Information security is an issue of serious global concern. The
complexity, accessibility, and openness of the Internet have served to
increase the security risk of information systems tremendously. This
paper concerns intrusion detection. We describe approaches to intrusion
detection using neural networks and support vector machines. The key
ideas are to discover useful patterns or features that describe user
behavior on a system, and use the set of relevant features to build
classifiers that can recognize anomalies and known intrusions, hopefully
in real time. Using a set of benchmark data from a KDD (knowledge
discovery and data mining) competition designed by DARPA, we demonstrate
that efficient and accurate classifiers can be built to detect
intrusions. We compare the performance of neural networks based, and
support vector machine based, systems for intrusion detection
%0 Conference Paper
%1 1007774
%A Mukkamala, S.
%A Janoski, G.
%A Sung, A.
%B Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
%D 2002
%K data mining real time
%P 1702 -1707
%R 10.1109/IJCNN.2002.1007774
%T Intrusion detection using neural networks and support vector
machines
%U http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1007774&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1007774
%V 2
%X Information security is an issue of serious global concern. The
complexity, accessibility, and openness of the Internet have served to
increase the security risk of information systems tremendously. This
paper concerns intrusion detection. We describe approaches to intrusion
detection using neural networks and support vector machines. The key
ideas are to discover useful patterns or features that describe user
behavior on a system, and use the set of relevant features to build
classifiers that can recognize anomalies and known intrusions, hopefully
in real time. Using a set of benchmark data from a KDD (knowledge
discovery and data mining) competition designed by DARPA, we demonstrate
that efficient and accurate classifiers can be built to detect
intrusions. We compare the performance of neural networks based, and
support vector machine based, systems for intrusion detection
@inproceedings{1007774,
abstract = {Information security is an issue of serious global concern. The
complexity, accessibility, and openness of the Internet have served to
increase the security risk of information systems tremendously. This
paper concerns intrusion detection. We describe approaches to intrusion
detection using neural networks and support vector machines. The key
ideas are to discover useful patterns or features that describe user
behavior on a system, and use the set of relevant features to build
classifiers that can recognize anomalies and known intrusions, hopefully
in real time. Using a set of benchmark data from a KDD (knowledge
discovery and data mining) competition designed by DARPA, we demonstrate
that efficient and accurate classifiers can be built to detect
intrusions. We compare the performance of neural networks based, and
support vector machine based, systems for intrusion detection},
added-at = {2012-07-11T03:38:28.000+0200},
author = {Mukkamala, S. and Janoski, G. and Sung, A.},
biburl = {https://www.bibsonomy.org/bibtex/255528873c2eb88af8b7046174e55dc68/ntnguyen},
booktitle = {Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on},
doi = {10.1109/IJCNN.2002.1007774},
interhash = {2d9df9ebff782567a5c3432dc6fca254},
intrahash = {55528873c2eb88af8b7046174e55dc68},
keywords = {data mining real time},
pages = {1702 -1707},
timestamp = {2012-07-11T03:38:28.000+0200},
title = {Intrusion detection using neural networks and support vector
machines},
url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1007774&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1007774},
volume = 2,
year = 2002
}