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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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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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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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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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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 Republic of Korea Department of Information Systems University of Maryland Baltimore County (UMBC) Baltimore USA Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul Republic of Korea

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.

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DLAN: A dual attention network for effective land cover classification in remote sensing

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Knowledge-Based Systems 2025年 319卷

作者： Fayaz, Muhammad Dang, L. Minh Moon, Hyeonjoon Department of Computer Science and Engineering Sejong University Seoul Korea Republic of Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul Korea Republic of

In the era of remote sensing (RS), the demand for accurate land cover classification (LCC) has intensified due to various environmental challenges such as deforestation and urbanization. Conventional approaches often rely on shallow features for classification, limiting their effectiveness in capturing spatial patterns and diverse land cover types. In response, this study introduces a novel LCC approach utilizing a convolutional neural network (CNN) equipped with a dual land cover attention segment. The proposed module integrates channel attention (CA) and spatial attention mechanisms (SA) to enhance the discriminative capabilities of deep models. Leveraging inter-channel and inter-spatial relationships, the dual attention module enables the identification of various land cover types, spatial patterns, and color variations. Through thorough experimentation, the InceptionV3 feature extractor was identified as the optimal backbone for the proposed network architecture. Furthermore, to address the challenge of diverse land cover types, highly curated datasets are utilized. Additionally, to optimize model efficiency and reduce size, an improved model compression approach is employed. The effectiveness of the proposed Dual Land Cover Attention Network (DLAN) was evaluated through extensive experimentation, demonstrating superior performance compared to conventional methods. The results indicate the potential of DLAN in advancing LCC tasks, facilitating detailed agricultural zoning, environmental monitoring, and urban planning at a regional scale. © 2025

关键词： Convolutional neural networks

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Ransomware Classification Framework Using the Behavioral Performance Visualization of Execution Objects

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computers, Materials & Continua 2022年第8期72卷 3401-3424页

作者： Jun-Seob Kim Ki-Woong Park Department of Computer and Information Security and Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Korea

A ransomware attack that interrupted the operation of Colonial Pipeline(a large *** pipeline company),showed that security threats by malware have become serious enough to affect industries and social infrastructure rather than individuals *** agents and characteristics of attacks should be identified,and appropriate strategies should be established accordingly in order to respond to such *** this purpose,the first task that must be performed is malware *** creators are well aware of this and apply various concealment and avoidance techniques,making it difficult to classify *** study focuses on new features and classification techniques to overcome these *** propose a behavioral performance visualization method using utilization patterns of system resources,such as the central processing unit,memory,and input/output,that are commonly used in performance analysis or tuning of *** extracted the usage patterns of the system resources for ransomware to performbehavioral performance *** results of the classification performance evaluation using the visualization results indicate an accuracy of at least 98.94%with a 3.69%loss ***,we designed and implemented a framework to perform the entire process—from data extraction to behavioral performance visualization and classification performance measurement—that is expected to contribute to related studies in the future.

关键词： Behavioral performance visualization ransomware malware classification

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AI-Driven Prioritization and Filtering of Windows Artifacts for Enhanced Digital Forensics

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computers, Materials & Continua 2024年第11期81卷 3371-3393页

作者： Juhwan Kim Baehoon Son Jihyeon Yu Joobeom Yun Department of Computer and Information Security and Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Republic of Korea

Digital forensics aims to uncover evidence of cybercrimes within compromised *** cybercrimes are often perpetrated through the deployment of malware,which inevitably leaves discernible traces within the compromised *** analysts are tasked with extracting and subsequently analyzing data,termed as artifacts,from these systems to gather ***,forensic analysts must sift through extensive datasets to isolate pertinent ***,manually identifying suspicious traces among numerous artifacts is time-consuming and *** studies addressed such inefficiencies by integrating artificial intelligence(AI)technologies into digital *** the efforts in previous studies,artifacts were analyzed without considering the nature of the data within them and failed to prove their efficiency through specific *** this study,we propose a system to prioritize suspicious artifacts from compromised systems infected with malware to facilitate efficient digital *** system introduces a double-checking method that recognizes the nature of data within target artifacts and employs algorithms ideal for anomaly *** key ideas of this method are:(1)prioritize suspicious artifacts and filter remaining artifacts using autoencoder and(2)further prioritize suspicious artifacts and filter remaining artifacts using logarithmic *** evaluation demonstrates that our system can identify malicious artifacts with high accuracy and that its double-checking method is more efficient than alternative *** system can significantly reduce the time required for forensic analysis and serve as a reference for future studies.

关键词： Digital forensics autoencoder logarithmic entropy prioritization anomaly detection windows artifacts artificial intelligence

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