Abstract
With the significant increase in use of networks, network security has become more important and challenging. An intrusion detection system plays a major role in providing security. This paper proposes a model in which Artificial Neural Network and Data Mining approaches are used together. In this model “Self Organizing Map” approach is used for behavior learning and “Outlier Mining” approach is used for detecting an intruder. The scope of the proposed model is for internet. This model improves the capability of detecting intruders: both masqueraders and misfeasors.
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Pareek, V., Mishra, A., Sharma, A., Chauhan, R., Bansal, S. (2010). A Deviation Based Outlier Intrusion Detection System. In: Meghanathan, N., Boumerdassi, S., Chaki, N., Nagamalai, D. (eds) Recent Trends in Network Security and Applications. CNSA 2010. Communications in Computer and Information Science, vol 89. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14478-3_39
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DOI: https://doi.org/10.1007/978-3-642-14478-3_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14477-6
Online ISBN: 978-3-642-14478-3
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