Intrusion Detection Systems Using Adaptive Regression Splines

TL;DR

This paper compares the effectiveness of Multivariate Adaptive Regression Splines, neural networks, and support vector machines in building intrusion detection systems to enhance cybersecurity.

Contribution

It provides a performance analysis of MARS, neural networks, and SVMs, highlighting the potential of adaptive regression splines for intrusion detection.

Findings

MARS offers flexible modeling for intrusion detection.

Neural networks and SVMs are also evaluated for performance.

The paper discusses the advantages of adaptive regression splines in cybersecurity.

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.