A Novel Framework for Network Traffic Classification Using Unknown Flow Detection

Shaikh, Zeba Atique; Harkut, Dinesh G.

doi:10.1109/csnt.2015.132

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Therefore, the systematic analysis of these classification models clarifies traffic classification, the QoS in numerous applications, and the problems in every classification system. Firewall access control, routing, policy-specific, and traffic QoS are just a few network services where traffic needs to be identified and classified [28,29].…”

Section: Literature Reviewmentioning

confidence: 99%

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Feature Selection and Dynamic Network Traffic Congestion Classification based on Machine Learning for Internet of Things

Elngar

Burlea‐Schiopoiu

2023

WJCMS

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The network traffic congestion classifier is essential for network monitoring systems. Network traffic characterization is a methodology to classify traffic into several classes supporting various attributes. In this paper, payload-based classification is suggested for network traffic characterization. It has a broad scope of utilization like network security assessment, intrusion identification, QoS supplier, et cetera; furthermore, it has significance in investigating different suspicious movements in the network. Numerous supervised classification techniques like Support Vector Machines and unsupervised clustering methods like K-Means connected are used in traffic classification. In current network conditions, minimal supervised data and unfamiliar applications influence the usual classification procedure's performance. This paper implements a methodology for network traffic classification using clustering, feature extraction, and variety for the Internet of Things (IoT). Further, K-Means is used for network traffic clustering datasets, and feature extraction is performed on grouped information. KNN, Naïve Bayes, and Decision Tree classification methods classify network traffic because of extracted features, which presents a performance measurement between these classification algorithms. The results discuss the best machine learning algorithm for network congestion classification. According to the outcome, clustering (k-means) with network classification (Decision Tree) generates a higher accuracy, 86.45 %, than other clustering and network classification

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Section: Literature Reviewmentioning

confidence: 99%

“…The Naïve Bayes algorithm is highly scalable and requires several features in the training phase. Naïve bays classification can classify traffic data using even a few training data [28].…”

Section: Algorithm I: K-means Clusteringmentioning

confidence: 99%

Feature Selection and Dynamic Network Traffic Congestion Classification based on Machine Learning for Internet of Things

Elngar

Burlea‐Schiopoiu

2023

WJCMS

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“…In 2015, Shaikh and Harkut [27] proposed a framework that classifies unknown flows in the network, solving the problem of applying unknowns in critical situations with little supervised training data. Flow label propagation was proposed, which automatically and accurately labeled more unlabeled flows to enhance the ability of Nearest Clustering-based Classifiers (NCCs).…”

Section: Unknown Traffic Identificationmentioning

confidence: 99%

OpenCBD: A Network-Encrypted Unknown Traffic Identification Scheme Based on Open-Set Recognition

Chen³

et al. 2022

Wireless Communications and Mobile Computing

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The encryption of network traffic promotes the development of encrypted traffic classification and identification research. However, many existing studies are only effective for closed-set experimental data, that is to say, only for traffic of known classes, while there are often lots of unknown classes traffic in the real environment of open sets, and many studies have difficulty identifying the traffic of unknown classes and can only misclassify them as known classes. How to identify unknown traffic and classify known traffic in an open-collection environment is one of the focuses of traffic analysis research. Considering these problems, this paper proposes a novel solution, which applies the open-set recognition method to the unknown traffic identification, and constructs a model based on deep learning and ensemble learning. The method constructs a model based on a convolutional neural network and a transformer encoder and then uses a three-stage training and testing process, combined with a novel loss function, to generalize to the open space to form OpenCBD. Experiments on public datasets show that the proposed method is significantly better than other open-set identification methods. It can not only distinguish known traffic from unknown traffic but also identify specific classes of known traffic.

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A review on machine learning–based approaches for Internet traffic classification

et al. 2020

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A Novel Framework for Network Traffic Classification Using Unknown Flow Detection

Cited by 4 publications

References 15 publications

Feature Selection and Dynamic Network Traffic Congestion Classification based on Machine Learning for Internet of Things

Feature Selection and Dynamic Network Traffic Congestion Classification based on Machine Learning for Internet of Things

OpenCBD: A Network-Encrypted Unknown Traffic Identification Scheme Based on Open-Set Recognition

A review on machine learning–based approaches for Internet traffic classification

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