IEEE Transactions on Machine Learning in Communications and Networking最新文献

{"title":"Deep Ensemble Learning With Pruning for DDoS Attack Detection in IoT Networks","authors":"Makhduma F. Saiyedand;Irfan Al-Anbagi","doi":"10.1109/TMLCN.2024.3395419","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3395419","url":null,"abstract":"The upsurge of Internet of Things (IoT) devices has increased their vulnerability to Distributed Denial of Service (DDoS) attacks. DDoS attacks have evolved into complex multi-vector threats that high-volume and low-volume attack strategies, posing challenges for detection using traditional methods. These challenges highlight the importance of reliable detection and prevention measures. This paper introduces a novel Deep Ensemble learning with Pruning (DEEPShield) system, to efficiently detect both high- and low-volume DDoS attacks in resource-constrained environments. The DEEPShield system uses ensemble learning by integrating a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network with a network traffic analysis system. This system analyzes and preprocesses network traffic while being data-agnostic, resulting in high detection accuracy. In addition, the DEEPShield system applies unit pruning to refine ensemble models, optimizing them for deployment on edge devices while maintaining a balance between accuracy and computational efficiency. To address the lack of a detailed dataset for high- and low-volume DDoS attacks, this paper also introduces a dataset named HL-IoT, which includes both attack types. Furthermore, the testbed evaluation of the DEEPShield system under various load scenarios and network traffic loads showcases its effectiveness and robustness. Compared to the state-of-the-art deep ensembles and deep learning methods across various datasets, including HL-IoT, ToN-IoT, CICIDS-17, and ISCX-12, the DEEPShield system consistently achieves an accuracy over 90% for both DDoS attack types. Furthermore, the DEEPShield system achieves this performance with reduced memory and processing requirements, underscoring its adaptability for edge computing scenarios.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"596-616"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10513369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895040","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}

{"title":"Federated Analytics With Data Augmentation in Domain Generalization Toward Future Networks","authors":"Xunzheng Zhang;Juan Marcelo Parra-Ullauri;Shadi Moazzeni;Xenofon Vasilakos;Reza Nejabati;Dimitra Simeonidou","doi":"10.1109/TMLCN.2024.3393892","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3393892","url":null,"abstract":"Federated Domain Generalization (FDG) aims to train a global model that generalizes well to new clients in a privacy-conscious manner, even when domain shifts are encountered. The increasing concerns of knowledge generalization and data privacy also challenge the traditional gather-and-analyze paradigm in networks. Recent investigations mainly focus on aggregation optimization and domain-invariant representations. However, without directly considering the data augmentation and leveraging the knowledge among existing domains, the domain-only data cannot guarantee the generalization ability of the FDG model when testing on the unseen domain. To overcome the problem, this paper proposes a distributed data augmentation method which combines Generative Adversarial Networks (GANs) and Federated Analytics (FA) to enhance the generalization ability of the trained FDG model, called FA-FDG. First, FA-FDG integrates GAN data generators from each Federated Learning (FL) client. Second, an evaluation index called generalization ability of domain (GAD) is proposed in the FA server. Then, the targeted data augmentation is implemented in each FL client with the GAD index and the integrated data generators. Extensive experiments on several data sets have shown the effectiveness of FA-FDG. Specifically, the accuracy of the FDG model improves up to 5.12% in classification problems, and the R-squared index of the FDG model advances up to 0.22 in the regression problem.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"560-579"},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10508396","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140818800","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}

{"title":"Multi-Agent Double Deep Q-Learning for Fairness in Multiple-Access Underlay Cognitive Radio Networks","authors":"Zain Ali;Zouheir Rezki;Hamid Sadjadpour","doi":"10.1109/TMLCN.2024.3391216","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3391216","url":null,"abstract":"Underlay Cognitive Radio (CR) systems were introduced to resolve the issue of spectrum scarcity in wireless communication. In CR systems, an unlicensed Secondary Transmitter (ST) shares the channel with a licensed Primary Transmitter (PT). Spectral efficiency of the CR systems can be further increased if multiple STs share the same channel. In underlay CR systems, the STs are required to keep interference at a low level to avoid outage at the primary system. The restriction on interference in underlay CR prevents some STs from transmitting while other STs may achieve high data rates, thus making the underlay CR network unfair. In this work, we consider the problem of achieving fairness in the rates of the STs. The considered optimization problem is non-convex in nature. The conventional iteration-based optimizers are time-consuming and may not converge when the considered problem is non-convex. To deal with the problem, we propose a deep-Q reinforcement learning (DQ-RL) framework that employs two separate deep neural networks for the computation and estimation of the Q-values which provides a fast solution and is robust to channel dynamic. The proposed technique achieves near optimal values of fairness while offering primary outage probability of less than 4%. Further, increasing the number of STs results in a linear increase in the computational complexity of the proposed framework. A comparison of several variants of the proposed scheme with the optimal solution is also presented. Finally, we present a novel cumulative reward framework and discuss how the combined-reward approach improves the performance of the communication system.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"580-595"},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504881","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820363","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}

{"title":"Online Learning to Cache and Recommend in the Next Generation Cellular Networks","authors":"Krishnendu S Tharakan;B. N. Bharath;Vimal Bhatia","doi":"10.1109/TMLCN.2024.3388975","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3388975","url":null,"abstract":"An efficient caching can be achieved by predicting the popularity of the files accurately. It is well known that the popularity of a file can be nudged by using recommendation, and hence it can be estimated accurately leading to an efficient caching strategy. Motivated by this, in this paper, we consider the problem of joint caching and recommendation in a 5G and beyond heterogeneous network. We model the influence of recommendation on demands by a Probability Transition Matrix (PTM). The proposed framework consists of estimating the PTM and use them to jointly recommend and cache the files. In particular, this paper considers two estimation methods namely a) \u0000<monospace>Bayesian estimation</monospace>\u0000 and b) a genie aided \u0000<monospace>Point estimation</monospace>\u0000. An approximate high probability bound on the regret of both the estimation methods are provided. Using this result, we show that the approximate regret achieved by the genie aided \u0000<monospace>Point estimation</monospace>\u0000 approach is \u0000<inline-formula> <tex-math>$mathcal {O}(T^{2/3} sqrt {log T})$ </tex-math></inline-formula>\u0000 while the \u0000<monospace>Bayesian estimation</monospace>\u0000 method achieves a much better scaling of \u0000<inline-formula> <tex-math>$mathcal {O}(sqrt {T})$ </tex-math></inline-formula>\u0000. These results are extended to a heterogeneous network consisting of M small base stations (SBSs) with a central macro base station. The estimates are available at multiple SBSs, and are combined using appropriate weights. Insights on the choice of these weights are provided by using the derived approximate regret bound in the multiple SBS case. Finally, simulation results confirm the superiority of the proposed algorithms in terms of average cache hit rate, delay and throughput.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"511-525"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140639381","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}

{"title":"A Link-Quality Anomaly Detection Framework for Software-Defined Wireless Mesh Networks","authors":"Sotiris Skaperas;Lefteris Mamatas;Vassilis Tsaoussidis","doi":"10.1109/TMLCN.2024.3388973","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3388973","url":null,"abstract":"Software-defined wireless mesh networks are being increasingly deployed in diverse settings, such as smart cities and public Wi-Fi access infrastructures. The signal propagation and interference issues that typically characterize these environments can be handled by employing SDN controller mechanisms, effectively monitoring link quality and triggering appropriate mitigation strategies, such as adjusting link and/or routing protocols. In this paper, we propose an unsupervised machine learning (ML) online framework for link quality detection consisting of: 1) improved preprocessing clustering algorithm, based on elastic similarity measures, to efficiently characterize wireless links in terms of reliability, and 2) a novel change point (CP) detector for the real-time identification of anomalies in the quality of selected links, which minimizes the overestimation error through the incorporation of a rank-based test and a recursive max-type procedure. In this sense, considering the communication constraints of such environments, our approach minimizes the detection overhead and the inaccurate decisions caused by overestimation. The proposed detector is validated, both on its individual components and as an overall mechanism, against synthetic but also real data traces; the latter being extracted from real wireless mesh network deployments.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"495-510"},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10499246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140639411","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}

{"title":"STTMC: A Few-Shot Spatial Temporal Transductive Modulation Classifier","authors":"Yunhao Shi;Hua Xu;Zisen Qi;Yue Zhang;Dan Wang;Lei Jiang","doi":"10.1109/TMLCN.2024.3387430","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3387430","url":null,"abstract":"The advancement of deep learning (DL) techniques has led to significant progress in Automatic Modulation Classification (AMC). However, most existing DL-based AMC methods require massive training samples, which are difficult to obtain in non-cooperative scenarios. The identification of modulation types under small sample conditions has become an increasingly urgent problem. In this paper, we present a novel few-shot AMC model named the Spatial Temporal Transductive Modulation Classifier (STTMC), which comprises two modules: a feature extraction module and a graph network module. The former is responsible for extracting diverse features through a spatiotemporal parallel network, while the latter facilitates transductive decision-making through a graph network that uses a closed-form solution. Notably, STTMC classifies a group of test signals simultaneously to increase stability of few-shot model with an episode training strategy. Experimental results on the RadioML.2018.01A and RadioML.2016.10A datasets demonstrate that the proposed method perform well in 3way-Kshot, 5way-Kshot and 10way-Kshot configurations. In particular, STTMC outperforms other existing AMC methods by a large margin.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"546-559"},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10497130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140648000","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}

{"title":"A Deep Learning Based Induced GNSS Spoof Detection Framework","authors":"Asif Iqbal;Muhammad Naveed Aman;Biplab Sikdar","doi":"10.1109/TMLCN.2024.3386649","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3386649","url":null,"abstract":"The Global Navigation Satellite System (GNSS) plays a crucial role in critical infrastructure by delivering precise timing and positional data. Nonetheless, the civilian segment of the GNSS remains susceptible to various spoofing attacks, necessitating robust detection mechanisms. The ability to deter such attacks significantly enhances the reliability and security of systems utilizing GNSS technology. Supervised Machine Learning (ML) techniques have shown promise in spoof detection. However, their effectiveness hinges on training data encompassing all possible attack scenarios, rendering them vulnerable to novel attack vectors. To address this limitation, we explore representation learning-based methods. These methods can be trained with a single data class and subsequently applied to classify test samples as either belonging to the training class or not. In this context, we introduce a GNSS spoof detection model comprising a Variational AutoEncoder (VAE) and a Generative Adversarial Network (GAN). The composite model is designed to efficiently learn the class distribution of the training data. The features used for training are extracted from the radio frequency and tracking modules of a standard GNSS receiver. To train our model, we leverage the Texas Spoofing Test Battery (TEXBAT) datasets. Our trained model yields three distinct detectors capable of effectively identifying spoofed signals. The detection performance across simpler to intermediate datasets for these detectors reaches approximately 99%, demonstrating their robustness. In the case of subtle attack scenario represented by DS-7, our approach achieves an approximate detection rate of 95%. In contrast, under supervised learning, the best detection score for DS-7 remains limited to 44.1%.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"457-478"},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10495074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633565","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}

{"title":"Fast Context Adaptation in Cost-Aware Continual Learning","authors":"Seyyidahmed Lahmer;Federico Mason;Federico Chiariotti;Andrea Zanella","doi":"10.1109/TMLCN.2024.3386647","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3386647","url":null,"abstract":"In the past few years, Deep Reinforcement Learning (DRL) has become a valuable solution to automatically learn efficient resource management strategies in complex networks with time-varying statistics. However, the increased complexity of 5G and Beyond networks requires correspondingly more complex learning agents and the learning process itself might end up competing with users for communication and computational resources. This creates friction: on the one hand, the learning process needs resources to quickly converge to an effective strategy; on the other hand, the learning process needs to be efficient, i.e., take as few resources as possible from the user’s data plane, so as not to throttle users’ Quality of Service (QoS). In this paper, we investigate this trade-off, which we refer to as cost of learning, and propose a dynamic strategy to balance the resources assigned to the data plane and those reserved for learning. With the proposed approach, a learning agent can quickly converge to an efficient resource allocation strategy and adapt to changes in the environment as for the Continual Learning (CL) paradigm, while minimizing the impact on the users’ QoS. Simulation results show that the proposed method outperforms static allocation methods with minimal learning overhead, almost reaching the performance of an ideal out-of-band CL solution.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"479-494"},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10495063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633566","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}

{"title":"Deep Reinforcement Learning-Based Robust Design for an IRS-Assisted MISO-NOMA System","authors":"Abdulhamed Waraiet;Kanapathippillai Cumanan;Zhiguo Ding;Octavia A. Dobre","doi":"10.1109/TMLCN.2024.3385748","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3385748","url":null,"abstract":"In this paper, we propose a robust design for an intelligent reflecting surface (IRS)-assisted multiple-input single output non-orthogonal multiple access (NOMA) system. By considering channel uncertainties, the original robust design problem is formulated as a sum-rate maximization problem under a set of constraints. In particular, the uncertainties associated with reflected channels through IRS elements and direct channels are taken into account in the design and they are modelled as bounded errors. However, the original robust problem is not jointly convex in terms of beamformers at the base station and phase shifts of IRS elements. Therefore, we reformulate the original robust design as a reinforcement learning problem and develop an algorithm based on the twin-delayed deep deterministic policy gradient agent (also known as TD3). In particular, the proposed algorithm solves the original problem by jointly designing the beamformers and the phase shifts, which is not possible with conventional optimization techniques. Numerical results are provided to validate the effectiveness and evaluate the performance of the proposed robust design. In particular, the results demonstrate the competitive and promising capabilities of the proposed robust algorithm, which achieves significant gains in terms of robustness and system sum-rates over the baseline deep deterministic policy gradient agent. In addition, the algorithm has the ability to deal with fixed and dynamic channels, which gives deep reinforcement learning methods an edge over hand-crafted convex optimization-based algorithms.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"424-441"},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10494408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633557","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}

{"title":"A DDPG-Based Zero-Touch Dynamic Prioritization to Address Starvation of Services for Deploying Microservices-Based VNFs","authors":"Swarna B. Chetty;Hamed Ahmadi;Avishek Nag","doi":"10.1109/TMLCN.2024.3386152","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3386152","url":null,"abstract":"The sixth generation of mobile networks (6G) promises applications and services with faster data rates, ultra-reliability, and lower latency compared to the fifth-generation mobile networks (5G). These highly demanding 6G applications will burden the network by imposing stringent performance requirements. Network Function Virtualization (NFV) reduces costs by running network functions as Virtual Network Functions (VNFs) on commodity hardware. While NFV is a promising solution, it poses Resource Allocation (RA) challenges. To enhance RA efficiency, we addressed two critical subproblems: the requirement of dynamic service priority and a low-priority service starvation problem. We introduce ‘Dynamic Prioritization’ (DyPr), employing an ML model to emphasize macro- and microlevel priority for unseen services and address the existing starvation problem in current solutions and their limitations. We present ‘Adaptive Scheduling’ (AdSch), a three-factor approach (priority, threshold waiting time, and reliability) that surpasses traditional priority-based methods. In this context, starvation refers to extended waiting times and the eventual rejection of low-priority services due to a ‘delay. Also, to further investigate, a traffic-aware starvation and deployment problem is studied to enhance efficiency. We employed a Deep Deterministic Policy Gradient (DDPG) model for adaptive scheduling and an online Ridge Regression (RR) model for dynamic prioritization, creating a zero-touch solution. The DDPG model efficiently identified ‘Beneficial and Starving’ services, alleviating the starvation issue by deploying twice as many low-priority services. With an accuracy rate exceeding 80%, our online RR model quickly learns prioritization patterns in under 100 transitions. We categorized services as ‘High-Demand’ (HD) or ‘Not So High Demand’ (NHD) based on traffic volume, providing insight into high revenue-generating services. We achieved a nearly optimal resource allocation by balancing low-priority HD and low-priority NHD services, deploying twice as many low-priority HD services as a model without traffic awareness.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"526-545"},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10494765","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140647819","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}