Internet Technology Letters最新文献

{"title":"Host-Level Botnet Detection via Internet DNS Traffic Analysis Using Neural Networks","authors":"H. G. Mohan,&nbsp;Jalesh Kumar,&nbsp;M. Nandish","doi":"10.1002/itl2.70101","DOIUrl":"https://doi.org/10.1002/itl2.70101","url":null,"abstract":"<div>\u0000 \u0000 <p>Botnets remain one of the most significant threats in Internet security, performing large-scale attacks such as distributed denial of service (DDoS), data exfiltration, and financial fraud. Detecting botnet activity at the host level is crucial for early mitigation, particularly by analyzing anomalies in domain name system (DNS) query sequences. This study proposes a deep learning-based DNS sequence analysis that leverages Bidirectional Gated Recurrent Units (BiGRU) to identify deviations in DNS query behavior indicative of botnet activity. The model learns temporal patterns in DNS sequences, distinguishing legitimate traffic from botnet-generated queries by capturing contextual dependencies over time. The proposed approach is trained and evaluated on a UNSW-NB15 dataset. The performance assessment of the proposed model demonstrates its effectiveness in detecting botnets with an accuracy of 99.22%. The comparative analysis with the existing approaches highlights the improvements in detection accuracy with a low misclassification rate.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When Construction Supply Chains Meet 6G: A Deep Neural Network-Based Real-Time Data Transmission Approach","authors":"Zhaoyi Tong,&nbsp;Rong Huang,&nbsp;Haoning Mai","doi":"10.1002/itl2.70071","DOIUrl":"https://doi.org/10.1002/itl2.70071","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional communication infrastructures often struggle to support the demands of real-time data exchange required for modern construction practices like building information modeling, drone monitoring, sensor networks, and automated equipment, leading to delays, cost overruns, and suboptimal resource allocation. This letter presents a deep neural network-based real-time dynamic selection (DRDS) algorithm for modern construction supply chains that leverages 6G network capabilities for ultrafast data transmission. The approach uses historical project data to train a deep neural network model that dynamically selects optimal priority rules for resource allocation and scheduling based on real-time project status. Experimental results demonstrate that DRDS outperforms existing methods, achieving 95.2% relative optimal solutions for large-scale projects while maintaining solution times under 1.12 s. When deployed on 6G networks, the algorithm achieves 0.23 ms transmission latency, 39.2% bandwidth utilization, and can support 12 580 sensor nodes per km².</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Edge Computing Integration in 5G Core on Real-Time Data Processing for Smart Applications","authors":"Ying Wang,&nbsp;Zhiyuan Wang","doi":"10.1002/itl2.70074","DOIUrl":"https://doi.org/10.1002/itl2.70074","url":null,"abstract":"<div>\u0000 \u0000 <p>Intelligent decision-making for clever apps is made possible by the 5G core's integration of edge computing, which improves real-time data processing capabilities. It greatly lowers latency and boosts efficiency in vital applications like industrial automation, smart cities, and driverless cars by moving processing closer to data sources. However, there are issues with current approaches such as excessive network congestion, longer processing times, and wasteful resource use. These restrictions impair real-time responsiveness and lower smart apps' overall performance. We suggest the Edge-Integrated 5G Smart Processing Framework (E5G-SPF) as a solution to these issues. To maximize real-time data processing, this system uses cutting-edge methods including network slicing, dynamic resource allocation, and edge-based AI inference. The 5G core's multi-access edge computing (MEC) nodes are used to distribute workloads effectively and reduce latency. By enabling ultra-fast data analytics, lowering communication overhead, and enhancing service reliability, the E5G-SPF architecture is intended to improve a variety of smart applications. E5G-SPF employs Deep Learning (e.g., CNNs, LSTMs) for real-time data inference at the edge. Reinforcement Learning (RL) is used for dynamic task scheduling and resource optimization. Federated Learning ensures privacy-preserving model updates across distributed edge nodes. Graph Neural Networks (GNNs) support topology-aware task allocation. Additionally, metaheuristic algorithms combined with ML are used for efficient, adaptive scheduling decisions. The proposed E5G-SPF framework achieves a latency reduction of up to 85%, lowering it from 60 to 9 ms, and improves processing speed by 72% compared to conventional cloud models. These enhancements enable real-time responsiveness for critical smart applications. Experimental results demonstrate that the E5G-SPF framework significantly improves processing speed, reduces end-to-end latency, and enhances resource efficiency compared to traditional cloud-based approaches. These findings confirm its potential in transforming next-generation smart applications by ensuring real-time data processing within the 5G core.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Intelligent Hybrid Machine Learning With Meta-Heuristic Optimization Algorithms for Enhancing Real-Time Video Stabilization","authors":"S. Afsal,&nbsp;J. Arul Linsely","doi":"10.1002/itl2.70089","DOIUrl":"https://doi.org/10.1002/itl2.70089","url":null,"abstract":"<div>\u0000 \u0000 <p>In the modern era, video stabilization is one of the essential advancement features of digital video processing equipped with 5G technology. Also, this technology leverages the intelligent software innovations to deliver high quality and smooth video recording experiences. Despite advancement in machine learning (ML) algorithms for video stabilization, there are numerous challenges, especially when applying 5G technologies like stable and unstable videos for training performance. Consequently, video stabilization includes complex analyses such as frame interpolation and motion assessment. Moreover, the advanced stabilization modes are developed to analyze the motion data. Nevertheless, they decrease or fail to calculate the features and provide poor results. To overcome these issues, an adaptive video stabilization methodology is proposed. In the proposed method, a novel Convolution Neural with StabNet based Hawks Optimization (CNSbHO) algorithm is introduced. In this research, hand-held video clips generally suffer from unwanted video jitters due to unbalanced camera motion. Therefore, 5G ultra-low latency with respect to drone footage video feeds is taken as the stabilization process. Then, a pre-processing Gaussian filter was enabled to enhance consistency and quality. Hereafter, a Convolution Neural Network (CNN) was used to extract the features, and motion estimation is also done in this section with feature tracking point of CNN. Furthermore, end-to-end stabilization strategy as StabNet model can provide stabilized frame outputs. Then, the Harris Hawks Optimization (HHO) algorithm was used to enhance the accuracy of the entire performance. The developed CNSbHO strategy was implemented in Python and validated using the 5G traffic datasets. In order to validate the effectiveness of the developed strategy, we selected the traditional algorithms for the comparison in terms of learned perceptual image patch similarity (LPIPS), structural similarity index (SSIM), accuracy, and peak signal-to-noise ratio (PSNR). The comparative assessment confirms that the proposed method outperforms conventional stabilization techniques, making it a reliable solution for real-time video processing tasks.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security and Privacy Challenges in 5G Core AI-Powered Threat Detection and Mitigation Strategies","authors":"Bin Ren","doi":"10.1002/itl2.70070","DOIUrl":"https://doi.org/10.1002/itl2.70070","url":null,"abstract":"<div>\u0000 \u0000 <p>Integrating artificial intelligence (AI) in the 5G core network enhances danger detection and mitigation capabilities, which ensure strong security and privacy security. However, existing techniques face challenges which include high fake positive prices, actual-time hazard version issues, and vulnerability to adversarial assaults. The adaptive AI-driven security framework (AASF) is our answer to these problems. It uses deep learning, federated learning, and anomaly detection to improve the accuracy of threat identification and lessen the effects of new cyber threats. AASF employs actual-time danger intelligence sharing and decentralized data processing to reinforce privacy preservation while enhancing detection efficiency. The proposed method ensures proactive security features, reduces latency in hazard mitigation, and minimizes records publicity risks. Experimental evaluation suggests that AASF performs better by traditional methods by acquiring high recognition accuracy, reduces false positives, and the response improves time, making it a viable solution to secure the 5G core network.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Cost of Insularity on Network Latency: A Case Study in the Mediterranean","authors":"Alessio Vecchio,&nbsp;Massimo Candela,&nbsp;Louis Pétiniaud,&nbsp;Valerio Luconi","doi":"10.1002/itl2.70084","DOIUrl":"https://doi.org/10.1002/itl2.70084","url":null,"abstract":"<div>\u0000 \u0000 <p>Islands face unique challenges in Internet connectivity due to their geographic isolation, geopolitical factors, and economic limitations. In this study, we present the first analysis of the Internet performance on the major Mediterranean islands, focusing on latency and traversed paths. By systematically comparing measurements from insular and mainland regions, we show that insular Internet connections exhibit higher latency and follow routing paths that are longer, more circuitous, and traverse sets of autonomous systems with different characteristics. We believe our results highlight performance gaps and can contribute to a better understanding of the Internet characteristics and limitations of insular regions.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy Preserving Efficient Worker Selection in the Cloud-Based Crowdsourcing Platform","authors":"Himanshu Suyal,&nbsp;Avtar Singh,&nbsp;Gulshan Shrivastava","doi":"10.1002/itl2.70092","DOIUrl":"https://doi.org/10.1002/itl2.70092","url":null,"abstract":"<div>\u0000 \u0000 <p>Crowdsourcing has become the most widely used tool to solve complex problems through the collective intelligence of distributed crowd workers, but ensuring both worker and task privacy remains a significant challenge. This research proposed a novel privacy-preserving framework, a lightweight dynamic worker selection method based on attribute-based selection that ensures the privacy of workers and tasks through pseudonymity and encryption. A two-phase encryption ensures the confidentiality and anonymity of workers and tasks against the crowd server. In addition, it incorporates efficient worker revocation to remove unreliable or spam workers without disturbing the overall schema. The detailed security analysis shows that our approach is to secure the task and worker identity with minimum complexity. An experimental study compares the proposed approach with the state-of-the-art approach, showing that it has a low computational cost and is feasible under resource-constrained environments.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Faster-ActionNet: Deep Partial Convolutional Neural Networks for Volleyball Action Detection on Edge Devices","authors":"Shaohua Wang","doi":"10.1002/itl2.70091","DOIUrl":"https://doi.org/10.1002/itl2.70091","url":null,"abstract":"<div>\u0000 \u0000 <p>To address the challenges of low accuracy in volleyball individual action recognition caused by complex scenarios in volleyball sports, Faster-ActionNet was proposed based on the backbone of YOLOv11. In this network, partial convolutions are adopted in both the backbone and neck modules to amplify critical feature representations while minimizing redundant computational and memory overhead. In the backbone network, the Feature Refinement and Fusion Network (FRFN) attention mechanism is integrated, which employs optimized and streamlined operations to reduce feature redundancy across channels. This enhancement significantly boosts the reconstruction quality of latent sharp images and alleviates the risk of critical feature degradation. Experiments evaluating the individual action recognition model on volleyball-specific tasks have revealed superior performance, with the model of mAP attaining 88.2% accuracy and 75.6 frames per second (FPS) in individual action recognition. These results have surpassed state-of-the-art benchmarks. This model demonstrates outstanding performance in real-world applications, providing valuable technical insights for improving sports action recognition and advancing computer vision technologies.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Power Physiological Fatigue Monitoring via TinyML-Enabled Wearables for Sports Evaluation","authors":"Yuqiu Zhang","doi":"10.1002/itl2.70053","DOIUrl":"https://doi.org/10.1002/itl2.70053","url":null,"abstract":"<div>\u0000 \u0000 <p>In the context of the growing integration of Internet of Things (IoT) and edge intelligence into sports technology, the ability to accurately monitor athlete fatigue in real time has become increasingly important for performance optimization and injury prevention. This paper presents a novel fatigue detection framework that leverages physiological signal fusion and personalized activity recognition, optimized for resource-constrained IoT devices using Tiny Machine Learning (TinyML) techniques. The proposed system combines inertial and heart rate signals collected from wearable devices and computes a lightweight, on-device Physiological Fatigue Index (PFI), enhanced with personalized calibration and adaptive thresholding. To support deployment on ultra-low-power microcontrollers, we apply quantization, pruning, and model distillation, reducing memory footprint and energy consumption while preserving high accuracy. Experimental results on data collected from 12 athletes demonstrate the effectiveness of the approach, achieving 93.4% accuracy and 44 mWh hourly power use, outperforming several state-of-the-art TinyML and classical baselines. This work contributes a deployable, scalable, and privacy-aware solution for continuous sports fatigue assessment in real-world environments.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Communication MIMO System for Performance of Thermal Noise, and Nonlinearity Losses of an RF Transceiver Phase Noise an RF in 6G","authors":"Malek Suliman Alshnaikat,&nbsp;Bushra N. Alsunbuli,&nbsp;Nor M. Mahyuddin,&nbsp;Widad Ismail,&nbsp;Anas Atef Shamaileh,&nbsp;Manal Hasan Jamil Barqawi,&nbsp;Lena Farrah,&nbsp;Bilal A. Ozturk","doi":"10.1002/itl2.70087","DOIUrl":"https://doi.org/10.1002/itl2.70087","url":null,"abstract":"<div>\u0000 \u0000 <p>Improved dependability, reduced latency, and great spectrum efficiency, especially in MIMO systems, are essential for 6G networks to be achieved in wireless communication. However, RF impairments, such as thermal noise, phase noise, and nonlinearity losses, severely hinder multiple-input multiple-output (MIMO) communication systems. These deficiencies restrict performance in high-frequency 6G settings due to signal distortion, phase instability, and a poor signal-to-noise ratio (SNR). An Adaptive RF Compensation and Distortion Mitigation Framework (ARC-DMF) is proposed here to resolve these issues. Dynamic compensation for the distortions inflicted by radio frequency interference is accomplished through the framework's adaptive filtering and machine learning-based correction system, ensuring good signal transmission and enhanced data integrity. The ARC-DMF approach employs digital pre-distortion (DPD) techniques, deep learning-based compensation modeling, and real-time distortion estimation to minimize the effects of nonlinear distortion effects, phase noise variations, and thermal noise fluctuations. Bit error rate (BER), capacity enhancement, error vector magnitude (EVM), and spectral efficiency are some of the important performance metrics tested in several 6G operational scenarios through substantial simulations. The simulation findings show that ARC-DMF substantially improves the performance of MIMO communication over traditional compensation methods, with reduced BER, better resilience to phase noise, and more resilient transmission. Enabling efficient and reliable wireless communication in high-frequency RF situations, this study's findings offer insights into RF impairment mitigation for next-generation 6G MIMO systems.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}