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.
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Cha, B., Lee, D. (2007). Network-Based Anomaly Intrusion Detection Improvement by Bayesian Network and Indirect Relation. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_18
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DOI: https://doi.org/10.1007/978-3-540-74827-4_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74826-7
Online ISBN: 978-3-540-74827-4
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