Real-Time Traffic Classification Based on Cosine Similarity Using Sub-application Vectors
نویسندگان
چکیده
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, payloadbased 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.
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