The intrusion detection system with Learning Automata

ABSTRACT: Intrusion detection, a topic that has evolved heavily due to the rising concern for information technology security, has endured numerous architecture abstractions. All of these architecture abstractions have strengths and weaknesses with regards to various factors like efficiency, security, integrity, durability, and cost-effectiveness, to name a few. In this paper, we will attempt to describe the architecture of intrusion detection that minimizes the weaknesses of this model. Our architecture will heavily build upon the Autonomous Agents For Intrusion Detection (AAFID) architecture, which has already been implemented in the Center for Education and Research in Information Assurance and Security (CERIAS) center in Purdue University. We will, however, design a different functionality for our agents, making them rather intelligent. Such intelligent agents will seek to use tools that the field of artificial intelligence provides in order to maximize their probability of detecting intrusions.