Internet Technology Letters最新文献

{"title":"Resilience of the Invisible Internet Project: A Computational Analysis","authors":"Siddique Abubakr Muntaka,&nbsp;Jacques Bou Abdo","doi":"10.1002/itl2.70119","DOIUrl":"https://doi.org/10.1002/itl2.70119","url":null,"abstract":"<div>\u0000 \u0000 <p>The invisible internet project (I2P) is a decentralized peer-to-peer anonymity network that protects users' privacy by routing traffic through encrypted tunnels across volunteer-run routers (nodes). Its distributed nature raises critical questions about structural resilience; specifically, how well it can withstand random (stochastic) failures and targeted (adversarial) attacks. This study models I2P's overlay using three representative network graphs or topologies: random graph (RG), scale-free (SF), and a theoretical modeling of I2P's network, herein referred to as <i>I2P Prime</i> (I2P′), all experimented with 50 000 nodes (peers) each to reflect the real-world conditions of the I2P network. Under random failures, all models exhibit high tolerance, maintaining a large connected component (LCC) even after 50% node removal, with I2P′ demonstrating the most graceful degradation in network efficiency. However, targeted attacks based on degree or betweenness centrality reveal substantial vulnerabilities. The SF network model of I2P collapsed rapidly, often below 30% node removal due to its hub-centric design. In contrast, I2P′ exhibits stronger fault tolerance, requiring nearly 50% of critical nodes to be removed before global connectivity fails. These findings highlight the structural advantages of I2P′, which strikes a balance between distributed connectivity and high resilience against both random failures and targeted attacks. For developers, enhanced adaptive peer selection and dynamic routing mechanisms could enhance robustness without undermining anonymity. For policymakers, our results highlight how targeted interventions might fragment illicit activity with minimal collateral impact. This work provides actionable insights into designing resilient anonymity networks that preserve privacy under stochastic and adversarial attacks.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843493","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-Cloud Collaboration Quality Measurement for Physical Education","authors":"Chunming Wang,&nbsp;Enqian Xing","doi":"10.1002/itl2.70110","DOIUrl":"https://doi.org/10.1002/itl2.70110","url":null,"abstract":"<div>\u0000 \u0000 <p>The public physical education is always outdoors, which makes the real-time evaluation of teaching ability difficult for physical education teachers. In order to adapt to the outdoor environment, this paper proposes an edge-cloud collaboration-based physical education evaluation method. First, the wearable devices are worn by students to collect their real-time status. Second, the students' data are transmitted to a cloud server via a wireless network. In the cloud server, a deployed AI model is used to evaluate the physical class. The quality of physical education is divided into five ranks in which there is a strict ordinal relation. In order to reflect the ordinal relation, this paper adopts support vector ordinal regression (SVOR) as the AI model. The SVOR model is learned offline using the students' data from wearable devices and the scores from experts. The scores include teaching attitude, teaching implementation, teaching academia, and teaching development. The simulation shows that the proposed physical education evaluation method can return the real-time quality result. Compared with traditional classification models, the SVOR can achieve much less mean absolute error (MAE) due to considering the ordinal relation in it.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832795","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":"High-Efficiency Triple-Band Antenna Design for Next-Generation Wireless Technologies","authors":"Krishnamoorthy N,&nbsp;Varalatchoumy M,&nbsp;Aruna R,&nbsp;Gandi Satyanarayana,&nbsp;Karthikeyan P,&nbsp;Rajesh Kumar E","doi":"10.1002/itl2.70078","DOIUrl":"https://doi.org/10.1002/itl2.70078","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents a compact antenna design with a tailored ground structure optimized for triple-band wireless applications. The proposed antenna operates efficiently across three distinct frequency bands: 1.9–5 GHz, 6.1–7.4 GHz, and 8.5–10.2 GHz, making it ideal for emerging wireless technologies including 5G, Wi-Fi 6E, and X-band communications. Comprehensive design optimization yields consistent S₁₁ values below −10 dB within these bands, delivering bandwidths of 3.1, 1.3, and 1.7 GHz, respectively. The antenna achieves a peak radiation efficiency of 80% and a gain of 4.5 dBi, all within a simple and compact structure suitable for versatile wireless applications.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811054","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 Management of E-Commerce Evaluation System Based on RBM and DBN Missing Rating Detection","authors":"Shaobin Dong,&nbsp;Aihua Li,&nbsp;Decai Kong","doi":"10.1002/itl2.70086","DOIUrl":"https://doi.org/10.1002/itl2.70086","url":null,"abstract":"<div>\u0000 \u0000 <p>In e-commerce evaluation systems, missing evaluation data is a common problem. It can lead to fake reviews by malicious users, affecting users' decisions on products and services. Therefore, this study introduces Restricted Boltzmann Machines (RBM) and Deep Belief Networks (DBN) to fill in missing rating data for sparsely rated users. An iterative optimization ranking method is also used to improve user reputation values for identifying malicious users. The results show that on the Netflix dataset, the DBN model achieves an accuracy of 91.05% and an F1 score of 89.79%. On the Movielens dataset, the DBN model achieves an accuracy of 97.53% and an F1 score of 96.42%, which is a 13.08% and 12.73% decrease in accuracy and F1 score compared to the Support Vector Machine (SVM) model. On the Movielens-100 dataset, the DBN model achieves an accuracy of 86.11% and an F1 score of 84.27%, significantly outperforming the other two models. These results demonstrate the significant performance of the proposed method in data filling and malicious user detection in evaluation systems. It has important application value in the management of e-commerce evaluation systems.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144782367","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":"Integration of 5G and 4G Communication in Battery Management Systems for Electric Vehicles: A Cloud-Based Architecture for Enhanced Performance and Analytics","authors":"R. Suganya,&nbsp;L. M. I. Leo Joseph,&nbsp;Sreedhar Kollem","doi":"10.1002/itl2.70112","DOIUrl":"https://doi.org/10.1002/itl2.70112","url":null,"abstract":"<div>\u0000 \u0000 <p>The Cloud-Based Architecture is proposed for the Integration of 4G and 5G Communication in a Battery Management System (BMS) for Electric Vehicles (EV). This study compares Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and an AI-optimized BMS algorithm. The AI-optimized BMS has recorded 88% State of Health (SoH), with a good old traditional BMS only managing 72%. Furthermore, the AI model reaches 85% energy efficiency, 20 ms latency, and 92% fault detection accuracy, surpassing existing approaches. Using Network performance analysis, 5G has 2.5× more throughput, and less latency (approx. 60% less than 4G), empowering real-time monitoring. This can make Over-the-air (OTA) updates 98% reliable with 5G and 85% with 4G, ensuring the software updates success rate. Incorporating this AI-based BMS system with 5G provides efficient automation of the battery management process, improving battery lifespan, energy efficiency, and enabling fault detection, predictive analysis, and remote battery update. Ideal for next-gen EV implementations, this scalable and cloud-based edge solution extends performance with low operational expenditure and optimal battery lifecycle management.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144782354","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":"End-To-End Anomaly Detection of Service Function Chains in Cloud-Native Systems Using a Self-Guided Quantum Generative Adversarial Network","authors":"N. Ashokkumar,&nbsp;N. Suma,&nbsp;R. Kiruthikaa,&nbsp;K. Vijayakumar,&nbsp;C. Thilagavathi","doi":"10.1002/itl2.70103","DOIUrl":"https://doi.org/10.1002/itl2.70103","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud-native technology enables Network Functions Virtualization (NFV) to dynamically provide and deploy network services in the Industrial Internet of Things (IIoT). However, compared to traditional hardware solutions, Service Function Chains (SFCs) are more vulnerable to faults in complex and dynamically changing cloud environments, requiring advanced anomaly detection techniques. Existing methods often struggle with accuracy, scalability, and efficiency in such environments, particularly due to high false positive rates (up to 15%) and poor adaptability to rapid scaling and latency-sensitive operations. This paper proposes a new Self-Guided Quantum Generative Adversarial Network with Puma Optimizer (SGQGANet-PO) for cloud-native SFC anomaly detection. The model benefits from the FullSight Dataset, beginning with Min-Max Normalization (MMN) for uniform feature scaling and Fast Pure Transformer Network (FPTN) for fast text feature extraction. SGQGANet-PO is based on quantum-inspired methods and is optimized with the Puma Optimizer to improve the robustness and convergence of the model. The method proposed has an accuracy of 99.76%, a precision of 99.5%, recall of 97.8%, F1-score of 98.6%, and an extremely low 0.24% error rate. The outcome shows better performance than other methods, providing a safe method for anomaly detection in cloud-native systems.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144782201","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":"Enhancing Edge Intelligence in Wireless Communication Networks Using Large Models for Security and Adaptive Control","authors":"Anshika Sharma,&nbsp;Shalli Rani","doi":"10.1002/itl2.70096","DOIUrl":"https://doi.org/10.1002/itl2.70096","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless communication networks (WCN) are becoming more complicated and dynamic, especially when it comes to edge computing. As a result, intelligent, self-governing systems that can manage security and control duties in real time are required. This paper presents a novel Edge Transformer for Security and Adaptive Control (EdgeFormer-SAC), a Transformer-based large model (LM) designed for edge environments that is compact and effective. Using a compressed Transformer architecture designed for low-latency and low-energy situations, the novel EdgeFormer-SAC combines security anomaly detection and adaptive control to jointly manage multi-task learning at the wireless edge. The proposed EdgeFormer-SAC model has been evaluated against well-known machine learning (ML) models including Support Vector Machine (SVM), deep learning (DL) models including Long-Short Term Memory (LSTM), Mobile Network Version 2 (MobileNetV2), Tiny Bidirectional Encoder Representations from Transformers (TinyBERT), and Deep Reinforcement Learning Agent (DRL) techniques through extensive tests in simulated wireless environments. The proposed EdgeFormer-SAC model maintained a real-time latency of 17.5 ms and low energy consumption at 1.3 W, while achieving the greatest accuracy and F1-score of 94.8% and 93%, respectively, and a false positive rate (FPR) of only 2.3% and an adaptation score of 89%.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 5","pages":""},"PeriodicalIF":0.5,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773843","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":"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}