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There is a tremendous growth in the field of information technology due to which, network security is also facing significant challenge.The traditional Intrusion Detection System (IDS) is unable to handle the recent attacks and malware's. Hence, IDS which is an indispensable component of the network needs to be protected. Data mining based network intrusion detection is widely used to identify how and where the intrusions occur. Reducing the number of features by selecting the important features is critical to improve the accuracy and speed of classification algorithms. In order to improve the accuracy of an individual classifier, the classifiers are combined which is the prevalent approach. This book covers the concept of selecting the significant features using bio-inspired approach and develop a hybrid classifier model for IDS in terms of high accuracy and detection rates.
About the author
P.Amudha graduated her B.E in CSE, M.Tech in IT & obtained her Ph.D. in Information and Communication Engineering from Anna University. Currently she is working as Associate Professor in the Dept of CSE, Avinashilingam University. She has many publications in refereed Intl/Nat Journals and conferences. She has a membership in ISTE, CSI and IAENG.
