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Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model

computer Modeling in engineering & Sciences 2025年第4期143卷 639-671页

作者： Noveela Iftikhar Mujeeb Ur Rehman Mumtaz Ali Shah Mohammed J.F.Alenazi Jehad Ali Knowledge Unit of Systems and Technology University of Management and TechnologySialkot51310Pakistan Department of Computer Science University ofWahWah Cantt47010Pakistan Department of Computer Engineering College of Computer and Information Sciences(CCIS)King Saud UniversityRiyadh11451Saudi Arabia Department of AI Convergence Network Ajou UniversitySuwon16499Republic of Korea

Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few *** devices are now easy targets for hackers because of their built-in security *** a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion *** proposed model is evaluated on the NSL-KDD *** hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world ***,this paper proposes a highly efficient deployment strategy for resource-constrained network *** results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)*** particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.

关键词： Intrusion detection self-organizing map Internet of Things dimensionality reduction

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Evaluating ai-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review

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Artificial Intelligence Review 2025年第8期58卷 1-78页

作者： Al-antari, Mugahed A. Salem, Saied Raza, Mukhlis Elbadawy, Ahmed S. Bütün, Ertan Aydin, Ahmet Arif Aydoğan, Murat Ertuğrul, Bilal Talo, Muhammed Gu, Yeong Hyeon Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of Department of Artificial Intelligence College of AI Convergence Daeyang AI Center Sejong University Seoul05006 Korea Republic of Department of Computer Engineering Faculty of Engineering Fırat University Elazığ Turkey Department of Computer Engineering Faculty of Engineering Inonu University Malatya Turkey Department of Software Engineering Faculty of Technology Fırat University Elazığ Turkey Department of Neurosurgery Faculty of Medicine Fırat University Elazığ Turkey Department of Computer Science & Engineering University of North Texas DentonTX76205 United States

Lumbar spinal stenosis (LSS) involves the narrowing of the spinal canal, leading to compression of the spinal cord and nerves in the lower back. Common causes include injuries, degenerative age-related changes, congenital conditions, and tumors, all of which contribute to back pain. Early diagnosis is critical for symptom management, preventing progression, and preserving quality of life. This study systematically reviews ai-based approaches for predicting LSS using MRI axial and sagittal imaging. The review focuses on various ai tasks: detection, segmentation, classification, hybrid approaches, spinal index measurements (SIM), and explainable ai frameworks. The aim is to highlight current knowledge, identify limitations in existing models, and propose future research directions. Following PRISMA guidelines and the PICO method (Population, Intervention, Comparison, Outcome), the review collects data from databases like PubMed, Web of Science, ScienceDirect, and IEEE Xplore (2005–2024). The Rayyan ai tool is used for duplicate removal and screening. The screening process includes an initial review of titles and abstracts, followed by full-text appraisal. The Meta Quality Appraisal Tool (MetaQAT) assesses the quality of selected articles. Of 1323 records, 97 duplicates were removed. After screening, 895 records were excluded, leaving 331 for full-text review. Among these, 184 articles were excluded for lacking ai relevance. Ultimately, 95 key articles (91 technical papers and 4 reviews) were identified for their contributions to ai-based LSS prediction. This review provides a comprehensive analysis of ai techniques in LSS prediction, guiding future research and advancing understanding in areas like explainable ai and large language models (LLMs). © The Author(s) 2025.

关键词： Explainable artificial intelligence (Xai) Harmonization Large language models (LLMs) LSS prediction Lumbar spinal stenosis (LSS) Spinal LSS indices measurements

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Integration of Federated Learning and Graph Convolutional networks for Movie Recommendation Systems

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computers, Materials & Continua 2025年第5期83卷 2041-2057页

作者： Sony Peng Sophort Siet Ilkhomjon Sadriddinov Dae-Young Kim Kyuwon Park Doo-Soon Park Department of Software Convergence Soonchunhyang UniversityAsan31538Republic of Korea Department of Computer Software and Engineering Soonchunhyang UniversityAsan31538Republic of Korea AI⋅SW Education Institute Soonchunhyang UniversityAsan31538Republic of Korea

Recommendation systems(RSs)are crucial in personalizing user experiences in digital environments by suggesting relevant content or *** filtering(CF)is a widely used personalization technique that leverages user-item interactions to generate ***,it struggles with challenges like the cold-start problem,scalability issues,and data *** address these limitations,we develop a Graph Convolutional networks(GCNs)model that captures the complex network of interactions between users and items,identifying subtle patterns that traditional methods may *** integrate this GCNs model into a federated learning(FL)framework,enabling themodel to learn fromdecentralized *** not only significantly enhances user privacy—a significant improvement over conventionalmodels but also reassures users about the safety of their ***,by securely incorporating demographic information,our approach further personalizes recommendations and mitigates the coldstart issue without compromising user *** validate our RSs model using the openMovieLens dataset and evaluate its performance across six key metrics:Precision,Recall,Area Under the Receiver Operating Characteristic Curve(ROC-AUC),F1 Score,Normalized Discounted Cumulative Gain(NDCG),and Mean Reciprocal Rank(MRR).The experimental results demonstrate significant enhancements in recommendation quality,underscoring that combining GCNs with CF in a federated setting provides a transformative solution for advanced recommendation systems.

关键词： Recommendation systems collaborative filtering graph convolutional networks federated learning framework

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LLM-based Methodology for Expanding 3D Metadata in Point Cloud 27

LLM-based Methodology for Expanding 3D Metadata in Point Clo...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Kim, Geon-Woo Kim, Joo-Chang Department of AI Computer Science and Engineering Kyonggi University Suwon16227 Korea Republic of Contents Convergence Software Research Institute Kyonggi University Suwon16227 Korea Republic of

ISBN: (纸本)9791188428137

This paper proposes a method for expanding the metadata of three-dimensional point cloud data using Large Language Models (LLMs). Currently, point cloud data plays a crucial role in various fields such as autonomous driving and medical image reconstruction, necessitating the expansion of metadata for efficient processing. Traditionally, metadata construction has relied on manual input, which is prone to errors. In this study, we propose a method that utilizes LLMs, particularly the Llama 3.1 model, to extract the center points of each class in the point cloud data and expand the metadata by adding these center points to the annotation files. By using center points, computational costs are reduced, and the performance of segmentation and detection models based on this data is improved. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Data assimilation

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Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Resource and Cyberattacks Prediction 19

Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Re...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Lee, Hyeonmin Kim, Dongchan Beom, Donggyu Byun, Gyurin Vo, Van-Vi Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of

ISBN: (纸本)9798331507817

This study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. The research integrates Graph Neural networks (GNNs) and Convolutional Long and Short-Term Memory (ConvLSTM) networks to predict future network traffic and detect cyberattacks. The model was trained and evaluated on the CICIDS 2018 dataset which includes various types of normal and malicious traffic. Grid-based representations were created from the dataset. The attack type of each timestamp were visualized using distinct colors to the grid images. These images are sued as input of ConvLSTM model. Protocol and destination port are modeled as nodes in a graph, with volume of data flow represented as weighted edges. This graph-based representation is designed to capture unique traffic patterns of specific services and enhance the analysis of spatial and temporal features. Experimental results show a 13% increase in macro F1 score and a 4% increase in weighted F1 score, highlighting the effectiveness of the proposed method. This research highlights its potential to enable proactive responses through the prediction of potential cyberattack occurrences. © 2025 IEEE.

关键词： Convolutional Long Short-Term Memory Cyberattack Detection Deep Learning Graph Neural networks network Traffic Prediction

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Ultra-Wideband Mode-Compressed Dipole Antenna with Folded U-Shaped Reflector

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IEEE Transactions on Antennas and Propagation 2025年

作者： Wang, Heesu Park, Yong Bae Park, Ikmo Ajou University Department of Electrical and Computer Engineering 206 Worldcup-ro Youngtong-gu Suwon16499 Korea Republic of Ajou University Department of AI Convergence Network Department of Electrical and Computer Engineering 206 Worldcup-ro Youngtong-gu Suwon16499 Korea Republic of

This paper proposes an ultra-low-profile ultrawideband antenna comprising a mode-compressed dipole and U-shaped reflector. The radiating element of the antenna is a wide-width mode-compressed dipole structure loaded with short stubs and slots, and it is positioned on top of the dielectric substrate. The wide-width dipole compresses the third-order mode of the dipole, while the added short stubs generate a resonant mode at lower frequencies and compress the fifth-order mode of the dipole. The slots added to each wide-width dipole arm generate a resonant mode for the antenna and improve its radiation pattern characteristics. For the impedance matching of the antenna, indirect feeding was applied using a driving element located at the bottom of the dielectric substrate. The proposed antenna has a –10 dB impedance bandwidth range of 0.99–5.15 GHz (135.5%), and its gain varies from 3.5 to 6.4 dBi within the operating bandwidth. The electrical size of the proposed antenna is only 0.257 λL × 0.132 λL × 0.0528 λL © 1963-2012 IEEE.

关键词： Reflection

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Joint Encryption and Optimization for 6G MEC-Enabled IoT networks

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IEEE Access 2025年 13卷 79757-79770页

作者： Ahmed, Manzoor Khan, Wali Ullah Alrayes, Fatma S. Said, Yahia Al-Sharafi, Ali M. Kim, Mi-Hye Dashdondov, Khongorzul Ullah, Inam Hubei Engineering University School of Computer and Information Science Institute for AI Industrial Technology Research Xiaogan City 432000 China University of Luxembourg Interdisciplinary Centre for Security Reliability and Trust (SnT) Luxembourg City 1855 Luxembourg College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Department of Information Systems P.O. Box 84428 Riyadh 11671 Saudi Arabia Northern Border University Department of Electrical Engineering College of Engineering Arar 91431 Saudi Arabia University of Bisha Department of Computer Science and Artificial Intelligence College of Computing and Information Technology P.O Box: 551 Bisha Saudi Arabia Chungbuk National University Department of Computer Engineering Chungbuk Cheongju 28644 South Korea Gachon University College of IT Convergence Department of Computer Engineering Seongnam 13120 South Korea Gachon University Department of Computer Engineering Seongnam 13120 South Korea

With the advent of advancements in future sixth-generation (6G) communication systems, Internet of Things (IoT) devices, characterized by their limited computational and communication capacities, have become integral in our lives. These devices are deployed extensively to gather vast amounts of data in real-time applications. However, their restricted battery life and computational resources present significant challenges in meeting the requirements of advanced communication systems. Mobile Edge Computing (MEC) has emerged as a promising solution to these challenges within the IoT realm in recent years. Despite its potential, securing MEC infrastructure in the context of IoT remains an open task. This study explores the operational dynamics of a secured IoT-enabled MEC infrastructure, focusing on providing real-time, on-demand, secure computational resources to low-powered IoT devices. It outlines a joint optimization problem to maximize computational throughput, minimize device energy consumption, reduce computational latency, and mitigate security overhead. An optimization algorithm is introduced to address these challenges by jointly allocating resources, thereby optimizing throughput, conserving energy, and meeting latency benchmarks through dynamic system adaptation. The effectiveness of the proposed model and algorithm is demonstrated through comparisons with relevant benchmark schemes, highlighting its efficiency in various scenarios. This work showcases the potential of advancements in encryption to deliver scalable security solutions with reduced resource consumption as the number of devices increases. © 2013 IEEE.

关键词： 6G Computational latency Computational throughput Energy consumption Internet of Things (IoT) Mobile Edge Computing (MEC) Secure encryption

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WQuatNet: Wide range quaternion-based head pose estimation

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Journal of King Saud University - computer and Information Sciences 2025年第3期37卷 1-14页

作者： Algabri, Redhwan Shin, Hyunsoo Abdu, Ahmed Bae, Ji-Hun Lee, Sungon Dept. of Computer Science & Engineering College of AI Convergence Sejong University Seoul 05006 South Korea Research Institute of Engineering and Technology Hanyang University Ansan 15588 South Korea School of Software Northwestern Polytechnical University Xi’an 710072 China Department of Robotics Hanyang University Ansan 15588 South Korea

Head pose estimation (HPE) is a critical task for numerous applications ranging from human-computer interaction, healthcare, and robotics, to surveillance. Most existing methods employ Euler angles as a representation, which often face challenges such as a gimbal lock, especially in full-range rotation scenarios or rotation matrices that require nine parameters. This study introduces WQuatNet, a novel deep learning-based model that leverages the quaternion representation, which uses only four parameters, to avoid this challenge. WQuatNet was designed based on a landmark-free HPE method to predict head poses across the full-range angles of 360∘ from images. Landmark-free methods bypass the need for explicit detection of facial landmarks;instead, they leverage the entire image to estimate the head orientation. The model incorporates a RepVGG-D2se backbone for robust feature extraction and introduces two loss functions tailored for quaternion predictions. Our experimental results on multiple HPE datasets covering both narrow- and full-range angles demonstrate that WQuatNet outperforms the state-of-the-art (SOTA) approaches in terms of accuracy. The performance of the proposed HPE was evaluated using the CMU, AGORA, BIWI, AFLW2000, and 300W-LP datasets. We also perform ablation studies and error analyses to validate the significance of each component of the model. © The Author(s) 2025.

关键词： Deep neural network Full range of rotation Head pose estimation Quaternion

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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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A network Path Method for Connecting Cross-Seam Using Ground Relay in LEO Satellite network

A Network Path Method for Connecting Cross-Seam Using Ground...

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Consumer Communications and networking Conference, CCNC IEEE

作者： Heon-Woo Chu Tae-Han Moon Won-Jae Lee Jae-Hyun Kim Department of Artificial Intelligence Convergence Network Ajou University Suwon South Korea Department of Electrical and Computer Engineering Ajou University Suwon South Korea

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

This paper proposes a method to overcome the cross-seam in a low Earth orbit (LEO) satellite network. In cross-seam, satellite links cannot be established due to differences in satellite movement directions. The proposed method involves utilizing ground relay to resolve this problem. The Walker-Star constellation has polar regions and cross-seams, where inter-satellite link connection is impossible due to relative velocity. In this paper, we compare the performance results in a satellite network when ground relay is utilized in a cross-seam. We analyze the propagation delay and the number of satellite hops. It analyzes satellite communication performance with and without a ground relay to major cities from Seoul. Also, the maximum performance improvement provided by the ground relay is analyzed. As a result of the analysis, the ground relay reduces propagation delay by up to 76% and reduces satellite hop count by 89%.

关键词： Satellites Urban areas Low earth orbit satellites Delays Relays Satellite communications Propagation delay

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