Network traffic classification &amp;#x2014; A comparative study of two common decision tree methods: C4.5 and Random forest

Munther, Alhamza; Alalousi, Alabass; Nizam, Shahrul; Othman, Rozmie Razif; Anbar, Mohammed

doi:10.1109/iced.2014.7015800

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…It is an extension of the ID3 algorithm and can classify data that has continuous or discrete features. Therefore, it is utilized in the classification of network traffic [63]. The Decision tree includes multiple attributes including nodes and leaf nodes.…”

Section: Decision Trees (C45)mentioning

confidence: 99%

Efficient Traffic Classification Using Hybrid Deep Learning

Sarhangian

2024

Preprint

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<p>Network traffic classification is important for QoS, security, and billing. Using a single machine learning classification model suffers from low prediction and classification accuracy, especially for high dimensional datasets with a high sparsity level. Recently, hybrid-deep learning algorithms have shown high efficiency for traffic forecasting and classification. In this research, we suggested two hybrid models – the Convolutional Neural Network (CNN) combined with the Recurrent Neural Network (RNN) models and the Gated recurrent unit (GRU) combined with Long ShortTerm Memory (LSTM) – to improve traffic classification accuracy in terms of the ratio of correct predictions to total input samples. The hybrid CNN-LSTM and CNN-GRU have achieved an accuracy of up to 99.23% and 93.92%, respectively, for binary classification and 67.16% for multiclass classification. We employed Markov Blanket Algorithm to make the reduced feature set selection, which significantly improved classification accuracy and fscore for multi-class scenarios.</p>

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Section: Decision Trees (C45)mentioning

confidence: 99%

Efficient Traffic Classification Using Hybrid Deep Learning

Sarhangian

2024

Preprint

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“…2,[4][5][6] Machine learning algorithms are popular and reliable methods to build up classifiers for identifying unknown traffic flows. The primary challenges in network traffic flow classification area are to enhance classification accuracy and to lower the computational time, which is mainly blocked by the new types of network protocols are introduced rapidly and the volume of network traffic increases exponentially.…”

Section: Introductionmentioning

confidence: 99%

“…Since network traffic flow classification plays an important role in the network management field and network security area, classification algorithms are used to handle the data. The primary challenges in network traffic flow classification area are to enhance classification accuracy and to lower the computational time, which is mainly blocked by the new types of network protocols are introduced rapidly and the volume of network traffic increases exponentially . Machine learning algorithms are popular and reliable methods to build up classifiers for identifying unknown traffic flows .…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of network traffic data

Miao

Ruan

Pan

et al. 2017

Concurrency and Computation

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With the large volume of network traffic flow, it is necessary to preprocess raw data before classification to gain the accurate results speedily. Feature selection is an essential approach in preprocessing phase. The principal component analysis (PCA) is recognized as an effective and efficient method. In this paper, we classify network traffic flows by using the PCA technique together with 6 machine learning algorithms-Naive Bayes, decision tree, 1-nearest neighbor, random forest, support vector machine, and H 2 O. We analyzed the impact of PCA on the classification results by applying each algorithm with and without PCA onto the data set. Experiments were set out by varying the size of input data sets, and the performances were measured from 2 aspects, including average overall accuracy and F-measure. The computational time was also considered in analyzing the performance. Our results showed that random forest and 1-nearest neighbor were the top 2 algorithms among all the 6 regarding the 2 metrics mentioned above. Then we continued the study of PCA impact on per class level with these 2 algorithms as examples. And the positive correlation between overall impact and the number of class with significant impact was revealed.Lastly, the visualization was used in exploring the reasons of the impacts caused by PCA. Two factors are considered in PCA's impact on per class level: benefit for classes grouped by PCA and mislabeled error interfered by nearby groups.

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“…Most of the cases decision tree based C4.5 algorithm is used for packet analysis to help the classifier which will gain insight knowledge about transmitted packets and strategies to obtain the maximum benefit [3].Even though the C4.5 algorithm is successor of ID3, it has more advantages over ID3.When number of nodes getting increase obviously we have to divide into sub classes, but when we split in ID3, it causes high error classification and will take more time for processing the subsets.C4.5 is recommended to overcome these concerns using gain ratio to select the records associative only to the attributes. Even though the C5.0 is descendant of C4.5, we cannot use it for two reasons first, data set has limitations, a data set used by a researchers may not be applied by other researchers, second; datamining tool Weka simulation tool will support C5.0.Because of these reasons it's advisable to use C4.5 in data classification or network traffic classification [6].…”

Section: Capabilities Of C45mentioning

confidence: 99%

“…One of the supervised machine learning approaches called Decision Tree, which is widely used in network traffic classification. There are many techniques available in Decision Tree approach, among them C4.5 will provide the highest classification accuracy than others [6]. Network traffic identification and classification are playing vital role in network management and security areas where different applications using different services and consuming more network resources.…”

Section: Network Traffic Classificationmentioning

confidence: 99%

Privacy Protection and Perfect Classification Nature of C4.5 Algorithm

Chokkanathan¹,

Koteeswaran²

2018

IJET

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C4.5 algorithm is developed by Ross Quinlan which is the extension of ID3 algorithm used for generating a decision trees.Since the tree generated by C4.5 can be used for classification, so it's also referred to as statistical classifier.Even though the Random Decision Tree is used to avoid the information leakage there are some problems and issues related to privacy maintenance.When we try to instantiate more instances for one class it leads to ambiguity at the same time creating new classes more and more will increase the complexity in RDT. These problems can be resolved by using our C4.5 algorithm.We can have any number of nodes in a network, each node can create its own tree or class and each class can initiate many number of instances for a disseminated classification consuming secure amount or threshold homomorphic encryption. The main objective of this paper is to discuss the ideal nature of the C4.5 algorithm and how they support this algorithm to be utilized in various datamining process.

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Network traffic classification — A comparative study of two common decision tree methods: C4.5 and Random forest

Cited by 11 publications

References 18 publications

Efficient Traffic Classification Using Hybrid Deep Learning

Efficient Traffic Classification Using Hybrid Deep Learning

Comprehensive analysis of network traffic data

Privacy Protection and Perfect Classification Nature of C4.5 Algorithm

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