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Incremental learning algorithm based on support vector machine with Mahalanobis distance (ISVMM) for intrusion prevention

Myint, Hnin Ohnmar and Meesad, Phayung (2009). Incremental learning algorithm based on support vector machine with Mahalanobis distance (ISVMM) for intrusion prevention. In: ECTI-CON 2009, IEEE, pp. 630–633.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1109/ECTICON.2009.5137129
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Abstract

In this paper we propose a new classifier called an incremental learning algorithm based on support vector machine with Mahalanobis distance (ISVMM). Prediction of the incoming data type by supervised learning of support vector machine (SVM), reducing the step of calculation and complexity of the algorithm by finding a support set, error set and remaining set, providing of hard and soft decisions, saving the time for repeatedly training the datasets by applying the incremental learning, a new approach for building an ellipsoidal kernel for multidimensional data instead of a sphere kernel by using Mahalanobis distance, and the concept of handling the covariance matrix from dividing by zero are various features of this new algorithm. To evaluate the classification performance of the algorithm, it was applied on intrusion prevention by employing the data from the third international knowledge discovery and data mining tools competition (KDDcup'99). According to the experimental results, ISVMM can predict well on all of the 41 features of incoming datasets without even reducing the enlarged dimensions and it can compete with the similar algorithm which uses a Euclidean measurement at the kernel distance.

Item Type: Conference or Workshop Item
Copyright Holders: 2009 IEEE
ISBN: 1-4244-3387-8, 978-1-4244-3387-2
Keywords: incremental learning; algorithm; support vector machine; Mahalanobis distance; ISVMM; intrusion prevention; intrusion detection
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 42608
Depositing User: Hnin Myint
Date Deposited: 29 Apr 2015 15:41
Last Modified: 11 Dec 2018 01:42
URI: http://oro.open.ac.uk/id/eprint/42608
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