Machine Learning Techniques for Traffic Identification and Classifiacation in SDWSN: A Survey

Thupae, Ratanang; Isong, Bassey; Gasela, Naison; Abu‐Mahfouz, Adnan M.

doi:10.1109/iecon.2018.8591178

Cited by 27 publications

(12 citation statements)

References 19 publications

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“…Deep learning models have been applied to network traffic classification currently. The research in [13] introduced a SAE-based scheme to classify unencrypted data flows. However, their scheme was only applied to unencrypted traffic and could not be deployed to the encrypted data.…”

Section: Survey On Ai Applied To Sdn and Related Reviewmentioning

confidence: 99%

Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

Chang

Lee

Chu

et al. 2020

Adv. technol. innov.

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The traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models.

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Section: Survey On Ai Applied To Sdn and Related Reviewmentioning

confidence: 99%

Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

Chang

Lee

Chu

et al. 2020

Adv. technol. innov.

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“…The CDN identifiers can be collected from the HTTP clear text messages and corresponding IP addresses can be resolved. Then, we look at the size of each uplink packet and instead of using thresholds as depicted in [16], we use K-means clustering to segregate the uplink packet sizes into two clusters; the first cluster represents the request packets and the second cluster represents the acknowledgment packets. Once the uplink request packets are identified, we sum the data downloaded between any two consecutive request packets, with this sum representing the downloaded chunk size corresponding to the first request between the two.…”

Section: A Inferring Chunk Sizesmentioning

confidence: 99%

From Encrypted Video Traces to Viewport Classification

Belmoukadam¹,

Barakat²

2020

2020 16th International Conference on Network and Service Management (CNSM)

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The Internet has changed drastically in recent years, multiple novel applications and services have emerged, all about consuming digital content. In parallel, users are no longer satisfied by the Internet's best effort service, instead, they expect a seamless service of high quality from the side of the network. This has increased the pressure on Internet service providers (ISP) in their effort to efficiently engineer their traffic and improve their end-users' experience. Content providers from their side, and to further protect the content of their customers, have shifted towards end-to-end encryption (e.g., TLS/SSL), which has complicated even further the task of ISPs in handling the traffic in their network. The challenge is notable for video streaming traffic which is driving the Internet traffic growth, and which imposes tight constraints on the quality of service provided by the network depending on the content of the video stream and the equipment on the end-user premises. Video streaming relies on the dynamic adaptive streaming over HTTP (DASH) protocol which takes into consideration the underlying network conditions (e.g., delay, loss rate, and throughput) and the viewport capacity (e.g., screen resolution) to improve the experience of the end user in the limit of available resources. Nevertheless, knowing the reality of the encrypted video traffic is of great help to ISPs as it allows taking appropriate network management actions. In this work, we propose an experimental framework able to infer fine-grained video flow information such as chunk sizes from encrypted YouTube video traces. We also present a novel technique to separate video and audio chunks from encrypted traces based on Gaussian Mixture Models (GMM). We evaluate our technique with real chunk sizes (Audio/Video) collected through the browser using the Chrome Web Request API [1]. Then, we leverage these results and our dataset to train a model able to predict the class of viewport (either SD or HD) per video session with an average 92% accuracy and 85% F1 score.

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“…Unsupervised machine learning approaches take advantage of the similarities between samples to classify unlabeled traffic. Well-known unsupervised approaches include k-means [13], AutoClass [14], and DBSCAN [15]. Machine learning approaches can classify encrypted traffic through the statistical features of the traffic.…”

Section: Related Workmentioning

confidence: 99%

Network Traffic Sampling System Based on Storage Compression for Application Classification Detection

et al. 2020

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With the development of the Internet, numerous new applications have emerged, the features of which are constantly changing. It is necessary to perform application classification detection on the network traffic to monitor the changes in the applications. Using RelSamp to sample traffic can provide the sampled traffic with sufficient application features to support application classification. RelSamp separately assigns counters for each flow to record the statistical features and introduces a collision chain into the hash flow table to resolve hash conflicts in the table entries. However, in high-speed networks, owing to the number of concurrent flows and heavy-tailed nature of the traffic, the storage allocation method of RelSamp results in a significant waste of storage on the traffic sampling device. Moreover, the hash conflict resolution of RelSamp causes the collision chains of several hash table entries to be excessively deep, thereby reducing the search efficiency of the flow nodes. To overcome the shortcomings of RelSamp, this study presents a sampling model known as MiniSamp. Based on the RelSamp sampling mechanism, MiniSamp introduces shared counter trees to compress the storage space of the counters during the sampling process and integrates an efficient search tree into the hash table. The search tree structure is adjusted according to the network environment to improve the search efficiency of the flow nodes. The experimental results demonstrate that MiniSamp can effectively aid network operators to classify traffic in the high-speed network.

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Machine Learning Techniques for Traffic Identification and Classifiacation in SDWSN: A Survey

Cited by 27 publications

References 19 publications

Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

From Encrypted Video Traces to Viewport Classification

Network Traffic Sampling System Based on Storage Compression for Application Classification Detection

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