Pub Date : 2024-10-04DOI: 10.1109/LNET.2024.3474253
Rashika Raina;David E. Simmons;Nidhi Simmons;Michel Daoud Yacoub
This letter advances on the outage probability (OP) performance of a machine learning (ML)-assisted single-user multi-resource system. We focus on OP optimality and the trade-off between outage improvement and the mean number of resources scanned until a suitable resource is captured. We first present expressions for the OP of this system, complemented by an outage loss function (OLF) for its minimization. We then derive: (i) the necessary and sufficient properties of an optimal model (OpM) and (ii) expressions for the average number of resources scanned by both OpM and non-OpMs. Here, non-OpMs refer to those trained with the OLF and binary cross entropy (BCE) loss functions. We establish that optimal performance requires a channel that exhibits no time decorrelation properties. For very high decorrelation values, we find that models trained using the OLF and BCE perform similarly. For intermediate (practical) decorrelation values, OLF outperforms BCE, and both approach the OpM as decorrelation tends to zero. Our analysis further reveals that, to be able to capture a suitable resource, models trained with the OLF scan a slightly higher number of resources than the OpM and those trained with BCE. This increase in the mean number of scanned resources is offset by a significant enhancement in the OP as compared to BCE.
{"title":"Optimal Classifier for an ML-Assisted Resource Allocation in Wireless Communications","authors":"Rashika Raina;David E. Simmons;Nidhi Simmons;Michel Daoud Yacoub","doi":"10.1109/LNET.2024.3474253","DOIUrl":"https://doi.org/10.1109/LNET.2024.3474253","url":null,"abstract":"This letter advances on the outage probability (OP) performance of a machine learning (ML)-assisted single-user multi-resource system. We focus on OP optimality and the trade-off between outage improvement and the mean number of resources scanned until a suitable resource is captured. We first present expressions for the OP of this system, complemented by an outage loss function (OLF) for its minimization. We then derive: (i) the necessary and sufficient properties of an optimal model (OpM) and (ii) expressions for the average number of resources scanned by both OpM and non-OpMs. Here, non-OpMs refer to those trained with the OLF and binary cross entropy (BCE) loss functions. We establish that optimal performance requires a channel that exhibits no time decorrelation properties. For very high decorrelation values, we find that models trained using the OLF and BCE perform similarly. For intermediate (practical) decorrelation values, OLF outperforms BCE, and both approach the OpM as decorrelation tends to zero. Our analysis further reveals that, to be able to capture a suitable resource, models trained with the OLF scan a slightly higher number of resources than the OpM and those trained with BCE. This increase in the mean number of scanned resources is offset by a significant enhancement in the OP as compared to BCE.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 3","pages":"158-162"},"PeriodicalIF":0.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517769","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 : 2024-10-03DOI: 10.1109/LNET.2024.3472034
Weibiao Tian;Ye Li;Jinwei Zhao;Sheng Wu;Jianping Pan
System-level performance evaluation over satellite networks often requires a simulated or emulated environment for reproducibility and low cost. However, the existing tools may not meet the needs for scenarios such as the low-earth orbit (LEO) satellite networks. To address the problem, this letter proposes and implements a trace-driven emulation method based on Linux’s eBPF technology. Building a Starlink traces collection system, we demonstrate that the method can effectively and efficiently emulate the connection conditions, and therefore provides a means for evaluating applications on local hosts.
卫星网络的系统级性能评估通常需要模拟或仿真环境，以实现可重复性和低成本。然而，现有工具可能无法满足低地轨道（LEO）卫星网络等场景的需求。为解决这一问题，本文基于 Linux 的 eBPF 技术，提出并实现了一种跟踪驱动的仿真方法。通过建立 Starlink 跟踪收集系统，我们证明了该方法可以有效地模拟连接条件，从而为评估本地主机上的应用程序提供了一种方法。
{"title":"An eBPF-Based Trace-Driven Emulation Method for Satellite Networks","authors":"Weibiao Tian;Ye Li;Jinwei Zhao;Sheng Wu;Jianping Pan","doi":"10.1109/LNET.2024.3472034","DOIUrl":"https://doi.org/10.1109/LNET.2024.3472034","url":null,"abstract":"System-level performance evaluation over satellite networks often requires a simulated or emulated environment for reproducibility and low cost. However, the existing tools may not meet the needs for scenarios such as the low-earth orbit (LEO) satellite networks. To address the problem, this letter proposes and implements a trace-driven emulation method based on Linux’s eBPF technology. Building a Starlink traces collection system, we demonstrate that the method can effectively and efficiently emulate the connection conditions, and therefore provides a means for evaluating applications on local hosts.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 3","pages":"188-192"},"PeriodicalIF":0.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1109/LNET.2024.3467031
Anastasios C. Politis;Constantinos S. Hilas
This letter evaluates the performance of the slotted Aloha protocol defined by the European Telecommunication Standard Institute (ETSI) SmartBAN specification, under saturation conditions. For this purpose, we develop a two-dimensional discrete time Markov chain (DTMC) to model the operational details of the protocol and assess its performance in terms of saturation throughput and average end-to-end delay. The accuracy of the proposed model is validated by means of simulation which reveals a very good match among theoretical and simulation results. The model can be used for protocol performance prediction and optimization purposes.
{"title":"Throughput and Delay Performance of Slotted Aloha in SmartBANs Under Saturation Conditions","authors":"Anastasios C. Politis;Constantinos S. Hilas","doi":"10.1109/LNET.2024.3467031","DOIUrl":"https://doi.org/10.1109/LNET.2024.3467031","url":null,"abstract":"This letter evaluates the performance of the slotted Aloha protocol defined by the European Telecommunication Standard Institute (ETSI) SmartBAN specification, under saturation conditions. For this purpose, we develop a two-dimensional discrete time Markov chain (DTMC) to model the operational details of the protocol and assess its performance in terms of saturation throughput and average end-to-end delay. The accuracy of the proposed model is validated by means of simulation which reveals a very good match among theoretical and simulation results. The model can be used for protocol performance prediction and optimization purposes.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 3","pages":"168-172"},"PeriodicalIF":0.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1109/LNET.2024.3465516
Ayaka Oki;Yukio Ogawa;Kaoru Ota;Mianxiong Dong
We apply federated learning (FL) to a distributed intrusion detection system (IDS), in which we deploy numerous detection servers on the edge of a network. FL can mitigate the impact of decreased training data in each server and exhibit almost the same detection rate as that of the non-distributed IDS for all attack classes. We verify the effect of FL using explainable artificial intelligence (XAI); this effect is demonstrated by the distance between the feature set of each attack class in the distributed IDS and that in the non-distributed IDS. The distance increases for independent learning and decreases for FL.
{"title":"Evaluation of Applying Federated Learning to Distributed Intrusion Detection Systems Through Explainable AI","authors":"Ayaka Oki;Yukio Ogawa;Kaoru Ota;Mianxiong Dong","doi":"10.1109/LNET.2024.3465516","DOIUrl":"https://doi.org/10.1109/LNET.2024.3465516","url":null,"abstract":"We apply federated learning (FL) to a distributed intrusion detection system (IDS), in which we deploy numerous detection servers on the edge of a network. FL can mitigate the impact of decreased training data in each server and exhibit almost the same detection rate as that of the non-distributed IDS for all attack classes. We verify the effect of FL using explainable artificial intelligence (XAI); this effect is demonstrated by the distance between the feature set of each attack class in the distributed IDS and that in the non-distributed IDS. The distance increases for independent learning and decreases for FL.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 3","pages":"198-202"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1109/LNET.2024.3444495
Omid Abbasi;Georges Kaddoum
Satellite communication systems encounter channel aging issues due to the substantial distance that separates users and satellites. In such systems, the estimated channel state at a given time slot reflects the channel state from several time slots in the past. This letter proposes a long short-term memory (LSTM)-based architecture for channel prediction to mitigate the channel aging problem. The proposed scheme predicts the next time slot’s channel based on a block of estimated channel state information (CSI) from previous time slots. We consider the effect of channel aging in the training phase so that channel prediction in the testing phase is performed based on available data. We demonstrated through simulation experiments on new radio non-terrestrial network tapped delay line (NR NTN TDL) channel models, that our proposed scheme can effectively mitigate channel aging, and that it performs better than outdated channels. The proposed scheme improves the reliability and efficiency of satellite communication systems with long propagation delays.
{"title":"Channel Aging-Aware LSTM-Based Channel Prediction for Satellite Communications","authors":"Omid Abbasi;Georges Kaddoum","doi":"10.1109/LNET.2024.3444495","DOIUrl":"https://doi.org/10.1109/LNET.2024.3444495","url":null,"abstract":"Satellite communication systems encounter channel aging issues due to the substantial distance that separates users and satellites. In such systems, the estimated channel state at a given time slot reflects the channel state from several time slots in the past. This letter proposes a long short-term memory (LSTM)-based architecture for channel prediction to mitigate the channel aging problem. The proposed scheme predicts the next time slot’s channel based on a block of estimated channel state information (CSI) from previous time slots. We consider the effect of channel aging in the training phase so that channel prediction in the testing phase is performed based on available data. We demonstrated through simulation experiments on new radio non-terrestrial network tapped delay line (NR NTN TDL) channel models, that our proposed scheme can effectively mitigate channel aging, and that it performs better than outdated channels. The proposed scheme improves the reliability and efficiency of satellite communication systems with long propagation delays.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 3","pages":"183-187"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1109/LNET.2024.3442833
Mohamed elShehaby;Aditya Kotha;Ashraf Matrawy
Adversarial training enhances the robustness of Machine Learning (ML) models against adversarial attacks. However, obtaining labeled training and adversarial training data in network/cybersecurity domains is challenging and costly. Therefore, this letter introduces Adaptive Continuous Adversarial Training (ACAT), a novel method that integrates adversarial training samples into the model during continuous learning sessions using real-world detected adversarial data. Experimental results with a SPAM detection dataset demonstrate that ACAT reduces the time required for adversarial sample detection compared to traditional processes (up to 4 times faster when dealing with 10,000 samples). Moreover, the accuracy of the under-attack ML-based SPAM filter increased from 69% to over 88% after just three retraining sessions.
{"title":"Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance Machine Learning Robustness","authors":"Mohamed elShehaby;Aditya Kotha;Ashraf Matrawy","doi":"10.1109/LNET.2024.3442833","DOIUrl":"https://doi.org/10.1109/LNET.2024.3442833","url":null,"abstract":"Adversarial training enhances the robustness of Machine Learning (ML) models against adversarial attacks. However, obtaining labeled training and adversarial training data in network/cybersecurity domains is challenging and costly. Therefore, this letter introduces Adaptive Continuous Adversarial Training (ACAT), a novel method that integrates adversarial training samples into the model during continuous learning sessions using real-world detected adversarial data. Experimental results with a SPAM detection dataset demonstrate that ACAT reduces the time required for adversarial sample detection compared to traditional processes (up to 4 times faster when dealing with 10,000 samples). Moreover, the accuracy of the under-attack ML-based SPAM filter increased from 69% to over 88% after just three retraining sessions.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 3","pages":"208-212"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1109/LNET.2024.3435723
Yanzhuo Jiang;Xueman Wang;Yingxu Lai;Yipeng Wang
Anomalies in packet length sequences caused by network topology structure and congestion greatly impact the performance of early network traffic classification. Additionally, insufficient differentiation of packet length sequences using a small number of packets also affects the performance. In this letter, we propose SePeric, a packet sequence permutation-aware approach to robust network traffic classification. By exploring the correlations within packet length sequences and adjusting them to eliminate the effects of anomalous sequence orders, as well as extracting additional features from the byte sequence of the first packet to supplement the insufficient differentiation in packet length sequences.
{"title":"A Packet Sequence Permutation-Aware Approach to Robust Network Traffic Classification","authors":"Yanzhuo Jiang;Xueman Wang;Yingxu Lai;Yipeng Wang","doi":"10.1109/LNET.2024.3435723","DOIUrl":"https://doi.org/10.1109/LNET.2024.3435723","url":null,"abstract":"Anomalies in packet length sequences caused by network topology structure and congestion greatly impact the performance of early network traffic classification. Additionally, insufficient differentiation of packet length sequences using a small number of packets also affects the performance. In this letter, we propose SePeric, a packet sequence permutation-aware approach to robust network traffic classification. By exploring the correlations within packet length sequences and adjusting them to eliminate the effects of anomalous sequence orders, as well as extracting additional features from the byte sequence of the first packet to supplement the insufficient differentiation in packet length sequences.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 3","pages":"203-207"},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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