Abstract
The state-of-the-art presently in the network intrusion detection, both in the network-level intrusion detection system and the host-level intrusion detection system, is completely based on the black box model which learns the pattern from knowledge database or from the dataset to the model. Proposed model is to combine the machine learning-based IDS approach and the domain knowledge incorporating method to build efficient and intelligent IDS which can be employed to detect typical intrusion and future intrusion which is not known. The idea behind is to make some data assimilation process in the features of the dataset such that a reduced and a meaningful feature set representation can be fed in to the model so as to construct intelligent generalized model which will be capable of handling unforeseen attack and new different kind of large data within in limited time period. May be with some compromise in the accuracy of the model but with increased generalizability.
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Jose, N., Govindarajan, J. (2022). DOMAIN-Based Intelligent Network Intrusion Detection System. In: Smys, S., Balas, V.E., Palanisamy, R. (eds) Inventive Computation and Information Technologies. Lecture Notes in Networks and Systems, vol 336. Springer, Singapore. https://doi.org/10.1007/978-981-16-6723-7_34
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DOI: https://doi.org/10.1007/978-981-16-6723-7_34
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