Earticle

초록

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).

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Multi-class Imbalance
 3. Problem Description
 4. Proposed Solution
 5. Results and Discussions
  5.1. The optimal feature set
  5.2. Comparison with other Machine Learning Algorithms
 6. Conclusion and Future Work
 References

키워드

저자

• M. P. Singh [ NIT Patna, Bihar, India ]
• Gargi Srivastava [ NIT Patna, Bihar, India ]
• Prabhat Kumar [ NIT Patna, Bihar, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605