Paper Info
Title
Comparisons of Machine Learning Algorithms for Application Identification of Encrypted Traffic
Abstract
Application identification assists network operators effectively on many tasks regarding network management such as controlling bandwidth or securing traffic from others. However, encryption is one of the factors to make application identification difficult, because it is so hard to infer the original (unencrypted) packets from encrypted packets. As a result, the accuracy of application identification is getting worse as the increase of encrypted traffic. In this paper, we propose a method to increase the accuracy of application identification whatever the traffic is encrypted or not. We propose EFM (Estimated Features Method) and investigate how three different supervised machine learning algorithms (Support Vector Machine, Naaive Bayes Kernel Estimation, and C4.5 decision tree) affect the accuracy of identification. Our results show that EFM using SVM is able to provide overall accuracy 97.2% for encrypted traffic.
Year
DOI
Venue
2011
10.1109/ICMLA.2011.162
ICMLA (2)
Keywords
Field
DocType
machine learning algorithms,overall accuracy,estimated features method,network management,support vector machine,encrypted packet,network operator,application identification,decision tree,naaive bayes kernel estimation,encrypted traffic,machine learning,support vector machines,cryptography,decision trees,learning artificial intelligence,computer network security
Decision tree,Data mining,Cryptography,Computer science,Encryption,Artificial intelligence,Network management,Kernel density estimation,Pattern recognition,Support vector machine,Network security,Network packet,Algorithm,Machine learning
Conference
Citations 
PageRank 
References 
6
0.56
3
Authors
5
Name
Order
Citations
PageRank
Yohei Okada191.65
Shingo Ata232953.56
Nobuyuki Nakamura3182.53
Yoshihiro Nakahira461.57
Ikuo Oka54617.05