Studies on Neural Networks for Intrusion Detection and Its Applications

Abstract

Security has become a crucial issue for computer systems because to the rapid expansion of computer networks over the last decade. In recent years, various soft-computing-based methodologies for the development of intrusion detection systems have been developed. The number of hidden layers in various neural network topologies is evaluated in order to discover the best neural network. The technique of attempting to discover instances of network attacks by comparing current behaviour to the expected actions of an intruder is known as misuse detection. The majority of current approaches to misuse detection rely on rule-based expert systems to spot signs of known attacks. These methods are less effective in detecting attacks that deviate from predicted patterns. Artificial neural networks have the ability to detect and classify network activity using data that is limited, incomplete, and nonlinear.