Earticle

초록

영어

The cyber-attacks represent one of the most dangerous secret weapons. Intrusion detection system is an important tool to protect our systems and networks against the various forms of attacks. The purpose of this paper is to build a fast and high performance hybrid hierarchical intrusion detection system called NFPHIDS that possesses the following characteristics: have a short training time, detect the low frequent attacks, give a high detection rate for frequent attacks, and give a low false alarm rate. NFPHIDS contains two levels. The first one includes four fast classifiers Random Forest, Simple Cart, Best first decision tree, Naive Bayes used for their excellent performance on the detection of respectively Normal behavior and DOS, Probe, R2L, and U2R. Only five outputs of the first level are selected, and used as inputs of the second level that contains Naïve Bayes as final classifier. The experimentation on KDD99 shows the high performance of our model compared to the results obtained by some well-known classifiers.

목차

Abstract
 1. Introduction
 2. Related Works
 4. Our Approach
  4.1. NFPHIDS
  4.2. The Operation Mode of NFPHIDS
 5. Experiments
  5.1. Training and Test Data Set
  5.2. Comparative Study of Classifiers
  5.3. Evaluation of the New Hierarchical IDS
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Ahmed Ahmim [ Laboratory of Networks and Systems, Department of Computer Science Badji Mokhtar-Annaba University, Algeria ]
• Nacira Ghoualmi-Zine [ Laboratory of Networks and Systems, Department of Computer Science Badji Mokhtar-Annaba University, Algeria ]

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