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Revolutionizing Cyber Defense: Leveraging Generative AI for Adaptive Threat Hunting 革命性的网络防御：利用生成式人工智能进行自适应威胁搜索
IF 0.9
Internet Technology Letters Pub Date : 2025-06-26 DOI: 10.1002/itl2.70039
Aditya K. Sood, Sherali Zeadally
{"title":"Revolutionizing Cyber Defense: Leveraging Generative AI for Adaptive Threat Hunting","authors":"Aditya K. Sood,&nbsp;Sherali Zeadally","doi":"10.1002/itl2.70039","DOIUrl":"https://doi.org/10.1002/itl2.70039","url":null,"abstract":"<p>Adaptive threat hunting, powered by Generative AI (GenAI), is reshaping the cybersecurity landscape, equipping analysts with the tools to predict, detect, and dynamically mitigate cyber threats. Adaptive threat hunting is critical because it enables an organization to detect threats proactively in the infrastructure, thereby reducing the risks and impacts. We present an adaptive threat hunting enhancement model using GenAI, design considerations, and coverage of real-world use cases, including future considerations.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/itl2.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Ensemble Classification-Based Spectrum Sensing Using Support Vector Machine for Cognitive Radio Networks 认知无线电网络中基于集成分类的支持向量机频谱感知
IF 0.9
Internet Technology Letters Pub Date : 2025-06-25 DOI: 10.1002/itl2.70063
Manpreet Kaur, Raj Singh, Sandeep Kumar
{"title":"Ensemble Classification-Based Spectrum Sensing Using Support Vector Machine for Cognitive Radio Networks","authors":"Manpreet Kaur,&nbsp;Raj Singh,&nbsp;Sandeep Kumar","doi":"10.1002/itl2.70063","DOIUrl":"https://doi.org/10.1002/itl2.70063","url":null,"abstract":"<div>\u0000 \u0000 <p>As next-generation communication systems require more spectrum-intensive applications, the challenge of spectrum scarcity becomes increasingly significant. A promising solution is cognitive radio networks (CRNs), which optimize the use of spectrum, a valuable and sharable natural resource that should not be wasted. To design efficient and sustainable networks for the future, it is crucial to ensure that spectrum sensing is not only accurate and rapid, but also energy-efficient. Spectrum sensing is a critical aspect of CRNs, and this study is mainly focused on it. This research employs a supervised Support Vector Machines (SVM) algorithm to detect primary users (PU). We analyze linear, polynomial, and Gaussian RBF SVM variants and enhance performance using an ensemble classification approach. Simulations show the ensemble classifier achieves the best results.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482044","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}
引用次数: 0
TinyFed: Lightweight Federated Learning for Constrained IoT Devices TinyFed：用于受限物联网设备的轻量级联邦学习
IF 0.9
Internet Technology Letters Pub Date : 2025-06-22 DOI: 10.1002/itl2.70061
Claudio Jr. N. da Silva, Maycon L. M. Peixoto, Gustavo B. Figueiredo, Cassio V. S. Prazeres
{"title":"TinyFed: Lightweight Federated Learning for Constrained IoT Devices","authors":"Claudio Jr. N. da Silva,&nbsp;Maycon L. M. Peixoto,&nbsp;Gustavo B. Figueiredo,&nbsp;Cassio V. S. Prazeres","doi":"10.1002/itl2.70061","DOIUrl":"https://doi.org/10.1002/itl2.70061","url":null,"abstract":"<p>TinyML enables machine learning inference on microcontrollers with limited resources. Extending this to a collaborative setting led to Tiny Federated Learning (TinyFL). This article presents TinyFed, a lightweight framework that supports the full federated learning cycle—from local training to model aggregation and redistribution. TinyFed was validated on ESP32 devices using a neural network with four inputs, three hidden layers, and two outputs to detect temperature, humidity, luminosity, and voltage anomalies. Local training achieved accuracies of up to 99.47%.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/itl2.70061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Zero Trust Networks and Federated Unlearning Based 6G Edge Networks: Attack Scenario, Security Model and Future Directions 零信任网络和基于联邦学习的6G边缘网络：攻击场景、安全模型和未来方向
IF 0.9
Internet Technology Letters Pub Date : 2025-06-22 DOI: 10.1002/itl2.70056
Nishat Mahdiya Khan, Pronaya Bhattacharya, Haipeng Liu, Zhu Zhu, Thippa Reddy Gadekallu
{"title":"Zero Trust Networks and Federated Unlearning Based 6G Edge Networks: Attack Scenario, Security Model and Future Directions","authors":"Nishat Mahdiya Khan,&nbsp;Pronaya Bhattacharya,&nbsp;Haipeng Liu,&nbsp;Zhu Zhu,&nbsp;Thippa Reddy Gadekallu","doi":"10.1002/itl2.70056","DOIUrl":"https://doi.org/10.1002/itl2.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>The dynamic interplay between federated learning (FL) and federated unlearning (FU) introduces vulnerabilities, particularly the slow poisoning attack scenario by malicious adversaries. The attack proceeds where adversaries can gradually degrade global model performance over successive update cycles. In this letter, we propose a blueprint architecture that integrates zero trust networks (ZTNs) into both the unlearning (FU) request and the client admission (FL) stages to counteract these threats. By enforcing continuous client verification and rigorous risk assessment, our vision ensures that only authenticated and reliable updates contribute to the global model, thereby preserving model integrity and safeguarding sensitive data. Promising future research directions and open challenges are also discussed.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339524","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}
引用次数: 0
Online Auxiliary Evaluation of Physical Education Teaching Based on Facial Expression Recognition 基于面部表情识别的体育教学在线辅助评价
IF 0.9
Internet Technology Letters Pub Date : 2025-06-19 DOI: 10.1002/itl2.70065
Yuan Gao
{"title":"Online Auxiliary Evaluation of Physical Education Teaching Based on Facial Expression Recognition","authors":"Yuan Gao","doi":"10.1002/itl2.70065","DOIUrl":"https://doi.org/10.1002/itl2.70065","url":null,"abstract":"<div>\u0000 \u0000 <p>Internet plus technology and artificial intelligence technology are widely used in online sports teaching and curriculum evaluation tasks. However, existing deep network-based online facial expression recognition is susceptible to complex scenarios such as lighting, and occlusion, which directly affect the accuracy of course evaluation. To this end, this paper designs an emotion recognition network based on spatiotemporal hypergraph convolution for robust online emotion analysis. Specifically, we collect facial video sequences from different clients and generate corresponding facial landmark sequences. On the server side, an effective spatiotemporal hypergraph convolutional network is deployed, in which the hypergraph convolution module can exploit high-order relationships between facial landmarks. To verify the effectiveness of our model, we conducted extensive comparative experiments on two public expression datasets and our self-built dataset. The experimental results show that the proposed model obtains higher accuracy and effectively improves the quality of physical education teaching evaluation.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323733","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}
引用次数: 0
Modeling of 5G Based OAM-RoF System Using ZCC OCDMA Code With TDM Over Fiber-FSO Link 基于光纤- fso链路的ZCC OCDMA码与TDM的5G OAM-RoF系统建模
IF 0.9
Internet Technology Letters Pub Date : 2025-06-18 DOI: 10.1002/itl2.70023
Meet Kumari
{"title":"Modeling of 5G Based OAM-RoF System Using ZCC OCDMA Code With TDM Over Fiber-FSO Link","authors":"Meet Kumari","doi":"10.1002/itl2.70023","DOIUrl":"https://doi.org/10.1002/itl2.70023","url":null,"abstract":"<div>\u0000 \u0000 <p>A 32 GHz radio-over-fiber (RoF) system using integrated mode division multiplexing (MDM) and optical code division multiple access (OCDMA) scheme is realized. Quad orbital angular momentum (OAM) based zero cross-correlation OCDMA coded signals with time division multiplexing are transmitted over hybrid fiber-free space optics (FSO) link under the impact of clear air, haze, light, and moderate fog conditions. Simulation results depict that the system offers a maximum 0.1 km fiber and 500 m FSO range at aggregate 30 Gbps throughput. Also, the system offers acceptable bit error rate performance with a maximum 14 μm spot size, 14 cm transmitter/receiver aperture diameters, and a minimum received power of −2 dBm. Compared to existing designs, this work offers optimum system performance.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144309036","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}
引用次数: 0
A Machine Learning Framework for Enhancing 5G mmWave Radio Frequency Prediction 增强5G毫米波射频预测的机器学习框架
IF 0.9
Internet Technology Letters Pub Date : 2025-06-16 DOI: 10.1002/itl2.70057
Shantha Mary Joshitta, Dukhbhanjan Singh, Sagar Gulati, Pooja Sapra, Romil Jain, Diksha Aggarwal
{"title":"A Machine Learning Framework for Enhancing 5G mmWave Radio Frequency Prediction","authors":"Shantha Mary Joshitta,&nbsp;Dukhbhanjan Singh,&nbsp;Sagar Gulati,&nbsp;Pooja Sapra,&nbsp;Romil Jain,&nbsp;Diksha Aggarwal","doi":"10.1002/itl2.70057","DOIUrl":"https://doi.org/10.1002/itl2.70057","url":null,"abstract":"<div>\u0000 \u0000 <p>5G mmWave technology offers high data rates and bandwidth, but high path loss and environmental changes affect signal quality. Existing models are not suitable for mmWave channels due to their varying nature over time. To overcome these challenges, this research presents an efficient time-dependent channel modeling framework based on a cuttlefish search-inspired efficient support vector machine (CS-ESVM) for predicting channel characteristics in large-scale measurement and RF at specific measurements at LOS and NLOS. The model is proposed to work for measurements at 28 GHz at a substation. The model also combines a prediction model and playback model for accurate channel characteristics key metrics such as root mean square error (RMSE), mean absolute percent error (MAPE), and correlation coefficient (CC), predicting the radio frequency. The proposed CS-ESVM model achieved the lowest RMSE values of 2.510 (LOS corridor), 1.210 (LOS hall), 1.815 (NLOS corridor), and 1.917 (NLOS hall), the lowest MAPE values of 0.009, 0.004, 0.003, and 0.007, and the highest CC values of 0.899, 0.969, 0.921, and 0.985. The findings suggest that CS-ESVM is more effective at predicting the mmWave channel's characteristics than traditional approaches. In conclusion, this ML-based framework improves the projection of 5G mmWave RF channels and provides a stable solution for real-time prediction in future network environments.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299864","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}
引用次数: 0
Edge Computing Oriented Decision and Optimization Method for Efficient and Intelligent Human Resource Management and Analysis 面向边缘计算的高效智能人力资源管理与分析决策与优化方法
IF 0.9
Internet Technology Letters Pub Date : 2025-06-12 DOI: 10.1002/itl2.70054
Meiyi Lin
{"title":"Edge Computing Oriented Decision and Optimization Method for Efficient and Intelligent Human Resource Management and Analysis","authors":"Meiyi Lin","doi":"10.1002/itl2.70054","DOIUrl":"https://doi.org/10.1002/itl2.70054","url":null,"abstract":"<div>\u0000 \u0000 <p>Modern enterprises face significant challenges in achieving real-time, intelligent workforce management due to the limitations of centralized cloud-based solutions in dynamic operational environments. This paper proposes an edge computing-oriented decision and optimization method for efficient and intelligent human resource management and analysis. First, we design a hierarchical edge-cloud architecture comprising infrastructure, edge, cloud, and application layers, specifically optimized for workforce data processing through localized decision modules. Second, we develop a TinyML-enhanced multi-objective optimization method that concurrently addresses the intelligent HR data sentiment analysis and optimal resource decision towards privacy and latency minimization, as well as F1 score maximization. Specifically, we establish the data analysis model, based on which we construct the problem as a multi-objective decision model to be addressed and obtain the optimization solution. Lastly, we carry out rich experiments which show that the proposed method achieves better performance than the compared methods, including achieving the F1 score over 90% and reducing the population size of model parameters greatly.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273254","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}
引用次数: 0
A Novel Machine Learning Architecture for Traffic Grooming and Resource Optimization in 5G Optical Fronthaul 5G光前传流量疏导与资源优化的新型机器学习架构
IF 0.9
Internet Technology Letters Pub Date : 2025-06-09 DOI: 10.1002/itl2.70052
Aiman Mailybayeva, Saurabh Jain, Jaspreet Sidhu, Nitish Vashisht, Narmadha Thangarasu, Saumya Goyal
{"title":"A Novel Machine Learning Architecture for Traffic Grooming and Resource Optimization in 5G Optical Fronthaul","authors":"Aiman Mailybayeva,&nbsp;Saurabh Jain,&nbsp;Jaspreet Sidhu,&nbsp;Nitish Vashisht,&nbsp;Narmadha Thangarasu,&nbsp;Saumya Goyal","doi":"10.1002/itl2.70052","DOIUrl":"https://doi.org/10.1002/itl2.70052","url":null,"abstract":"<div>\u0000 \u0000 <p>The emergence of 5G networks has emerged as innovative solutions for traffic grooming and resource management in optical fronthaul networks. Traditional methods are often incapable of managing the complexity of different traffic patterns, low latencies, and high bandwidth consumption, which leads to suboptimal resource allocation and, consequently, high operating costs. The objective is to develop an innovative machine learning (ML) architecture called Intelligent Multi-Attentive Generative Adversarial Networks (IMAGAN) for maximizing resource utilization and traffic grooming (TG) in 5G optical fronthaul networks. The suggested IMAGAN-based architecture consists of a multi-attentive model for identifying spatiotemporal traffic patterns combined with a generative adversarial model to provide synthetic network scenarios. The findings indicate that the IMAGAN-based architecture enhances the performance of energy management systems in terms of resource utilization ratio, bandwidth utilization ratio, rejection ratio, MAE, and RMSE. The findings of the study offer a strong foundation for further improvements in intelligent 5G network design and management.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244756","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}
引用次数: 0
TinyML Induced Portable Food Defect Detection for Edge Computing TinyML诱导的便携式食品缺陷检测边缘计算
IF 0.9
Internet Technology Letters Pub Date : 2025-06-04 DOI: 10.1002/itl2.70044
Yanxia Liu
{"title":"TinyML Induced Portable Food Defect Detection for Edge Computing","authors":"Yanxia Liu","doi":"10.1002/itl2.70044","DOIUrl":"https://doi.org/10.1002/itl2.70044","url":null,"abstract":"<div>\u0000 \u0000 <p>Food defect detection is a critical link in ensuring food safety. Traditional machine vision-based detection systems rely on cloud servers to complete algorithmic inference, suffering from problems such as large detection equipment volume and insufficient real-time performance. This paper proposes a portable lightweight detection scheme based on Terminal Machine Learning (TinyML) and Mobile Edge Computing (MEC). Through lightweight neural network model compression technology and edge node task collaboration mechanisms, low-power operation and millisecond-level response of the detection equipment are achieved. Experimental results show that in the scenario of fruit surface defect detection, the system achieves a detection accuracy of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>95.2</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$$ 95.2% $$</annotation>\u0000 </semantics></math>, with a single-frame inference power consumption of only 85 mW, meeting the practical application requirements of portable devices.</p>\u0000 </div>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206656","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}
引用次数: 0
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