation method for the measured speed was developed and utilized. In addition, an optimization procedure for the parameters of sensor uncertainty modeling was proposed and validated. These methods increased the localization accuracy of the digital mapping algorithm of the MFL PIG. Specifically, the root mean squared value of the two-dimensional distance error decreased by 47.73%. The proposed methods were applied to the LFGR equipped with four odometers and a high-accuracy inertial measurement unit. Moreover, additional sensors and a new algorithm for attitude angle correction of the robot were utilized. The proposed methods were successfully validated using field ILI results. The methods enhance the effectiveness of the integrity management of pipeline systems, thus contributing toward their safe and reliable operation. © 2025 Wiley Periodicals LLC.

关键词： Extended Kalman filters

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