IEEE Transactions on Machine Learning in Communications and Networking最新文献_第9页

Reinforcement Learning for Robust Header Compression (ROHC) Under Model Uncertainty 模型不确定情况下鲁棒性标题压缩（ROHC）的强化学习

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-04 DOI: 10.1109/TMLCN.2024.3409200

Shusen Jing;Songyang Zhang;Zhi Ding

{"title":"Reinforcement Learning for Robust Header Compression (ROHC) Under Model Uncertainty","authors":"Shusen Jing;Songyang Zhang;Zhi Ding","doi":"10.1109/TMLCN.2024.3409200","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3409200","url":null,"abstract":"Robust header compression (ROHC), critically positioned between network and MAC layers, plays an important role in modern wireless communication networks for improving data efficiency. This work investigates bi-directional ROHC (BD-ROHC) integrated with a novel architecture of reinforcement learning (RL). We formulate a partially observable Markov decision process (POMDP), where the compressor is the POMDP agent, and the environment consists of the decompressor, channel, and header source. Our work adopts the well-known deep Q-network (DQN), which takes the history of actions and observations as inputs, and outputs the Q-values of corresponding actions. Compared with the ideal dynamic programming (DP) proposed in existing works, the newly proposed method is scalable to the state, action, and observation spaces. In contrast, DP often incurs formidable computation costs when the number of states becomes large due to long decompressor feedback delays and complex channel models. In addition, the new method does not require prior knowledge of the transition dynamics and accurate observation dependency of the model, which are often unavailable in practical applications.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1033-1044"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725562","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

Graph Neural Networks Approach for Joint Wireless Power Control and Spectrum Allocation 用于联合无线功率控制和频谱分配的图神经网络方法

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-03 DOI: 10.1109/TMLCN.2024.3408723

Maher Marwani;Georges Kaddoum

{"title":"Graph Neural Networks Approach for Joint Wireless Power Control and Spectrum Allocation","authors":"Maher Marwani;Georges Kaddoum","doi":"10.1109/TMLCN.2024.3408723","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3408723","url":null,"abstract":"The proliferation of wireless technologies and the escalating performance requirements of wireless applications have led to diverse and dynamic wireless environments, presenting formidable challenges to existing radio resource management (RRM) frameworks. Researchers have proposed utilizing deep learning (DL) models to address these challenges to learn patterns from wireless data and leverage the extracted information to resolve multiple RRM tasks, such as channel allocation and power control. However, it is noteworthy that the majority of existing DL architectures are designed to operate on Euclidean data, thereby disregarding a substantial amount of information about the topological structure of wireless networks. As a result, the performance of DL models may be suboptimal when applied to wireless environments due to the failure to capture the network’s non-Euclidean geometry. This study presents a novel approach to address the challenge of power control and spectrum allocation in an N-link interference environment with shared channels, utilizing a graph neural network (GNN) based framework. In this type of wireless environment, the available bandwidth can be divided into blocks, offering greater flexibility in allocating bandwidth to communication links, but also requiring effective management of interference. One potential solution to mitigate the impact of interference is to control the transmission power of each link while ensuring the network’s data rate performance. Therefore, the power control and spectrum allocation problems are inherently coupled and should be solved jointly. The proposed GNN-based framework presents a promising avenue for tackling this complex challenge. Our experimental results demonstrate that our proposed approach yields significant improvements compared to other existing methods in terms of convergence, generalization, performance, and robustness, particularly in the context of an imperfect channel.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"717-732"},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10545547","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141298352","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

Data-Driven Energy Efficiency Modeling in Large-Scale Networks: An Expert Knowledge and ML-Based Approach 大规模网络中数据驱动的能效建模：基于专家知识和 ML 的方法

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-06-03 DOI: 10.1109/TMLCN.2024.3407691

David López-Pérez;Antonio De Domenico;Nicola Piovesan;Mérouane Debbah

{"title":"Data-Driven Energy Efficiency Modeling in Large-Scale Networks: An Expert Knowledge and ML-Based Approach","authors":"David López-Pérez;Antonio De Domenico;Nicola Piovesan;Mérouane Debbah","doi":"10.1109/TMLCN.2024.3407691","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3407691","url":null,"abstract":"The energy consumption of mobile networks poses a critical challenge. Mitigating this concern necessitates the deployment and optimization of network energy-saving solutions, such as carrier shutdown, to dynamically manage network resources. Traditional optimization approaches encounter complexity due to factors like the large number of cells, stochastic traffic, channel variations, and intricate trade-offs. This paper introduces the simulated reality of communication networks (SRCON) framework, a novel, data-driven modeling paradigm that harnesses live network data and employs a blend of machine learning (ML)- and expert-based models. These mix of models accurately characterizes the functioning of network components, and predicts network energy efficiency and user equipment (UE) quality of service for any energy carrier shutdown configuration in a specific network. Distinguishing itself from existing methods, SRCON eliminates the reliance on expensive expert knowledge, drive testing, or incomplete maps for predicting network performance. This paper details the pipeline employed by SRCON to decompose the large network energy efficiency modeling problem into ML- and expert-based submodels. It demonstrates how, by embracing stochasticity, and carefully crafting the relationship between such submodels, the overall computational complexity can be reduced and prediction accuracy enhanced. Results derived from real network data underscore the paradigm shift introduced by SRCON, showcasing significant gains over a state-of-the-art method used by a operator for network energy efficiency modeling. The reliability of this local, data-driven modeling of the network proves to be a key asset for network energy-saving optimization.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"780-804"},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141429918","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

Deep Ensemble Learning With Pruning for DDoS Attack Detection in IoT Networks 利用剪枝深度集合学习检测物联网网络中的 DDoS 攻击

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-04-30 DOI: 10.1109/TMLCN.2024.3395419

Makhduma F. Saiyedand;Irfan Al-Anbagi

{"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}

引用次数: 0

Federated Analytics With Data Augmentation in Domain Generalization Toward Future Networks 面向未来网络的领域泛化中的联合分析与数据增强

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-04-25 DOI: 10.1109/TMLCN.2024.3393892

Xunzheng Zhang;Juan Marcelo Parra-Ullauri;Shadi Moazzeni;Xenofon Vasilakos;Reza Nejabati;Dimitra Simeonidou

{"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}

引用次数: 0

Multi-Agent Double Deep Q-Learning for Fairness in Multiple-Access Underlay Cognitive Radio Networks 多代理双深度 Q 学习促进多重接入下层认知无线电网络的公平性

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-04-18 DOI: 10.1109/TMLCN.2024.3391216

Zain Ali;Zouheir Rezki;Hamid Sadjadpour

{"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}

引用次数: 0

Online Learning to Cache and Recommend in the Next Generation Cellular Networks 通过在线学习在下一代蜂窝网络中进行缓存和推荐

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-04-17 DOI: 10.1109/TMLCN.2024.3388975

Krishnendu S Tharakan;B. N. Bharath;Vimal Bhatia

{"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}

引用次数: 0

A Link-Quality Anomaly Detection Framework for Software-Defined Wireless Mesh Networks 软件定义无线网格网络的链路质量异常检测框架

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-04-15 DOI: 10.1109/TMLCN.2024.3388973

Sotiris Skaperas;Lefteris Mamatas;Vassilis Tsaoussidis

{"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}

引用次数: 0

STTMC: A Few-Shot Spatial Temporal Transductive Modulation Classifier STTMC：少量时空传导调制分类器

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-04-11 DOI: 10.1109/TMLCN.2024.3387430

Yunhao Shi;Hua Xu;Zisen Qi;Yue Zhang;Dan Wang;Lei Jiang

{"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}

引用次数: 0

A Deep Learning Based Induced GNSS Spoof Detection Framework 基于深度学习的诱导式全球导航卫星系统欺骗检测框架

IEEE Transactions on Machine Learning in Communications and Networking Pub Date : 2024-04-10 DOI: 10.1109/TMLCN.2024.3386649

Asif Iqbal;Muhammad Naveed Aman;Biplab Sikdar

{"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}

引用次数: 0