DocumentCode :
3222265
Title :
Adaptive intrusion detection algorithm based on learning algorithm
Author :
Kim, Dae-Wook ; Yang, Jae-Won ; Sim, Kwee-Bo
Author_Institution :
Sch. of Electr. & Electron. Eng., Chung-Ang Univ., Seoul, South Korea
Volume :
3
fYear :
2004
fDate :
2-6 Nov. 2004
Firstpage :
2229
Abstract :
Signature based intrusion detection system (IDS), having stored rules for detecting intrusions at the library, judges whether new inputs are intrusion or not by matching them with the new inputs. However their policy has two restrictions generally. First, when they couldn´t make rules against new intrusions, false negative (FN) errors may are taken place. Second, when they made a lot of rules for maintaining diversification, the amount of resources grows larger proportional to their amount. In this paper, we propose the learning algorithm which can evolve the competent of anomaly detectors having the ability to detect anomalous attacks by genetic algorithm. The anomaly detectors are the population be composed of by following the negative selection procedure of the biological immune system. To show the effectiveness of proposed system, we apply the learning algorithm to the artificial network environment, which is a computer security system.
Keywords :
authorisation; digital signatures; genetic algorithms; learning (artificial intelligence); security of data; adaptive intrusion detection algorithm; anomaly detector; artificial network environment; biological immune system; computer security system; false negative error; genetic algorithm; learning algorithm; Computer crime; Computer security; Detectors; Genetics; IP networks; Immune system; Intrusion detection; Libraries; Network servers; Vaccines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics Society, 2004. IECON 2004. 30th Annual Conference of IEEE
Print_ISBN :
0-7803-8730-9
Type :
conf
DOI :
10.1109/IECON.2004.1432145
Filename :
1432145
Link To Document :
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