DocumentCode :
1805991
Title :
A New Intrusion Detection Method Based on Artificial Immune System
Author :
Wang, Baoyi ; Zhang, Shaomin
Author_Institution :
North China Electr. Power Univ., Baoding
fYear :
2007
fDate :
18-21 Sept. 2007
Firstpage :
91
Lastpage :
98
Abstract :
Use the algorithm of generating variable-radius detectors to generate detectors. Analyze different effects on detection results by choosing different radii. Test samples need to compare with all detectors to detect intrusions. When the number of detectors is large, the detection speed is slow. Cluster near detectors into an area. The test samples which fall in this area only compare with the detectors in this area not all detectors, so the times of comparing are reduced and the detection speed is increased. For the test sample falling in some area of non-self space which is not covered by any detector, comparing the minimum distance between this test sample and all self training samples with the minimum distance between this test sample and all detectors can judge whether this test sample is non-self It is proved by experiments that this method can increase the detection speed as well as detection rate.
Keywords :
artificial immune systems; learning (artificial intelligence); pattern clustering; security of data; artificial immune system; intrusion detection method; k-means clustering; variable-radius detector generation; Artificial immune systems; Automatic testing; Computer networks; Concurrent computing; Detectors; Humans; Immune system; Intrusion detection; Parallel processing; Power system protection; artificial immune; clustering; detection speed; holes; intrusion detection; kmeans;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Network and Parallel Computing Workshops, 2007. NPC Workshops. IFIP International Conference on
Conference_Location :
Liaoning
Print_ISBN :
978-0-7695-2943-1
Type :
conf
DOI :
10.1109/NPC.2007.117
Filename :
4351465
Link To Document :
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