A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks
Costa
K
A
P
author
Pereira
L
A
M
author
Nakamura
R
Y
M
author
Pereira
C
R
author
Papa
J
P
author
Xavier Falcão
A
author
2015
Abstract We propose a nature-inspired approach to estimate the probability density function (pdf) used for data clustering based on the optimum-path forest algorithm (OPFC). OPFC interprets a dataset as a graph, whose nodes are the samples and each sample is connected to its k-nearest neighbors in a given feature space (a k-nn graph). The nodes of the graph are weighted by their pdf values and the pdf is computed based on the distances between the samples and their k-nearest neighbors. Once the k-nn graph is defined, OPFC finds one sample (root) at each maximum of the pdf and propagates one optimum-path tree (cluster) from each root to the remaining samples of its dome. Clustering effectiveness will depend on the pdf estimation, and the proposed approach efficiently computes the best value of k for a given application. We validate our approach in the context of intrusion detection in computer networks. First, we compare OPFC with data clustering based on k-means, and self-organization maps. Second, we evaluate several metaheuristic techniques to find the best value of k.
Intrusion detection
Optimum-path forest
Meta-heuristic
Clustering
exported from refbase (http://cns.ctu.edu.vn/refbase/show.php?record=77), last updated on Mon, 12 Jan 2015 09:22:48 +0700
text
http://www.sciencedirect.com/science/article/pii/S0020025514009311
http://www.sciencedirect.com/science/article/pii/S0020025514009311
10.1016/j.ins.2014.09.025
Costa_etal2015
Information Sciences
Innovative Applications of Artificial Neural Networks in Engineering
2015
continuing
periodical
academic journal
294
95
108
0020-0255