Date of Award
2004
Document Type
Thesis
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Khoshgoftaar, Taghi M.
Abstract
Increasing aggressions through cyber terrorism pose a constant threat to information security in our day to day life. Implementing effective intrusion detection systems (IDSs) is an essential task due to the great dependence on networked computers for the operational control of various infrastructures. Building effective IDSs, unfortunately, has remained an elusive goal owing to the great technical challenges involved, and applied data mining techniques are increasingly being utilized in attempts to overcome the difficulties. This thesis presents a comparative study of the traditional "direct" approaches with the recently explored "indirect" approaches of classification which use class binarization and combiner techniques for intrusion detection. We evaluate and compare the performance of IDSs based on various data mining algorithms, in the context of a well known network intrusion evaluation data set. It is empirically shown that data mining algorithms when applied using the indirect classification approach yield better intrusion detection models.
Extent
203 p.
Recommended Citation
Ibrahim, Nawal H., "Evaluating indirect and direct classification techniques for network intrusion detection" (2004). Electronic Theses and Dissertations 2000 - 2009. 712.
https://digitalcommons.fau.edu/etd_general_5/712
Identifier
FADT13128
Comments
College of Engineering and Computer Science|FAU Electronic Theses and Dissertations Collection|Thesis (M.S.)--Florida Atlantic University, 2004.|College of Engineering and Computer Science