Dynamic Online Traffic Classification Using Data Stream Mining

Data stream mining techniques are able to classify evolving data streams such as network traffic in the presence of concept drift. In order to classify high bandwidth network traffic in real-time, data stream mining classifiers need to be implemented on reconfigurable high throughput platform, such as Field Programmable Gate Array (FPGA). This paper proposes an algorithm for online network traffic classification based on the concept of incrementalk-means clustering to continuously learn from both labeled and unlabeled flow instances. Two distance measures for incrementalk-means (Euclidean and Manhattan) distance are analyzed to measure their impact on the network traffic classification in the presence of concept drift. The experimental results on real datasets show that the proposed algorithm exhibits consistency, up to 94% average accuracy for both distance measures, even in the presence of concept drifts. The proposed incrementalk-means classification using Manhattan distance can classify network traffic 3 times faster than Euclidean distance at 671 thousands flow instances per second.

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Real-Time Monitoring of Road Traffic Using Data Stream Mining

2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) ◽

10.1109/ice.2018.8436271 ◽

2018 ◽

Cited By ~ 6

Author(s):

Paulo Figueiras ◽

Zala Herga ◽

Guilherme Guerreiro ◽

Antonio Rosa ◽

Ruben Costa ◽

...

Keyword(s):

Real Time ◽

Data Stream ◽

Road Traffic ◽

Data Stream Mining ◽

Real Time Monitoring ◽

Stream Mining ◽

Using Data

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Anomalous Network Packet Detection Using Data Stream Mining

Journal of Information Security ◽

10.4236/jis.2011.24016 ◽

2011 ◽

Vol 02 (04) ◽

pp. 158-168 ◽

Cited By ~ 7

Author(s):

Zachary Miller ◽

William Deitrick ◽

Wei Hu

Keyword(s):

Data Stream ◽

Data Stream Mining ◽

Stream Mining ◽

Using Data ◽

Packet Detection

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Knowledge Discovery Using Data Stream Mining

Advances in Business Information Systems and Analytics - Social Network Analytics for Contemporary Business Organizations ◽

10.4018/978-1-5225-5097-6.ch012 ◽

2018 ◽

pp. 231-258

Author(s):

Prasanna Lakshmi Kompalli

Keyword(s):

Data Streams ◽

Data Stream ◽

Relevant Information ◽

Research Community ◽

Data Stream Mining ◽

Data Sets ◽

Stream Mining ◽

Real World Problem ◽

Using Data ◽

Over Time

In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data. Such data sets, which continuously and rapidly grow over time, are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging task. Data stream mining is a process of gaining knowledge from continuous and rapid records of data. Due to increased streaming information, data stream mining has attracted the research community in the recent past. There is voluminous literature that has been published in this domain over the past few years. Due to this, isolating the correct study would be grueling task for researchers and practitioners. While addressing a real-world problem, it would be difficult to find relevant information as it would be hidden in data streams. This chapter tries to provide solution as it is an amalgamation of all techniques used for data stream mining.

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