Earticle

초록

영어

Neural Network (NN) models employed in lots of researches about system call intrusion detection, however, most probably suffer from problems, such as sensitivity to random initialization, local optimum, outlier of training data, etc. A robust supervised relevance neural gas with discriminant analysis, called RSRNG-DA, is firstly proposed. By incorporating several robust strategies, e.g. outlier resistant scheme and building discriminant analysis on dissimilarity spaces of prototypes, into supervised relevance neural gas (SRNG) framework, RSRNG-DA possesses better robust properties. RSRNG-DA can tolerate the influence cased by outlier and random ordering of training data, and present a significant improvement on classification efficiency. Moreover, the relevant degree of each system call as feature that contributes most to classification performance can be determined, such that the relevance of all features can help to prune irrelevant pattern dimensions. Our technique is evaluated on the system call database maintained by NMU. Experimental results are compared with other existing methods in the literature, and have shown the superior performance on detection rate, false positive and computation time aspects.

목차

Abstract
 1. Introduction
 2. Review of Supervised Relevance Neural Gas
 3. Robust Supervised Relevance Neural Gas Algorithm with Discriminant Analysis
 4. Experiments and Results
 5. Interesting Finding on Feature Relevance Vector
 6. Conclusion
 References

키워드

저자

• Jia Weifeng [ School of Software Engineering, Anyang Normal University, Anyang, 455000, China ] Corresponding author
• Chen Weijun [ School of Software Engineering, Anyang Normal University, Anyang, 455000, China ]

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