The CoralReef Software Suite as a Tool for System and Network Administrators
LISA '01 Proceedings of the 15th USENIX conference on System administration
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Traffic classification using clustering algorithms
Proceedings of the 2006 SIGCOMM workshop on Mining network data
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Challenging statistical classification for operational usage: the ADSL case
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Timely and continuous machine-learning-based classification for interactive IP traffic
IEEE/ACM Transactions on Networking (TON)
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There are several network traffic classification methodologies which differ in their assurance and computational complexity. This paper presents a novel network traffic classifier based on Bayesian Classification. This classifier combines the results from three different traffic classification methods, namely, Well Known Ports, Signature Analysis and Support Vector Machines, using a Naïve Bayes technique with data obtained from network inspection. The results are then compared with the results obtained from applying Well Known Ports, Signature Analysis and Machine Learning traffic classification methods individually. The comparison shows that the proposed combined method increases the amount of correctly classified traffic in at least 20% with respect to each individual methodology.