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DEEP PACKET INSPECTION USING HYBRID CLASSIFIER FOR UNKNOWN TRAFFIC FLOWS IN THE INTERNET


Affiliations
1 Sona College of Technology, India
2 Madanapalle Institute of Technology and Science, India
 

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In this era of Internet every electronic device being manufactured comes with built-ins to connect itself with the Internet. Such electronic gadgets create a massive amount of traffic flows every second. Managing this immense Internet traffic has become an exhausting job. Classifying these massive flows of data and interpreting them has attained considerable importance in the sight of researchers and Internet Service Providers. Many methods have been presented by various researchers to address this classification issue. This paper presents a Hybrid Classifier algorithm, which is a combination of the well-known machine learning types; the supervised learning and unsupervised learning to solve this classification issue. Accurate and Efficient recognition of flows is the key to manage flows in real-time. This algorithm classifies the traffic flows into real time flows and non-real time flows. It uses the built decision support model for classifying the flows based on the target class. To further validate the classification, it applies the k-means model. A significant improvement in the classification accuracy has been obtained.

Keywords

Voltage Stability, Voltage Collapse, Voltage Stability Indices, Radial Distribution Network.
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  • DEEP PACKET INSPECTION USING HYBRID CLASSIFIER FOR UNKNOWN TRAFFIC FLOWS IN THE INTERNET

Abstract Views: 260  |  PDF Views: 137

Authors

P Shanmugaraja
Sona College of Technology, India
K Chokkanathan
Madanapalle Institute of Technology and Science, India
K Thangaraj
Sona College of Technology, India
P Ilanchezhian
Sona College of Technology, India

Abstract


In this era of Internet every electronic device being manufactured comes with built-ins to connect itself with the Internet. Such electronic gadgets create a massive amount of traffic flows every second. Managing this immense Internet traffic has become an exhausting job. Classifying these massive flows of data and interpreting them has attained considerable importance in the sight of researchers and Internet Service Providers. Many methods have been presented by various researchers to address this classification issue. This paper presents a Hybrid Classifier algorithm, which is a combination of the well-known machine learning types; the supervised learning and unsupervised learning to solve this classification issue. Accurate and Efficient recognition of flows is the key to manage flows in real-time. This algorithm classifies the traffic flows into real time flows and non-real time flows. It uses the built decision support model for classifying the flows based on the target class. To further validate the classification, it applies the k-means model. A significant improvement in the classification accuracy has been obtained.

Keywords


Voltage Stability, Voltage Collapse, Voltage Stability Indices, Radial Distribution Network.

References