Authors:
Srinivas Mukkamala
1
;
Andrew H. Sung
1
;
Ajith Abraham
2
and
Vitorino Ramos
3
Affiliations:
1
New Mexico Tech, United States
;
2
Oklahama State University, United States
;
3
CVRM-IST, Instituto Superior Técnico,Technical University of Lisbon, Portugal
Keyword(s):
Network security, intrusion detection, adaptive regression splines, neural networks, support vector machines
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Formal Methods
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Methodologies and Technologies
;
Operational Research
;
Security
;
Sensor Networks
;
Signal Processing
;
Simulation and Modeling
;
Soft Computing
Abstract:
Past few years have witnessed a growing recognition of intelligent techniques for the construction of efficient and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDS) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this paper, we report a performance analysis between Multivariate Adaptive Regression Splines (MARS), neural networks and support vector machines. The MARS procedure builds flexible regression models by fitting separate splines to distinct intervals of the predictor variables. A brief comparison of different neural network learning algorithms is also given.