A comparison of neural projection techniques applied to intrusion detection systems

Authors:
Álvaro Herrero;Emilio Corchado;Paolo Gastaldo;Rodolfo Zunino
Affiliations:
Civil Engineering Department, University of Burgos, Burgos, Spain;Civil Engineering Department, University of Burgos, Burgos, Spain;Department of Biophysical and Electronic Engineering, Genoa University, Genoa, Italy;Department of Biophysical and Electronic Engineering, Genoa University, Genoa, Italy
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

Citing 12
Cited 3

Multilayer feedforward networks are universal approximators

Neural Networks
The rectified Gaussian distribution

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Intrusion detection with neural networks

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Neural Network Component for an Intrusion Detection System

SP '92 Proceedings of the 1992 IEEE Symposium on Security and Privacy
Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit

Data Mining and Knowledge Discovery
Intrusion detection using hierarchical neural networks

Pattern Recognition Letters
A Projection Pursuit Algorithm for Exploratory Data Analysis

IEEE Transactions on Computers
Detecting compounded anomalous SNMP situations using cooperative unsupervised pattern recognition

ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
A neural model in intrusion detection systems

ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Analyzing TCP traffic patterns using self organizing maps

ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
MOVICAB-IDS: visual analysis of network traffic data streams for intrusion detection

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Hierarchical Kohonenen net for anomaly detection in network security

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

IDS Based on Bio-inspired Models

KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
MOVIH-IDS: A mobile-visualization hybrid intrusion detection system

Neurocomputing
ADMIRE: Anomaly detection method using entropy-based PCA with three-step sketches

Computer Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper reviews one nonlinear and two linear projection architectures, in the context of a comparative study, which are used as either alternative or complementary tools in the identification and analysis of anomalous situations by Intrusion Detection Systems (IDSs). Three neural projection models are empirically compared, using real traffic data sets in an IDS framework. The specific multivariate data analysis techniques that drive these models are able to identify different factors or components by studying higher order statistics - variance and kurtosis - in order to display the most interesting projections or dimensions. Our research describes how a network manager is able to diagnose anomalous behaviour in data traffic through visual projection of network traffic. We also emphasize the importance of the time-dependent variable in the application of these projection methods.