Improvement of anomaly intrusion detection performance by indirect relation for FTP service

Authors:
ByungRae Cha;JongGeun Jeong
Affiliations:
Dept. of Computer Eng., Honam Univ., Korea;Korea Research Foundation, Korea
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

Citing 2
Cited 1

An Intrusion-Detection Model

IEEE Transactions on Software Engineering - Special issue on computer security and privacy
On a Pattern-Oriented Model for Intrusion Detection

IEEE Transactions on Knowledge and Data Engineering

Network-Based Anomaly Intrusion Detection Improvement by Bayesian Network and Indirect Relation

KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference

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Abstract

In this paper, intrusion detection method using Bayesian Networks was estimated probability values of behavior contexts based on Bayes theory and Indirect relation. The contexts of network-based FTP service was represented Bayesian Networks of graphic types. We profiled concisely network-based FTP behaviors using behavior context by prior, posterior and Indirect relation. And this method be able to visualize behavior profile to detect/analyze anomaly behavior by BF-XML. We achieve simulation to translate audit data of network into BF-XML which is behavior profile of semi-structured data type for anomaly detection and to visualize BF-XML as SVG.