JUCS - Journal of Universal Computer Science 22(4): 589-604, doi: 10.3217/jucs-022-04-0589
An Approach for Intrusion Detection Using Novel Gaussian Based Kernel Function
Gunupudi Rajesh Kumar‡, Nimmala Mangathayaru‡, Gugulothu Narsimha§‡ VNRVJIET, Hyderabad, India§ Jawaharlal Nehru Technological University, Hyderabad, India
Corresponding author: Gunupudi Kumar ( gunupudirajesh@gmail.com ) © Gunupudi Kumar, Nimmala Mangathayaru, Gugulothu Narsimha. This article is freely available under the J.UCS Open Content License. Citation:
Kumar GR, Mangathayaru N, Narsimha G (2016) An Approach for Intrusion Detection Using Novel Gaussian Based Kernel Function. JUCS - Journal of Universal Computer Science 22(4): 589-604. https://doi.org/10.3217/jucs-022-04-0589 |  |
Abstract
Software Security and Intrusion Detection need to be dealt at three levels Network, Host level and Application level. In this paper the major objective is to design and analyze the suitability of Gaussian similarity measure for intrusion detection. The objective is to use this as a distance measure to find the distance between any two data samples of training set such as DARPA Data Set, KDD Data Set. This major objective is to use this measure as a distance metric when applying k-means algorithm. The novelty of this approach is making use of the proposed distance function as part of k-means algorithm so as to obtain disjoint clusters. This is followed by a case study, which demonstrates the process of Intrusion Detection. The proposed similarity has fixed upper and lower bounds.
Keywords
intrusion detection, similarity function, k-means, Gaussian, text processing, software vulnerabilities