Intrusion detection by machine learning: A review
Created by W.Langdon from
gp-bibliography.bib Revision:1.8157
- @Article{Tsai200911994,
-
author = "Chih-Fong Tsai and Yu-Feng Hsu and Chia-Ying Lin and
Wei-Yang Lin",
-
title = "Intrusion detection by machine learning: A review",
-
journal = "Expert Systems with Applications",
-
volume = "36",
-
number = "10",
-
pages = "11994--12000",
-
year = "2009",
-
ISSN = "0957-4174",
-
DOI = "doi:10.1016/j.eswa.2009.05.029",
-
URL = "http://www.sciencedirect.com/science/article/B6V03-4WBC1NH-C/2/3e0271b6e5009bc945abd584bdb46c71",
-
keywords = "genetic algorithms, genetic programming, Intrusion
detection, Machine learning, Hybrid classifiers,
Ensemble classifiers",
-
abstract = "The popularity of using Internet contains some risks
of network attacks. Intrusion detection is one major
research problem in network security, whose aim is to
identify unusual access or attacks to secure internal
networks. In literature, intrusion detection systems
have been approached by various machine learning
techniques. However, there is no a review paper to
examine and understand the current status of using
machine learning techniques to solve the intrusion
detection problems. This chapter reviews 55 related
studies in the period between 2000 and 2007 focusing on
developing single, hybrid, and ensemble classifiers.
Related studies are compared by their classifier
design, datasets used, and other experimental setups.
Current achievements and limitations in developing
intrusion detection systems by machine learning are
present and discussed. A number of future research
directions are also provided.",
-
notes = "survey",
- }
Genetic Programming entries for
Chih-Fong Tsai
Yu-Feng Hsu
Chia-Ying Lin
Wei-Yang Lin
Citations