Pub Date : 2024-06-24DOI: 10.1109/TMLCN.2024.3418756
Ulya Sabeel;Shahram Shah Heydari;Khalil El-Khatib;Khalid Elgazzar
AI-based Network Intrusion Detection Systems (NIDS) provide effective mechanisms for cybersecurity analysts to gain insights and thwart several network attacks. Although current IDS can identify known/typical attacks with high accuracy, current research shows that such systems perform poorly when facing atypical and dynamically changing (polymorphic) attacks. In this paper, we focus on improving detection capability of the IDS for atypical and polymorphic network attacks. Our system generates adversarial polymorphic attacks against the IDS to examine its performance and incrementally retrains it to strengthen its detection of new attacks, specifically for minority attack samples in the input data. The employed attack quality analysis ensures that the adversarial atypical/polymorphic attacks generated through our system resemble original network attacks. We showcase the high performance of the IDS that we have proposed by training it using the CICIDS2017 and CICIoT2023 benchmark datasets and evaluating its performance against several atypical/polymorphic attack flows. The results indicate that the proposed technique, through adaptive training, learns the pattern of dynamically changing atypical/polymorphic attacks, identifies such attacks with approximately 90% balanced accuracy for most of the cases, and surpasses various state-of-the-art detection and class balancing techniques.
{"title":"Incremental Adversarial Learning for Polymorphic Attack Detection","authors":"Ulya Sabeel;Shahram Shah Heydari;Khalil El-Khatib;Khalid Elgazzar","doi":"10.1109/TMLCN.2024.3418756","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3418756","url":null,"abstract":"AI-based Network Intrusion Detection Systems (NIDS) provide effective mechanisms for cybersecurity analysts to gain insights and thwart several network attacks. Although current IDS can identify known/typical attacks with high accuracy, current research shows that such systems perform poorly when facing atypical and dynamically changing (polymorphic) attacks. In this paper, we focus on improving detection capability of the IDS for atypical and polymorphic network attacks. Our system generates adversarial polymorphic attacks against the IDS to examine its performance and incrementally retrains it to strengthen its detection of new attacks, specifically for minority attack samples in the input data. The employed attack quality analysis ensures that the adversarial atypical/polymorphic attacks generated through our system resemble original network attacks. We showcase the high performance of the IDS that we have proposed by training it using the CICIDS2017 and CICIoT2023 benchmark datasets and evaluating its performance against several atypical/polymorphic attack flows. The results indicate that the proposed technique, through adaptive training, learns the pattern of dynamically changing atypical/polymorphic attacks, identifies such attacks with approximately 90% balanced accuracy for most of the cases, and surpasses various state-of-the-art detection and class balancing techniques.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"869-887"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10570491","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494827","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}
Pub Date : 2024-06-24DOI: 10.1109/TMLCN.2024.3418748
Guo Hao Thng;Said Mikki
In recent years, the research involving the use of machine learning in the field of communication networks have shown promising results, in particular, improving receiver sensitivity against noise and link impairment. The proposal of analog radio-over-fiber fronthaul solutions simplifies the overall base station configuration by generating wireless signals at the desired transmission frequency, directly after photodiode heterodyne detection, without requiring additional frequency upconversion components. However, analog radio-over-fiber signals is more susceptible to nonlinear distortions originating from the optical transmission system. This paper explores the use of machine learning in an analog radio-over-fiber link, improving receiver sensitivity in the presence of phase noise. The machine learning algorithm is implemented at the receiver. To evaluate the feasibility of the proposed machine learning based phase noise correction approach, software simulations were conducted to collect data needed for machine leanring algorithm training. Initial findings suggests that the proposed machine-learning-based receiver’s can perform close to conventional heterodyned-based receivers in terms of detection accuracy, exhibiting great tolerance against phase-induced noise, with a symbol error rate improvement from $10^{-2}$ to $10^{-5}$ , using a relatively simple machine learning algorithm with only 3 hidden layers consisting of fully connected feedforward neural networks.
{"title":"A Machine Learning Aided Reference-Tone-Based Phase Noise Correction Framework for Fiber-Wireless Systems","authors":"Guo Hao Thng;Said Mikki","doi":"10.1109/TMLCN.2024.3418748","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3418748","url":null,"abstract":"In recent years, the research involving the use of machine learning in the field of communication networks have shown promising results, in particular, improving receiver sensitivity against noise and link impairment. The proposal of analog radio-over-fiber fronthaul solutions simplifies the overall base station configuration by generating wireless signals at the desired transmission frequency, directly after photodiode heterodyne detection, without requiring additional frequency upconversion components. However, analog radio-over-fiber signals is more susceptible to nonlinear distortions originating from the optical transmission system. This paper explores the use of machine learning in an analog radio-over-fiber link, improving receiver sensitivity in the presence of phase noise. The machine learning algorithm is implemented at the receiver. To evaluate the feasibility of the proposed machine learning based phase noise correction approach, software simulations were conducted to collect data needed for machine leanring algorithm training. Initial findings suggests that the proposed machine-learning-based receiver’s can perform close to conventional heterodyned-based receivers in terms of detection accuracy, exhibiting great tolerance against phase-induced noise, with a symbol error rate improvement from \u0000<inline-formula> <tex-math>$10^{-2}$ </tex-math></inline-formula>\u0000 to \u0000<inline-formula> <tex-math>$10^{-5}$ </tex-math></inline-formula>\u0000, using a relatively simple machine learning algorithm with only 3 hidden layers consisting of fully connected feedforward neural networks.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"888-903"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10570478","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494826","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}
Pub Date : 2024-06-21DOI: 10.1109/TMLCN.2024.3417806
Da Huang;Akram Al-Hourani
The proliferation of the Internet of Things (IoT) has created significant opportunities for future telecommunications. A popular category of IoT devices is oriented toward low-cost and low-power applications. However, certain aspects of such category, including the authentication process, remain inadequately investigated against cyber vulnerabilities. This is caused by the inherent trade-off between device complexity and security rigor. In this work, we propose an authentication method based on radio frequency fingerprinting (RFF) using deep learning. This method can be implemented on the base station side without increasing the complexity of the IoT devices. Specifically, we propose four representation modalities based on continuous wavelet transform (CWT) to exploit tempo-spectral radio fingerprints. Accordingly, we utilize the generative adversarial network (GAN) and convolutional neural network (CNN) for spoof detection and authentication. For empirical validation, we consider the widely popular LoRa system with a focus on the preamble of the radio frame. The presented experimental test involves 20 off-the-shelf LoRa modules to demonstrate the feasibility of the proposed approach, showing reliable detection results of spoofing devices and high-level accuracy in authentication of 92.4%.
{"title":"Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods","authors":"Da Huang;Akram Al-Hourani","doi":"10.1109/TMLCN.2024.3417806","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3417806","url":null,"abstract":"The proliferation of the Internet of Things (IoT) has created significant opportunities for future telecommunications. A popular category of IoT devices is oriented toward low-cost and low-power applications. However, certain aspects of such category, including the authentication process, remain inadequately investigated against cyber vulnerabilities. This is caused by the inherent trade-off between device complexity and security rigor. In this work, we propose an authentication method based on radio frequency fingerprinting (RFF) using deep learning. This method can be implemented on the base station side without increasing the complexity of the IoT devices. Specifically, we propose four representation modalities based on continuous wavelet transform (CWT) to exploit tempo-spectral radio fingerprints. Accordingly, we utilize the generative adversarial network (GAN) and convolutional neural network (CNN) for spoof detection and authentication. For empirical validation, we consider the widely popular LoRa system with a focus on the preamble of the radio frame. The presented experimental test involves 20 off-the-shelf LoRa modules to demonstrate the feasibility of the proposed approach, showing reliable detection results of spoofing devices and high-level accuracy in authentication of 92.4%.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"841-854"},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10568158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141474855","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}
Pub Date : 2024-06-21DOI: 10.1109/TMLCN.2024.3417889
Nageswara S. V. Rao;Chris Y. T. Ma;Fei He
Data transfer infrastructures composed of Data Transfer Nodes (DTN) are critical to meeting distributed computing and storage demands of clouds, data repositories, and complexes of supercomputers and instruments. The infrastructure’s throughput profile, estimated as a function of the connection round trip time using Machine Learning (ML) methods, is an indicator of its operational state, and has been utilized for monitoring, diagnosis and optimization purposes. We show that the inherent statistical variations and precision of throughput profiles estimated by ML methods can be exploited for unauthorized use of DTNs’ computing and network capacity. We present a game theoretic formulation that captures the cost-benefit trade-offs between an attacker that attempts to hide under the profile’s statistical variations and a provider that attempts to balance compromise detection with the cost of throughput measurements. The Nash equilibrium conditions adapted to this game provide qualitative insights and bounds for the success probabilities of the attacker and provider, by utilizing the generalization equation of ML-estimate. We present experimental results that illustrate this game wherein a significant portion of DTN computing capacity is compromised without being detected by an attacker that exploits the ML estimate properties.
由数据传输节点（DTN）组成的数据传输基础设施对于满足云、数据存储库以及超级计算机和仪器群的分布式计算和存储需求至关重要。使用机器学习（ML）方法估算的基础设施吞吐量曲线是连接往返时间的函数，是其运行状态的指标，已被用于监控、诊断和优化目的。我们的研究表明，可以利用 ML 方法估算的吞吐量曲线的固有统计变化和精度，在未经授权的情况下使用 DTN 的计算和网络容量。我们提出了一个博弈论公式，它捕捉到了试图隐藏在吞吐量曲线统计变化下的攻击者与试图平衡破坏检测与吞吐量测量成本的提供者之间的成本效益权衡。通过利用 ML-estimate 的广义方程，适应该博弈的纳什均衡条件为攻击者和提供者的成功概率提供了定性的见解和界限。我们展示的实验结果说明了这种博弈，在这种博弈中，利用 ML 估计特性的攻击者可以在不被检测到的情况下破坏大部分 DTN 计算能力。
{"title":"Game Strategies for Data Transfer Infrastructures Against ML-Profile Exploits","authors":"Nageswara S. V. Rao;Chris Y. T. Ma;Fei He","doi":"10.1109/TMLCN.2024.3417889","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3417889","url":null,"abstract":"Data transfer infrastructures composed of Data Transfer Nodes (DTN) are critical to meeting distributed computing and storage demands of clouds, data repositories, and complexes of supercomputers and instruments. The infrastructure’s throughput profile, estimated as a function of the connection round trip time using Machine Learning (ML) methods, is an indicator of its operational state, and has been utilized for monitoring, diagnosis and optimization purposes. We show that the inherent statistical variations and precision of throughput profiles estimated by ML methods can be exploited for unauthorized use of DTNs’ computing and network capacity. We present a game theoretic formulation that captures the cost-benefit trade-offs between an attacker that attempts to hide under the profile’s statistical variations and a provider that attempts to balance compromise detection with the cost of throughput measurements. The Nash equilibrium conditions adapted to this game provide qualitative insights and bounds for the success probabilities of the attacker and provider, by utilizing the generalization equation of ML-estimate. We present experimental results that illustrate this game wherein a significant portion of DTN computing capacity is compromised without being detected by an attacker that exploits the ML estimate properties.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"925-938"},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10568232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141539198","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}
Pub Date : 2024-06-13DOI: 10.1109/TMLCN.2024.3414296
Zhikai Liu;Haifeng Luo;Tharmalingam Ratnarajah
Digital self-interference cancellation (DSIC) has become a pivotal strategy for implementing in-band full-duplex (IBFD) radios to overcome the hurdles posed by residual self-interference that persist after propagation and analog domain cancellation. This work proposes a novel reservoir computing-based DSIC (RC-DSIC) technique and compares it with traditional polynomial-based (PL-DSIC) and various existing neural network-based (NN-DSIC) approaches. We begin by delineating the structure of the RC and exploring its capability to address the DSIC task, highlighting its potential advantages over current methodologies. Subsequently, we examine the computational complexity of these approaches and undertake extensive simulations to compare the proposed RC-DSIC approach against PL-DSIC and existing NN-DSIC schemes. Our results reveal that the RC-DSIC scheme attains 99.84% of the performance offered by PL-based DSIC algorithms while requiring only 1.51% of the computational demand. Compared to many existing NN-DSIC schemes, the RC-DSIC method achieves at least 99.73% of its performance with no more than 36.61% of the computational demand. This performance justifies the viability of RC-DSIC as an effective and efficient solution for DSIC in IBFD, striking it is a better implementation method in terms of computational simplicity.
{"title":"Reservoir Computing-Based Digital Self-Interference Cancellation for In-Band Full-Duplex Radios","authors":"Zhikai Liu;Haifeng Luo;Tharmalingam Ratnarajah","doi":"10.1109/TMLCN.2024.3414296","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3414296","url":null,"abstract":"Digital self-interference cancellation (DSIC) has become a pivotal strategy for implementing in-band full-duplex (IBFD) radios to overcome the hurdles posed by residual self-interference that persist after propagation and analog domain cancellation. This work proposes a novel reservoir computing-based DSIC (RC-DSIC) technique and compares it with traditional polynomial-based (PL-DSIC) and various existing neural network-based (NN-DSIC) approaches. We begin by delineating the structure of the RC and exploring its capability to address the DSIC task, highlighting its potential advantages over current methodologies. Subsequently, we examine the computational complexity of these approaches and undertake extensive simulations to compare the proposed RC-DSIC approach against PL-DSIC and existing NN-DSIC schemes. Our results reveal that the RC-DSIC scheme attains 99.84% of the performance offered by PL-based DSIC algorithms while requiring only 1.51% of the computational demand. Compared to many existing NN-DSIC schemes, the RC-DSIC method achieves at least 99.73% of its performance with no more than 36.61% of the computational demand. This performance justifies the viability of RC-DSIC as an effective and efficient solution for DSIC in IBFD, striking it is a better implementation method in terms of computational simplicity.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"855-868"},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10556632","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141474856","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}
Pub Date : 2024-06-12DOI: 10.1109/TMLCN.2024.3413669
Shayan Zargari;Chintha Tellambura;Amine Maaref;Geoffrey Ye Li
In ambient backscatter communication (AmBC), battery-free devices (tags) harvest energy from ambient radio frequency (RF) signals and communicate with readers. Although reliable channel estimation (CE) is critical, classical pilot-based estimators tend to perform poorly. To address this challenge, we treat CE as a denoising problem using conditional generative adversarial networks (CGANs). A three-dimensional (3D) denoising block leverages spatial and temporal characteristics of pilot signals, considering both real and imaginary components of channel matrices. The proposed CGAN estimator is extensively evaluated against traditional estimators like minimum mean-squared error (MMSE), least squares (LS), convolutional neural network (CNN), CNN-based deep residual learning denoiser (CRLD), and blind estimation. Simulation results show 82% gain of the proposed estimator over CRLD and MMSE estimators at an SNR of 5 dB. Moreover, it has advanced learning capabilities and accurately replicates complex channel characteristics.
{"title":"Deep Conditional Generative Adversarial Networks for Efficient Channel Estimation in AmBC Systems","authors":"Shayan Zargari;Chintha Tellambura;Amine Maaref;Geoffrey Ye Li","doi":"10.1109/TMLCN.2024.3413669","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3413669","url":null,"abstract":"In ambient backscatter communication (AmBC), battery-free devices (tags) harvest energy from ambient radio frequency (RF) signals and communicate with readers. Although reliable channel estimation (CE) is critical, classical pilot-based estimators tend to perform poorly. To address this challenge, we treat CE as a denoising problem using conditional generative adversarial networks (CGANs). A three-dimensional (3D) denoising block leverages spatial and temporal characteristics of pilot signals, considering both real and imaginary components of channel matrices. The proposed CGAN estimator is extensively evaluated against traditional estimators like minimum mean-squared error (MMSE), least squares (LS), convolutional neural network (CNN), CNN-based deep residual learning denoiser (CRLD), and blind estimation. Simulation results show 82% gain of the proposed estimator over CRLD and MMSE estimators at an SNR of 5 dB. Moreover, it has advanced learning capabilities and accurately replicates complex channel characteristics.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"805-822"},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10555303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141453439","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}
Pub Date : 2024-06-11DOI: 10.1109/TMLCN.2024.3412054
Kosuke Tamura;Shun Kojima;Phuc V. Trinh;Shinya Sugiura;Chang-Jun Ahn
This paper proposes a novel joint signal-to-noise ratio (SNR) and Rician K-factor estimation scheme based on supervised multimodal learning. In the case of using machine learning to estimate the communication environment, achieving high accuracy requires a sufficient amount of training data. To solve this problem, we introduce a multimodal convolutional neural network (CNN) structure using different waveform formats. The proposed scheme obtains “feature diversity” by increasing the modalities from the same received signal, such as sequence data and spectrogram image. Especially with a limited dataset, training convergence is accelerated since different features can be extracted from each modality. Simulations demonstrate that the presented scheme achieves superior performance compared to conventional estimation methods.
本文提出了一种基于有监督多模态学习的新型信噪比（SNR）和里克里亚 K 因子联合估计方案。在使用机器学习估计通信环境的情况下，要达到高精度需要足够多的训练数据。为了解决这个问题，我们引入了一种使用不同波形格式的多模态卷积神经网络（CNN）结构。所提出的方案通过增加同一接收信号的模式（如序列数据和频谱图图像）来获得 "特征多样性"。特别是在数据集有限的情况下，由于可以从每种模态中提取不同的特征，因此可以加快训练收敛速度。模拟结果表明，与传统的估计方法相比，所提出的方案性能更优。
{"title":"Joint SNR and Rician K-Factor Estimation Using Multimodal Network Over Mobile Fading Channels","authors":"Kosuke Tamura;Shun Kojima;Phuc V. Trinh;Shinya Sugiura;Chang-Jun Ahn","doi":"10.1109/TMLCN.2024.3412054","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3412054","url":null,"abstract":"This paper proposes a novel joint signal-to-noise ratio (SNR) and Rician K-factor estimation scheme based on supervised multimodal learning. In the case of using machine learning to estimate the communication environment, achieving high accuracy requires a sufficient amount of training data. To solve this problem, we introduce a multimodal convolutional neural network (CNN) structure using different waveform formats. The proposed scheme obtains “feature diversity” by increasing the modalities from the same received signal, such as sequence data and spectrogram image. Especially with a limited dataset, training convergence is accelerated since different features can be extracted from each modality. Simulations demonstrate that the presented scheme achieves superior performance compared to conventional estimation methods.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"766-779"},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430007","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}
Pub Date : 2024-06-05DOI: 10.1109/TMLCN.2024.3410208
Halit Bugra Tulay;Can Emre Koksal
With the growing adoption of vehicular networks, ensuring the security of these networks is becoming increasingly crucial. However, the broadcast nature of communication in these networks creates numerous privacy and security concerns. In particular, the Sybil attack, where attackers can use multiple identities to disseminate false messages, cause service delays, or gain control of the network, poses a significant threat. To combat this attack, we propose a novel approach utilizing the channel state information (CSI) of vehicles. Our approach leverages the distinct spatio-temporal variations of CSI samples obtained in vehicular communication signals to detect these attacks. We conduct extensive real-world experiments using vehicle-to-everything (V2X) data, gathered from dedicated short-range communications (DSRC) in vehicular networks. Our results demonstrate a high detection rate of over 98% in the real-world experiments, showcasing the practicality and effectiveness of our method in realistic vehicular scenarios. Furthermore, we rigorously test our approach through advanced ray-tracing simulations in urban environments, which demonstrates high efficacy even in complex scenarios involving various vehicles. This makes our approach a valuable, hardware-independent solution for the V2X technologies at major intersections.
{"title":"Sybil Attack Detection Based on Signal Clustering in Vehicular Networks","authors":"Halit Bugra Tulay;Can Emre Koksal","doi":"10.1109/TMLCN.2024.3410208","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3410208","url":null,"abstract":"With the growing adoption of vehicular networks, ensuring the security of these networks is becoming increasingly crucial. However, the broadcast nature of communication in these networks creates numerous privacy and security concerns. In particular, the Sybil attack, where attackers can use multiple identities to disseminate false messages, cause service delays, or gain control of the network, poses a significant threat. To combat this attack, we propose a novel approach utilizing the channel state information (CSI) of vehicles. Our approach leverages the distinct spatio-temporal variations of CSI samples obtained in vehicular communication signals to detect these attacks. We conduct extensive real-world experiments using vehicle-to-everything (V2X) data, gathered from dedicated short-range communications (DSRC) in vehicular networks. Our results demonstrate a high detection rate of over 98% in the real-world experiments, showcasing the practicality and effectiveness of our method in realistic vehicular scenarios. Furthermore, we rigorously test our approach through advanced ray-tracing simulations in urban environments, which demonstrates high efficacy even in complex scenarios involving various vehicles. This makes our approach a valuable, hardware-independent solution for the V2X technologies at major intersections.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"753-765"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10550012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319672","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}
Pub Date : 2024-06-05DOI: 10.1109/TMLCN.2024.3410211
Mohammed Saif;Md. Zoheb Hassan;Md. Jahangir Hossain
It is anticipated that aerial-terrestrial integrated networks incorporating unmanned aerial vehicles (UAVs) mounted relays will offer improved coverage and connectivity in the beyond 5G era. Meanwhile, federated learning (FL) is a promising distributed machine learning technique for building inference models over wireless networks due to its ability to maintain user privacy and reduce communication overhead. However, off-the-shelf FL models aggregate global parameters at a central parameter server (CPS), increasing energy consumption and latency, as well as inefficiently utilizing radio resource blocks (RRBs) for distributed user devices (UDs). This paper presents a resource-efficient and decentralized FL framework called FedMoD (federated learning with model dissemination), for millimeter-wave (mmWave) aerial-terrestrial integrated networks with the following two unique characteristics. Firstly, FedMoD incorporates a novel decentralized model dissemination scheme that uses UAVs as local model aggregators through UAV-to-UAV and device-to-device (D2D) communications. As a result, FedMoD 1) increases the number of participant UDs in developing the FL model; and 2) achieves global model aggregation without involving CPS. Secondly, FedMoD reduces FL’s energy consumption using radio resource management (RRM) under the constraints of over-the-air learning latency. To achieve this, by leveraging graph theory, FedMoD optimizes the scheduling of line-of-sight (LOS) UDs to suitable UAVs and RRBs over mmWave links and non-LOS UDs to available LOS UDs via overlay D2D communications. Extensive simulations reveal that FedMoD, despite being decentralized, offers the same convergence performance to the conventional centralized FL frameworks.
{"title":"Decentralized Aggregation for Energy-Efficient Federated Learning in mmWave Aerial-Terrestrial Integrated Networks","authors":"Mohammed Saif;Md. Zoheb Hassan;Md. Jahangir Hossain","doi":"10.1109/TMLCN.2024.3410211","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3410211","url":null,"abstract":"It is anticipated that aerial-terrestrial integrated networks incorporating unmanned aerial vehicles (UAVs) mounted relays will offer improved coverage and connectivity in the beyond 5G era. Meanwhile, federated learning (FL) is a promising distributed machine learning technique for building inference models over wireless networks due to its ability to maintain user privacy and reduce communication overhead. However, off-the-shelf FL models aggregate global parameters at a central parameter server (CPS), increasing energy consumption and latency, as well as inefficiently utilizing radio resource blocks (RRBs) for distributed user devices (UDs). This paper presents a resource-efficient and decentralized FL framework called FedMoD (federated learning with model dissemination), for millimeter-wave (mmWave) aerial-terrestrial integrated networks with the following two unique characteristics. Firstly, FedMoD incorporates a novel decentralized model dissemination scheme that uses UAVs as local model aggregators through UAV-to-UAV and device-to-device (D2D) communications. As a result, FedMoD 1) increases the number of participant UDs in developing the FL model; and 2) achieves global model aggregation without involving CPS. Secondly, FedMoD reduces FL’s energy consumption using radio resource management (RRM) under the constraints of over-the-air learning latency. To achieve this, by leveraging graph theory, FedMoD optimizes the scheduling of line-of-sight (LOS) UDs to suitable UAVs and RRBs over mmWave links and non-LOS UDs to available LOS UDs via overlay D2D communications. Extensive simulations reveal that FedMoD, despite being decentralized, offers the same convergence performance to the conventional centralized FL frameworks.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1283-1304"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10550002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230893","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}
Pub Date : 2024-06-04DOI: 10.1109/TMLCN.2024.3409205
Eric Samikwa;Antonio Di Maio;Torsten Braun
Federated Learning (FL) in edge Internet of Things (IoT) environments is challenging due to the heterogeneous nature of the learning environment, mainly embodied in two aspects. Firstly, the statistically heterogeneous data, usually non-independent identically distributed (non-IID), from geographically distributed clients can deteriorate the FL training accuracy. Secondly, the heterogeneous computing and communication resources in IoT devices often result in unstable training processes that slow down the training of a global model and affect energy consumption. Most existing solutions address only the unilateral side of the heterogeneity issue but neglect the joint problem of resources and data heterogeneity for the resource-constrained IoT. In this article, we propose Dynamic Federated split Learning (DFL) to address the joint problem of data and resource heterogeneity for distributed training in IoT. DFL enhances training efficiency in heterogeneous dynamic IoT through resource-aware split computing of deep neural networks and dynamic clustering of training participants based on the similarity of their sub-model layers. We evaluate DFL on a real testbed comprising heterogeneous IoT devices using two widely-adopted datasets, in various non-IID settings. Results show that DFL improves training performance in terms of training time by up to 48%, accuracy by up to 32%, and energy consumption by up to 62.8% compared to classic FL and Federated Split Learning in scenarios with both data and resource heterogeneity.
{"title":"DFL: Dynamic Federated Split Learning in Heterogeneous IoT","authors":"Eric Samikwa;Antonio Di Maio;Torsten Braun","doi":"10.1109/TMLCN.2024.3409205","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3409205","url":null,"abstract":"Federated Learning (FL) in edge Internet of Things (IoT) environments is challenging due to the heterogeneous nature of the learning environment, mainly embodied in two aspects. Firstly, the statistically heterogeneous data, usually non-independent identically distributed (non-IID), from geographically distributed clients can deteriorate the FL training accuracy. Secondly, the heterogeneous computing and communication resources in IoT devices often result in unstable training processes that slow down the training of a global model and affect energy consumption. Most existing solutions address only the unilateral side of the heterogeneity issue but neglect the joint problem of resources and data heterogeneity for the resource-constrained IoT. In this article, we propose Dynamic Federated split Learning (DFL) to address the joint problem of data and resource heterogeneity for distributed training in IoT. DFL enhances training efficiency in heterogeneous dynamic IoT through resource-aware split computing of deep neural networks and dynamic clustering of training participants based on the similarity of their sub-model layers. We evaluate DFL on a real testbed comprising heterogeneous IoT devices using two widely-adopted datasets, in various non-IID settings. Results show that DFL improves training performance in terms of training time by up to 48%, accuracy by up to 32%, and energy consumption by up to 62.8% compared to classic FL and Federated Split Learning in scenarios with both data and resource heterogeneity.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"733-752"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547401","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319638","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}
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