Radio Frequency Traffic Classification Over WLAN

Kornycky, Joe; Abdul-Hameed, O; Kondoz, A.M.; Barber, B.C.

doi:10.1109/tnet.2016.2562259

Cited by 33 publications

(18 citation statements)

References 35 publications

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“…Yingqiu et al [70] K-Means Data clustering Applied K-means clustering algorithm to produce an overall 90% accuracy in Internet traffic classification in a completely unsupervised manner. Kornycky et al [169] GMM Data Clustering GMM with universal background model is used for encrypted WLAN traffic classification. Liu et al [170] GMM Data Clustering GMM and Kerner's traffic theory based ML model is used to evaluate real-time Internet traffic performance.…”

Section: A Internet Traffic Classificationmentioning

confidence: 99%

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

et al. 2019

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While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

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Section: A Internet Traffic Classificationmentioning

confidence: 99%

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

et al. 2019

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“…This scheme can be incorporated with the kernel density estimation theory, called naive Bayes kernel (NBK), to improve its classification accuracy to over 95%. The decision tree-based scheme in [17] and the Bayesian neural network (BNN) schemes in [18] and [19] consist of two processes: 1) a training process for tree or neural network construction, and 2) a testing process for classification decision. Such approaches have low time complexity in the testing process, satisfying good classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

A user application-based access point selection algorithm for dense WLANs

Kim

Lee

et al. 2019

PLoS ONE

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The current commercial access point (AP) selection schemes are mostly based on received signal strength, but perform poorly in many situations. To address this problem, a number of alternative schemes collect and analyze the actual load of every candidate AP. However, these schemes may incur significant latency and signaling overhead in dense wireless local area networks (WLANs). To mitigate such overhead, we propose a user application-based AP selection scheme that considers historical information about AP performance. Without inducing any signaling activity, our scheme monitors the amount of network traffic used by applications and estimates the achievable throughput of APs. Our scheme employs the characteristics of application traffic with the intent of accurately predicting AP performance. Using a measurement study in dense WLAN environments, we show that our scheme achieves higher throughput and lower association latency than those of existing schemes in places highly accessible to the user.

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“…There are many approaches and methodologies for traffic classification proposed in literature [5]. Such methodologies can be grouped into three main categories [6]. Port-based classification is used when the protocols are assigned to wellknown transport-layer port (i.e., TCP, HTTP).…”

Section: Introductionmentioning

confidence: 99%

“…While traffic classification in wired networks have been extensively investigated, very few works address the problem in wireless systems, despite the emergence of CR technology makes this aspect rather important [6]. This work proposes a ML approach for traffic classification in wireless networks using low-cost radio-frequency (RF) sensors.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for User Traffic Classification in Wireless Systems

Testi

Favarelli

Giorgetti

2018

2018 26th European Signal Processing Conference (EUSIPCO)

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The ability to answer all important questions about the radio-frequency (RF) scene is essential for cognitive radios (CRs) to be effective. In this paper, we propose a RF-based automatic traffic recognizer that, observing the radio spectrum emitted by a communication link and exploiting machine learning (ML) techniques, is able to distinguish between two types of data streams. Numerical results based on real waveforms collected by a RF sensor, demonstrate that over-the-air user traffic classification is possible with an accuracy of 97% at high signal-to-noise ratios (SNRs). Moreover, we show that using a neural network (NN) very good classification performance can be achieved also at low SNRs (around 2 dB). Finally, the impact of the observed RF bandwidth and the acquisition time window on the classification accuracy are analyzed in detail.

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Radio Frequency Traffic Classification Over WLAN

Cited by 33 publications

References 35 publications

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

A user application-based access point selection algorithm for dense WLANs

Machine Learning for User Traffic Classification in Wireless Systems

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