An Appraisal and Proportional Study of Data Mining Techniques for Web Intrusion Detection Methods

Abstract

Despite of growing statistics technology widely, security has remained one stimulating area for computers and webs. In statistics security, intrusion detection is the act of detecting actions that attempt to compromise the confidentiality, integrity or availability of a resource. Currently many researchers have focused on intrusion detection methods based on data mining techniques as an efficient artifice. Data mining is one of the technologies applied to intrusion detection to invent a new pattern from the massive web data as well as to reduce the strain of the manual compilations of the intrusion and normal behavior patterns. This article reviews the current state of art data mining techniques, compares various data mining techniques used to implement an intrusion detection methods such as Decision Trees, Artificial Neural Web, Naïve Bayes, Support Vector Machine and K- Nearest Neighbour Algorithm by highlighting advantages and disadvantages of each of the techniques. Finally, a discussion of the future technologies and methodologies which promise to enhance the ability of computer methods to detect intrusion is provided and current research challenges are pointed out in the field of intrusion detection methods.