Earticle

초록

영어

Network anomaly detection has been a hot topic in the past years. However, high false alarm rate, difficulties in obtaining exact clean data for the modeling of normal patterns and the deterioration of detection rate because of “unclean” training set always make it not as good as we expect. Therefore, we propose a novel data mining method for network anomaly detection in this paper. Experimental results on the well-known KDD Cup 1999 dataset demonstrate it can effectively detect anomalies with high true positives, low false positives as well as with high confidence than the state-of-the-art anomaly detection methods. Furthermore, even provided with not purely “clean” data (unclean data), the proposed method is still robust and effective.

목차

Abstract.
 1. Introduction
 2. TCM-KNN Algorithm
 3. Anomaly Detection Framework Based on TCM-KNN Al
 4. Experimental Results
  4.1 Dataset and Preprocess
  4.2 Experimental Results
 5. Conclusions and Future Work
 References

키워드

저자

• Yang Li [ Chinese Academy of Sciences, Beijing China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605