Research Publication(A systematic review of anomaly detection in IoT security: towards quantum machine learning approach)

A systematic review of anomaly detection in IoT security: towards quantum machine learning approach저자Andres J. Aparcana-Tasayco, Xianjun Deng, Jong Hyuk Park게시 날짜2025/09/29저널EPJ Quantum Technology권12호112DOIhttps://doi.org/10.1140/epjqt/s40507-025-00414-6게시자Springer nature 설명Integrating IoT into daily life generates massive data, enabling smart factories and driving advancements in related technologies like cloud/edge computing, ML, and AI. While ML has been used for data analysis and forecasting, challenges such as data complexity, security, and computing limitations persist, particularly in anomaly detection crucial for network security. Recent research indicates the potential of quantum computing and Quantum Machine Learning (QML) to outperform traditional methods in anomaly detection within IoT, an area lacking a comprehensive review. This paper presents a systematic review of Machine Learning-based anomaly detection techniques for IoT security. Despite previous reviews, this study includes the analysis of feature engineering and quantum machine learning techniques in literature. Our findings show that current models have high detection rates on known datasets, but face scalability, real-time processing, and generalization issues. Privacy and security concerns in federated learning (FL) and the effects of data drift also need to be addressed, along with the challenges of 5G and 6G-enabled IoT environments. Future directions include integrating Explainable AI into anomaly detection, exploring adaptive learning techniques, and combining blockchain with machine learning models. The study also highlights the potential of quantum computing to enhance threat detection through quantum machine learning models.

Research Publication(Machine Learning-Based Blockchain Technology for Secure V2X Communication: Open Challenges and Solutions)

Machine Learning-Based Blockchain Technology for Secure V2X Communication: Open Challenges and Solutions저자Yonas Teweldemedhin Gebrezgiher, Sekione Reward Jeremiah, Xianjun Deng, Jong Hyuk Park게시 날짜2025/8/4저널Sensors권25호15페이지4793게시자MDPI설명Vehicle-to-everything (V2X) communication is a fundamental technology in the development of intelligent transportation systems, encompassing vehicle-to-vehicle (V2V), infrastructure (V2I), and pedestrian (V2P) communications. This technology enables connected and autonomous vehicles (CAVs) to interact with their surroundings, significantly enhancing road safety, traffic efficiency, and driving comfort. However, as V2X communication becomes more widespread, it becomes a prime target for adversarial and persistent cyberattacks, posing significant threats to the security and privacy of CAVs. These challenges are compounded by the dynamic nature of vehicular networks and the stringent requirements for real-time data processing and decision-making. Much research is on using novel technologies such as machine learning, blockchain, and cryptography to secure V2X communications. Our survey highlights the security challenges faced by V2X communications and assesses current ML and blockchain-based solutions, revealing significant gaps and opportunities for improvement. Specifically, our survey focuses on studies integrating ML, blockchain, and multi-access edge computing (MEC) for low latency, robust, and dynamic security in V2X networks. Based on our findings, we outline a conceptual framework that synergizes ML, blockchain, and MEC to address some of the identified security challenges. This integrated framework demonstrates the potential for real-time anomaly detection, decentralized data sharing, and enhanced system scalability. The survey concludes by identifying future research directions and outlining the …학술 문서Machine Learning-Based Blockchain Technology for Secure V2X Communication: Open Challenges and SolutionsYT Gebrezgiher, SR Jeremiah, X Deng, JH Park - Sensors, 2025관련 학술자료 전체 3개의 버전

Research Publication(Evaluating climate adaptation strategies for coastal resilience using multi-criteria decision-making framework)

Evaluating climate adaptation strategies for coastal resilience using multi-criteria decision-making framework저자Meng Teng, Fuli Zhang, Zhi Gong, Jong Hyuk Park게시 날짜2025/8/1저널Marine Pollution Bulletin권217페이지118060게시자Pergamon설명Coastal regions face significant challenges due to climate change, requiring effective adaptation strategies to protect ecosystems, infrastructure, and communities. Selecting appropriate strategies remains complex due to the need to balance ecological, economic, and social factors. This study introduces a novel AHP-based Multi-Criteria Decision-Making (MCDM) framework that systematically integrates empirical evidence and expert judgment to evaluate and prioritize coastal adaptation strategies. The framework incorporates inputs from 20 experts and evaluates alternatives across four key criteria: Environmental Impact (weight = 0.533), Cost-Effectiveness (0.255), Social Acceptance (0.149), and Long-Term Sustainability (0.063). Among the evaluated strategies, mangrove restoration received the highest priority score of 0.70, highlighting its superior performance across environmental, social, and sustainability …전체 인용횟수2회 인용2025학술 문서Evaluating climate adaptation strategies for coastal resilience using multi-criteria decision-making frameworkM Teng, F Zhang, Z Gong, JH Park - Marine Pollution Bulletin, 20252회 인용 관련 학술자료 전체 4개의 버전

Research Publication(Edge-Fog Enhanced Post-Quantum Network Security: Applications, Challenges and Solutions)

Edge-Fog Enhanced Post-Quantum Network Security: Applications, Challenges and Solutions.저자Seo Yeon Moon, Byung Hyun Jo, Abir El Azzaoui, Sushil Kumar Singh, Jong Hyuk Park게시 날짜2025/7/1출처Computers, Materials & Continua권84호1설명With the rapid advancement of ICT and IoT technologies, the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency. However, these technologies face critical security challenges, exacerbated by the emergence of quantum computing, which threatens traditional encryption methods. The rise in cyber-attacks targeting IoT and Edge/Fog networks underscores the need for robust, quantum-resistant security solutions. To address these challenges, researchers are focusing on Quantum Key Distribution and Post-Quantum Cryptography, which utilize quantum-resistant algorithms and the principles of quantum mechanics to ensure data confidentiality and integrity. This paper reviews the current security practices in IoT and Edge/Fog environments, explores the latest advancements in QKD and PQC technologies, and discusses …학술 문서Edge-Fog Enhanced Post-Quantum Network Security: Applications, Challenges and Solutions.SY Moon, BH Jo, AE Azzaoui, SK Singh, JH Park - Computers, Materials & Continua, 2025관련 학술자료 전체 2개의 버전

Research Publication(A comprehensive survey on large language models for multimedia data security: challenges and solutions)

A comprehensive survey on large language models for multimedia data security: challenges and solutions저자Ankit Kumar, Mikail Mohammed Salim, David Camacho, Jong Hyuk Park게시 날짜2025/7/1출처Computer Networks권267페이지111379게시자Elsevier설명The rapid expansion of IoT applications utilizes multimedia data integrated with Large Language Models (LLMs) for interpreting digital information by leveraging the capabilities of artificial intelligence (AI) driven neural network systems. These models are extensively used as generative AI tools for data augmentation but data security and privacy remain a fundamental concern associated with LLM model in the digital domain. Traditional security approach shows potential challenges in addressing emerging threats such as adversarial attacks, data poisoning, or privacy breaches, especially in dynamic and resource-constrained IoT environments. Such malicious attacks target the LLM model during the learning and evaluation phase to exploit the vulnerabilities for unauthorized access. The proposed study conducts a comprehensive survey of the transformative potential of LLM models for securing multimedia data …전체 인용횟수1회 인용2025학술 문서A comprehensive survey on large language models for multimedia data security: challenges and solutionsA Kumar, MM Salim, D Camacho, JH Park - Computer Networks, 20251회 인용 관련 학술자료