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

Authors:
Byungrae Cha;Dongseob Lee
Affiliations:
Dept. of Computer Eng., Honam Univ., Korea;Dept. of Information & Communication Eng., Honam Univ., Korea
Venue:
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Year:
2007

Citing 3
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
Improvement of anomaly intrusion detection performance by indirect relation for FTP service

IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks

Improving the performance of neural networks with random forest in detecting network intrusions

ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II

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Abstract

In this paper, Network-based anomaly 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. We achieve simulation to translate audit data of network into Bayesian network which is network-based behavior profile for anomaly detection.