Expert System for Intrusion Detection and Its Applications: A Brief Overview

Abstract

Different neural network structures are examined in order to determine the optimal neural network in terms of the number of hidden layers. The process of attempting to identify instances of network attacks by comparing current activity to the expected actions of an intruder is known as misuse detection. The majority of current approaches to detecting misuse involve the use of rule-based expert systems to identify indicators of known attacks. These techniques are less effective at detecting attacks that deviate from expected patterns.  Based on limited, incomplete, and nonlinear data sources, artificial neural networks have the potential to identify and classify network activity. This paper provides a brief overview of the use of various Artificial Intelligence techniques and their advancements in the design, development, and application of Intrusion Detection Systems (IDS) for protecting computer and communication networks from intruders.