Neural network-based intrusion detection systems

Abstract

As the Internet expands both in number of hosts connected and number of services provided, security has become a key issue. The goal of intrusion detection is to positively identify all true attacks and negatively identify all non-attacks. Most current approaches to intrusion detection involve the use of rule-based expert systems to identify indications of known attacks. However, these techniques are unable to identify attacks which vary from expected patterns. Artificial neural networks provide the potential to identify and classify network activity based on limited, incomplete, and nonlinear data sources. This paper presents an overview of recent advances in process of intrusion detection that utilizes neural networks, and discuss the future of this approach.