Database Intrusion Detection Using Sequential Data Mining Approaches

Abstract

This dissertation demonstrates how sequential data mining algorithms can be enhanced. It also demonstrates how they can be used in detecting intrusion in the database. The dissertation is in eight chapters organized as follows: Chapter One: It begins with an introduction on the main to fields covered on the thesis which are Data Mining and Intrusion Detection. First, it starts with defining the data mining and its importance. Afterwards, it provides an introduction to the intrusion and intrusion detection. Also, the chapter provides a summary of the efforts done in the area data intrusion detection. Furthermore, the chapter provides a summary of the work done on the thesis. Finally, it provides a summary of the rest of the thesis is organized. Chapter Two: In this chapter, a summarized background is provided on all the fields and topics that the research interacts with. The main topics that are covered on the chapter are database, intrusion, intrusion detection, data mining and sequential data mining. Chapter Three: This chapter discusses the algorithm that was selected for study by the thesis, Apriori algorithm. The chapters begins with explaining why the algorithm is selected by the thesis. The chapter mainly explains the generic model of the algorithm and how it works. Finally it discusses three models or versions of Apriori algorithm. They share the same idea but differ in the implementation mechanism.