Online Internet traffic identification algorithm based on multistage classifier

2013 ◽  
Vol 10 (2) ◽  
pp. 89-97 ◽  
Author(s):  
Du Min ◽  
Chen Xingshu ◽  
Tan Jun
Keyword(s):  
Internet Traffic ◽  
Identification Algorithm ◽  
Traffic Identification ◽  
Multistage Classifier

P2P Traffic Identification Algorithm Based on Topology

2012 ◽  
Vol 487 ◽  
pp. 297-300
Author(s):  
Ru Xia Sun ◽  
Chun Yong Yin
Keyword(s):  
False Alarm ◽  
Detection Rate ◽  
The Internet ◽  
Identification Algorithm ◽  
Distributed Network ◽  
Network Behavior ◽  
High Detection Rate ◽  
Traffic Identification ◽  
Major Threat ◽  
High Detection

The botnet consists of some computers controlled by an attacker and has become a major threat to the internet and users. Because the p2p botnet is a distributed network, making the identification of p2p bots is very difficult. In response to this threat, we present a p2p identification algorithm based on topology. This method only depends on three network behavior features. Our approach has a high detection rate and an acceptable low false alarm rate.

A Method for P2P Traffic Identification Based on Semi-Supervised Learning

2014 ◽  
Vol 602-605 ◽  
pp. 1933-1937
Author(s):  
Lian Fa Wu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Supervised Learning ◽  
Internet Traffic ◽  
Support Vector ◽  
Traffic Classification ◽  
Learning Methods ◽  
Traffic Identification ◽  
Supervised Methods ◽  
Internet Traffic Classification

In recent years, Internet traffic classification using machine learning is a hot topic, and supervised learning methods which contain Support Vector Machine were used to identify Internet traffic in many papers. The supervised learning methods need many instances which have been labeled to train classifying model, but it is difficult to label the instances because many traffic have been encrypted. Labeled instances and unlabeled instances can be used by semi-supervised learning methods to train the classifying model, so that it is very fit for p2p traffic identification. Transductive support vector machine is one of the typical semi-supervised learning methods. Based on theoretic analyzing and experiment, we compared the accuracy of TSVM and SVM. The experiment results show that the semi-supervised methods have some advantages on identification of p2p traffic.

Header signature maintenance for Internet traffic identification

2016 ◽  
Vol 27 (1) ◽  
pp. e1959 ◽  
Author(s):  
Sung-Ho Yoon ◽  
Jun-Sang Park ◽  
Baraka D. Sija ◽  
Mi-Jung Choi ◽  
Myung-Sup Kim
Keyword(s):  
Internet Traffic ◽  
Traffic Identification

An early Intelligent P2P Traffic Identification Method

2013 ◽  
Vol 321-324 ◽  
pp. 2812-2817
Author(s):  
Jian Fen Peng ◽  
Xu Yan Tu ◽  
Hong Bing Wang ◽  
Ya Jian Zhou
Keyword(s):  
Internet Traffic ◽  
The Internet ◽  
Traffic Identification ◽  
Identification Method ◽  
Training Samples ◽  
Flow Feature ◽  
C4.5 Algorithm

In order to identify P2P traffic quickly and accurately as early as possible, an early intelligent P2P traffic identification method(EIIC) is proposed, which uses the size of early three packets payload and server port number obtained from the TCP flow as flow feature and classifies the traffic based on C4.5 algorithm. The results show that EIIC satisfies the following conditions: extracted features used, training samples selected under the unbiased conditions, it can be adaptive to actual network conditions and early classify the Internet traffic into application among WEB, MAIL, BitTorrent and eMule categories efficiently and quickly.

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