Early traffic classification using support vector machines

Sena, Gabriel Gómez; Belzarena, Pablo

doi:10.1145/1636682.1636693

Cited by 20 publications

(2 citation statements)

References 18 publications

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“…There are also some other studies in which different ML algorithms were compared and classified [7,22,[29][30][31]. In recent studies, application flow has been assumed to be two-way between server and client; and statistical features have been calculated backward and forward separately [8,9,29,[32][33][34][35][36].…”

Section: Related Workmentioning

confidence: 99%

Classification with Intelligent Systems for Internet Traffic in Enterprise Networks

2016

IJCCIE

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Use of internet has recently reached a very high level. As a result, issues such as development in the service quality, efficient use of network and the availability of different service packets have gained more importance. In order to perform traffic management effectively, the classification of traffic data flowing over the internet-especially providing the security on major networks-has become more important. This information is required to be known by the network administrators to manage the network properly because the internet traffic and variety in applications has rapidly increased. Port, payload and statistical information have been commonly used for the classification. Because there are a limited number of options in the classification made according to the port and payload based approaches, the supervised machine learning algorithms have started to be frequently used in the internet traffic classification. Support vector machine (SVM) and Neural networks (NN) based classifiers were used so many times in the previous studies. In the performed study, extreme learning machine (ELM) algorithm has been used instead of traditional classifying techniques; and the success of the classification made with ELM has been found to be higher than the others.

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Section: Related Workmentioning

confidence: 99%

Classification with Intelligent Systems for Internet Traffic in Enterprise Networks

2016

IJCCIE

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“…Much research work has been done on SVM based traffic identification and made great progress. Gabriel Gómez Sena [6] use the size of the first packets on both directions of a flow as a statistical fingerprint, by comparing the centroid clustering method with the SVM clustering method prove that the SVM based method has much higher accuracy in traffic identification. Rui Wang [7] and his colleagues concern that the peer of the P2P application connect with more different address than normal nodes, based on which he proposed a sensitive feature extraction algorithm and transformed the flow data to 3-dimension feature data as the input vector of the SVM algorithm.…”

Section: Svm Based Network Traffic Identification Reviewmentioning

confidence: 99%

SVM Based P2P Traffic Identification Method With Multiple Properties

Zhao¹,

Wei²,

Zou³

2012

IJEM

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With the rapid development of the Internet, P2P has become the main network application in the Internet, which consumes most of the network resources. Accurately identifying and making control of the P2P traffic is of great significance. As a mature classification theory, support vector machine (SVM) algorithm is suitable for P2P traffic identification. This paper proposes a SVM based P2P flow identification method, adopting multidimensional flow properties as the input vector, which can improve the P2P flow classification accuracy. Analysis shows this method has many advantages over the other methods.

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Traffic Classification and Application Identification in Network Forensics

Pluskal¹,

Lichtner²,

Ryšavý³

2018

Advances in Digital Forensics XIV

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Network traffic classification is an absolute necessity for network monitoring, security analyses and digital forensics. Without accurate traffic classification, the computational demands imposed by analyzing all the IP traffic flows are enormous. Classification can also reduce the number of flows that need to be examined and prioritized for analysis in forensic investigations. This chapter presents an automated feature elimination method based on a feature correlation matrix. Additionally, it proposes an enhanced statistical protocol identification method, which is compared against Bayesian network and random forests classification methods that offer high accuracy and acceptable performance. Each classification method is used with a subset of features that best suit the method. The methods are evaluated based on their ability to identify the application layer protocols and the applications themselves. Experiments demonstrate that the random forests classifier yields the most promising results whereas the proposed enhanced statistical protocol identification method provides an interesting trade-off between higher performance and slightly lower accuracy.

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Early traffic classification using support vector machines

Cited by 20 publications

References 18 publications

Classification with Intelligent Systems for Internet Traffic in Enterprise Networks

Classification with Intelligent Systems for Internet Traffic in Enterprise Networks

SVM Based P2P Traffic Identification Method With Multiple Properties

Traffic Classification and Application Identification in Network Forensics

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