A principal components analysis neural gas algorithm for anomalies clustering

Authors:
Xiufen Fang;Guisong Liu;Ting-Zhu Huang
Affiliations:
School of Applied Mathematics, Institute of Computational Science, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China;School of Applied Mathematics, Institute of Computational Science, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
Venue:
WSEAS TRANSACTIONS on SYSTEMS
Year:
2010

Citing 16
Cited 0

Original Contribution: Principal components, minor components, and linear neural networks

Neural Networks
Principal component neural networks: theory and applications

Principal component neural networks: theory and applications
Self-organizing maps

Self-organizing maps
Intrusion detection

Intrusion detection
The 1999 DARPA off-line intrusion detection evaluation

Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
A framework for constructing features and models for intrusion detection systems

ACM Transactions on Information and System Security (TISSEC)
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Robust growing neural gas algorithm with application in cluster analysis

Neural Networks - 2004 Special issue: New developments in self-organizing systems
Batch and median neural gas

Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A study in using neural networks for anomaly and misuse detection

SSYM'99 Proceedings of the 8th conference on USENIX Security Symposium - Volume 8
Hierarchical Kohonenen net for anomaly detection in network security

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Min-max hyperellipsoidal clustering for anomaly detection in network security

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust recursive least squares learning algorithm for principal component analysis

IEEE Transactions on Neural Networks
Local PCA algorithms

IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data

IEEE Transactions on Neural Networks
Learning in linear neural networks: a survey

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Neural gas network is a single-layered soft competitive neural network, which can be applied to clustering analysis with fast convergent speed comparing to Self-organizing Map (SOM), K-means etc. Combining neural gas with principal component analysis, this paper proposes a new clustering method, namely principal components analysis neural gas (PCA-NG), and the online learning algorithm is also given. The soft competitive learning of PCA-NG is based on local principal subspace, which characterizes the profile of a certain cluster. We utilize the PCA-NG to the domain of intrusion detection. Some experiments are carried out to illustrate the performance of the proposed approach by using a synthetic Gaussian-distributed dataset and the KDD CUP 1999 Intrusion Detection Evaluation dataset.