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.
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Cha, B., Jeong, J. (2007). Improvement of Anomaly Intrusion Detection Performance by Indirect Relation for FTP Service. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_108
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DOI: https://doi.org/10.1007/978-3-540-73007-1_108
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
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