Future Generation Computer Systems (IF 6.1, JCR Top 9%) 연구 논문 게재

Block-FDT: Blockchain-Enhanced Federated Learning Approach to Secure DT-Assisted IIoT Networks저자Sekione Reward Jeremiah, ByungHyun Jo, Kim-Kwang Raymond Choo, Jong Hyuk Park게시 날짜2026/2/1저널Future Generation Computer Systems페이지108410게시자North-Holland설명The Industrial Internet of Things (IIoT) has transformed modern industries by enhancing automation, efficiency, and connectivity. However, this advancement has introduced critical cybersecurity challenges that may not be addressed using conventional security measures. Intrusion detection systems (IDSs) leveraging machine learning (ML) are increasingly adopted to address IIoT security concerns. However, centralized ML models face significant privacy and security concerns. Federated Learning (FL) addresses these privacy concerns, yet FL is susceptible to Byzantine attacks that can poison global model updates. To address these challenges, this paper proposes BlockFDT, a blockchain-enhanced asynchronous FL framework with Cyber Digital Twin (CDT) for threat detection in IIoT networks. The system uses a long short-term memory (LSTM)-based CDT to predict gateway behavior across six temporal features (gradient norm, loss reduction, anomaly score, gradient variance, latency, staleness), enabling intelligent client selection through Adaptive Participation Control (APC). Blockchain integration provides tamper-proof audit trails of model aggregation, client selection, and Byzantine rejections using SHA-256 hashing with asynchronous writes. We evaluate Block-FDT on 20 % of the Edge-IIoTset dataset (88,768 samples, 6-category classification) across 20 distributed gateways, with 40 % Byzantine attacks. Experiments demonstrate that Block-FDT achieves 91.15 % detection accuracy with staleness-aware asynchronous aggregation. The blockchain introduces a minimal overhead (0.357 % latency), providing transparency without compromising system performance.

Information Fusion (IF 15.5, JCR Top 2%) 연구 논문 게재

Multi-view learning and model fusion framework for threat detection in multi-protocol IoMT networks저자Sekione Reward Jeremiah, Abir El Azzaoui, Stefanos Gritzalis, Jong Hyuk Park게시 날짜2026/1/1 저널Information Fusion (IF 15.5, JCR Top 2%)권125페이지103435게시자Elsevier설명The Internet of Medical Things (IoMT) holds significant transformative potential for modern healthcare systems. It enables real-time patient monitoring and data insights for making informed clinical decisions. However, despite these advantages, IoMT networks face critical security challenges due to device resource constraints and heterogeneity. Existing research on IoMT security has primarily focused on data security concerns, overlooking the complexity and vulnerabilities arising from the heterogeneity of devices and communication protocols. Due to the complexity of IoMT network traffic and the high volume of data, advanced methods are necessary to enhance the security and reliability of these networks. Machine Learning (ML)-based methods provide effective techniques for detecting, preventing, and mitigating cyber threats. However, conventional centralized ML approaches are susceptible to privacy risks and vulnerabilities to single points of failure (SPoFs). This study proposes a cyberthreat detection method that employs a multi-view-based model fusion approach within a Federated Learning (FL) framework to enhance detection capabilities across multi-protocol IoMT networks. Federated learning is adopted to preserve data privacy by avoiding data transfer to central servers and mitigating SPoFs. The proposed method is evaluated using the CICIoMT2024 dataset featuring 17 Wi-Fi devices and 14 simulated MQTT devices with 18 attack scenarios across five categories (DoS, DDoS, spoofing, Recon, and MQTT). Overall, the method achieves superior threat detection using TabNet as the base learner and MLP as the meta-learner, with accuracies of 99.7 % and 99.4 % in binary and multi-class classification, respectively.

서울과기대, 박종혁 교수 ‘2025 세계 상위 2% 연구자’ 선정

서울과학기술대학교(총장 김동환, 이하 서울과기대)는 2025년 스탠퍼드대학교와 글로벌 학술 출판사 엘스비어(Elsevier)가 공동 발표한 '2025 세계 상위 2% 연구자(Top 2% Scientists 2025)' 명단에 본교 소속 25명의 연구자가 선정됐다고 10월 28일 밝혔다.이번 평가는 세계 최대 학술 데이터베이스인 SCOPUS의 인용 데이터를 기반으로, 전 세계 연 구자의 학문적 영향력을 객관적으로 분석한 결과이다.올해 선정된 서울과기대 '세계 최상위 2% 연구자'는 공과대학 김태영, 임성진, 정은성, 정용환, 황병철, 박혜림, 박성군, 노영숙, 최병준, 안효진, 정보통신대학 박종혁, 최세완, 김경화, 도현락, 이영일, 에너지바이오대학의 노인섭, 김철, 권용재, 인문사회대학 김상묵, 기술경영융합대학 김 경옥, 창의융합대학 유승훈, 철도전문대학원 최의민 교수 등이다.기사 링크 : 서울과기대, ‘2025 세계 상위 2% 연구자’ 25명 선정 - 교수신문

EPJ Quantum Technology (IF 5.6, JCR Top 9%) 연구 논문 게재

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 Technology ( IF 5.6, JCR Rank 9% )권12호112DOIhttps://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.

Sensors (IF 3.5, JCR Top 24%) 연구 논문 게재

Machine Learning-Based Blockchain Technology for Secure V2X Communication: Open Challenges and Solutions저자Yonas Teweldemedhin Gebrezgiher, Sekione Reward Jeremiah, Xianjun Deng, Jong Hyuk Park게시 날짜2025/8/4저널Sensors (IF 3.5, JCR Top 24%)권25호15페이지4793게시자MDPI설명Vehicle-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