Abstract
Security is one of the most major concern issue arises in computer and internet technology. To conquer this problem, Intrusion Detection System (IDS) is the challenging solution in network systems. Such system is used to detect the known or unknown attacks made by intruders. Data mining methodologies like, clustering, classification, etc., plays a very important role in design and development of such IDS. They makes such system more effective and efficient. This paper describes some recent hybrid data mining based approaches used in development of IDS. We also describe the hybrid classification approaches used in IDS. Such Hybrid classifiers are any mixture of basic classifiers such as, SVM, Bayesian classifier, Neural network classifier, etc.
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Datir, H.N., Jawandhiya, P.M. (2019). Survey on Hybrid Data Mining Algorithms for Intrusion Detection System. In: Balas, V., Sharma, N., Chakrabarti, A. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-13-1402-5_22
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DOI: https://doi.org/10.1007/978-981-13-1402-5_22
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