An induction learning approach for building intrusion detection models using genetic algorithms

Jian Guan; Da Xin Liu; Bin Ge Cui

An induction learning approach for building intrusion detection models using genetic algorithms

Jian Guan^*
, Da Xin Liu
, Bin Ge Cui

^*Corresponding author for this work

Harbin Engineering University

Research output: Contribution to conference › Paper › peer-review

13 Scopus citations

Abstract

It is a complex knowledge engineering that building and updating an effective intrusion detection system. A method of learning the intrusion detection rules based on Genetic Algorithms was presented in order to realize the automation of the detection models. The same attributes of an intrusion can be found through the heuristic search in the network data space. In our experiments the characters of an attack, such as smurf, were summarized inductively through the datasets of the 1998 DARPA Intrusion Detection Evaluation Program. The effectiveness and robustness of the approach are proved.

Original language	English
Pages	4339-4342
Number of pages	4
State	Published - 2004
Externally published	Yes
Event	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China Duration: 15 Jun 2004 → 19 Jun 2004

Conference

Conference	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings
Country/Territory	China
City	Hangzhou
Period	15/06/04 → 19/06/04

Keywords

Genetic algorithms
Induction learning
Information processing
Intelligent method
Intrusion detection

Cite this

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title = "An induction learning approach for building intrusion detection models using genetic algorithms",

abstract = "It is a complex knowledge engineering that building and updating an effective intrusion detection system. A method of learning the intrusion detection rules based on Genetic Algorithms was presented in order to realize the automation of the detection models. The same attributes of an intrusion can be found through the heuristic search in the network data space. In our experiments the characters of an attack, such as smurf, were summarized inductively through the datasets of the 1998 DARPA Intrusion Detection Evaluation Program. The effectiveness and robustness of the approach are proved.",

keywords = "Genetic algorithms, Induction learning, Information processing, Intelligent method, Intrusion detection",

author = "Jian Guan and Liu, \{Da Xin\} and Cui, \{Bin Ge\}",

year = "2004",

language = "英语",

pages = "4339--4342",

note = "WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings ; Conference date: 15-06-2004 Through 19-06-2004",

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T1 - An induction learning approach for building intrusion detection models using genetic algorithms

AU - Guan, Jian

AU - Liu, Da Xin

AU - Cui, Bin Ge

PY - 2004

Y1 - 2004

N2 - It is a complex knowledge engineering that building and updating an effective intrusion detection system. A method of learning the intrusion detection rules based on Genetic Algorithms was presented in order to realize the automation of the detection models. The same attributes of an intrusion can be found through the heuristic search in the network data space. In our experiments the characters of an attack, such as smurf, were summarized inductively through the datasets of the 1998 DARPA Intrusion Detection Evaluation Program. The effectiveness and robustness of the approach are proved.

AB - It is a complex knowledge engineering that building and updating an effective intrusion detection system. A method of learning the intrusion detection rules based on Genetic Algorithms was presented in order to realize the automation of the detection models. The same attributes of an intrusion can be found through the heuristic search in the network data space. In our experiments the characters of an attack, such as smurf, were summarized inductively through the datasets of the 1998 DARPA Intrusion Detection Evaluation Program. The effectiveness and robustness of the approach are proved.

KW - Genetic algorithms

KW - Induction learning

KW - Information processing

KW - Intelligent method

KW - Intrusion detection

UR - https://www.scopus.com/pages/publications/4444310563

M3 - 论文

AN - SCOPUS:4444310563

SP - 4339

EP - 4342

T2 - WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings

Y2 - 15 June 2004 through 19 June 2004

ER -

An induction learning approach for building intrusion detection models using genetic algorithms

Abstract

Conference

Keywords

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Cite this