earticle

영어

Internet traffic classification is one of the popular research interest area because of its benefits for many applications like intrusion detection system, congestion avoidance, traffic prediction etc. Internet traffic is classified on the basis of statistical features because port and payload based techniques have their limitations. For statistics based techniques machine learning is used. The statistical feature set is large. Hence, it is a challenge to reduce the large feature set to an optimal feature set. This will reduce the time complexity of the machine learning algorithm. This paper tries to obtain an optimal feature set by using a hybrid approach -An unsupervised clustering algorithm (K-Means) with a supervised feature selection algorithm (Best Feature Selection).