Earticle

초록

영어

In this paper we have proposed a hybrid intrusion detection system consisting of a misuse detection model based upon a Binary Tree of Classifiers as the first stage and an anomaly detection model based upon SVM Classifier as the second stage. The Binary Tree consists of several best known classifiers specialized in detecting specific attacks at a high level of accuracy. Combination of a Binary Tree and specialized classifiers will increase accuracy of the misuse detection model. The misuse detection model will detect only known attacks. In-order to detect unknown attacks, we have an anomaly detection model as the second stage. SVM has been used, since it’s the best known classifier for anomaly detection which will detect patterns that deviate from normal behavior. The proposed hybrid intrusion detection has been tested and evaluated using KDD Cup ’99, NSL-KDD and UNSW-NB15 dataset.

목차

Abstract
 1. Introduction
  1.1. Intrusion Detection Systems (IDS)
 2. Related Work
 3. Methodology
  3.1. Datasets
 4. Experiments and Results
  4.1.Weka API and Pseudo Code
 5. Conclusion and Future Work
 References

키워드

저자

• Amar Agrawal [ Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada ]
• Sabah Mohammed [ Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada ]
• Jinan Fiaidhi [ Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605