Network Data Stream Classification by Deep Packet Inspection and Machine Learning

2018 ◽  
pp. 245-251
Author(s):  
Chunyong Yin ◽  
Hongyi Wang ◽  
Jin Wang
Keyword(s):  
Machine Learning ◽  
Data Stream ◽  
Network Data ◽  
Deep Packet Inspection ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Packet Inspection

Analyzing and repairing concept drift adaptation in data stream classification

2021 ◽  
Author(s):  
Ben Halstead ◽  
Yun Sing Koh ◽  
Patricia Riddle ◽  
Russel Pears ◽  
Mykola Pechenizkiy ◽  
...  
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
Stream Classification ◽  
Data Stream Classification

Chi-MFlexDT: Chi-square-based multi flexible fuzzy decision tree for data stream classification

2021 ◽  
pp. 107301
Author(s):  
Farnaz Mahan ◽  
Maryam Mohammadzad ◽  
Seyyed Meysam Rozekhani ◽  
Witold Pedrycz
Keyword(s):  
Decision Tree ◽  
Data Stream ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Chi Square ◽  
Stream Classification ◽  
Data Stream Classification

scholarly journals Research on QoS Classification of Network Encrypted Traffic Behavior Based on Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1376
Author(s):  
Yung-Fa Huang ◽  
Chuan-Bi Lin ◽  
Chien-Min Chung ◽  
Ching-Mu Chen
Keyword(s):  
Machine Learning ◽  
Majority Voting ◽  
Deep Packet Inspection ◽  
Traffic Classification ◽  
K 13 ◽  
Packet Inspection ◽  
Majority Voting Method ◽  
Encrypted Traffic ◽  
Traffic Behavior

In recent years, privacy awareness is concerned due to many Internet services have chosen to use encrypted agreements. In order to improve the quality of service (QoS), the network encrypted traffic behaviors are classified based on machine learning discussed in this paper. However, the traditional traffic classification methods, such as IP/ASN (Autonomous System Number) analysis, Port-based and deep packet inspection, etc., can classify traffic behavior, but cannot effectively handle encrypted traffic. Thus, this paper proposed a hybrid traffic classification (HTC) method based on machine learning and combined with IP/ASN analysis with deep packet inspection. Moreover, the majority voting method was also used to quickly classify different QoS traffic accurately. Experimental results show that the proposed HTC method can effectively classify different encrypted traffic. The classification accuracy can be further improved by 10% with majority voting as K = 13. Especially when the networking data are using the same protocol, the proposed HTC can effectively classify the traffic data with different behaviors with the differentiated services code point (DSCP) mark.

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