Abstract
Internet traffic classification has a critical role on network monitoring, quality of service, intrusion detection, network security and trend analysis. The conventional port-based method is ineffective due to dynamic port usage and masquerading techniques. Besides, payload-based method suffers from heavy load and encryption. Due to these facts, machine learning based statistical approaches have become the new trend for the network measurement community. In this short paper, we propose a new statistical approach based on DBSCAN clustering and weighted cosine similarity. Our experimental test results show that the proposed approach achieves very high accuracy.
This work has been supported by Inforcept Networks corporation.
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References
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DBSCAN Wikipedia, http://en.wikipedia.org/wiki/DBSCAN
Cosine Similarity Wikipedia, http://en.wikipedia.org/wiki/Cosine_similarity
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© 2012 IFIP International Federation for Information Processing
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Beşiktaş, C., Mantar, H.A. (2012). Real-Time Traffic Classification Based on Cosine Similarity Using Sub-application Vectors. In: Pescapè, A., Salgarelli, L., Dimitropoulos, X. (eds) Traffic Monitoring and Analysis. TMA 2012. Lecture Notes in Computer Science, vol 7189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28534-9_10
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DOI: https://doi.org/10.1007/978-3-642-28534-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28533-2
Online ISBN: 978-3-642-28534-9
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