Application of Data Mining to Network Intrusion Detection: Classifier Selection Model

Authors:
Huy Anh Nguyen;Deokjai Choi
Affiliations:
Computer Science Department, Chonnam National University, Gwangju, Korea 500-757;Computer Science Department, Chonnam National University, Gwangju, Korea 500-757
Venue:
APNOMS '08 Proceedings of the 11th Asia-Pacific Symposium on Network Operations and Management: Challenges for Next Generation Network Operations and Service Management
Year:
2008

Citing 5
Cited 2

C4.5: programs for machine learning

C4.5: programs for machine learning
Parzen-Window Network Intrusion Detectors

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set

Intelligent Data Analysis
Estimating continuous distributions in Bayesian classifiers

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

Network intrusion detection system: a machine learning approach

Intelligent Decision Technologies
A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. The uncertainty to explore if certain algorithms perform better for certain attack classes constitutes the motivation for the reported herein. In this paper, we evaluate performance of a comprehensive set of classifier algorithms using KDD99 dataset. Based on evaluation results, best algorithms for each attack category is chosen and two classifier algorithm selection models are proposed. The simulation result comparison indicates that noticeable performance improvement and real-time intrusion detection can be achieved as we apply the proposed models to detect different kinds of network attacks.