Mining maximal frequent itemsets for intrusion detection

Abstract

It has been the recent research focus and trend to apply data mining techniques in an intrusion detection system for discovering new types of attacks, but it is still in its infancy. This paper presents an innovative technique, called MMID, that applies maximal frequent itemsets mining to intrusion detection and can significantly improve the accuracy and performance of an intrusion detection system. The experimental results show that MMID is efficient and accurate for the attacks that occur intensively in a short period of time. © Springer-Verlag 2004.