Title :
An intrusion detection system based on data mining and immune principles
Author :
Zhao, Jun-zhong ; Huang, Hou-Kuan
Author_Institution :
Sch. of Comput. & Inf. Technol., Northern Jiaotong Univ., Beijing, China
Abstract :
In this paper, a framework of an immune-based intrusion detection system (IDS) is presented. Here data mining techniques are used to discover frequently occurring patterns, which are equivalent to self proteins in the immune system. During the tolerance process known as negative selection, a set of valid detectors that does not match any self protein mined previously is generated in the space of nonself based on a distance metric. These negative detectors are distributed into the network system to perform anomaly detection independently and concurrently. Our experiment confirms a low false positive rate and a high detection rate.
Keywords :
data mining; safety systems; security of data; anomaly detection; computer security; data mining techniques; distance metric; frequently occurring patterns; high detection rate; immune-based intrusion detection system; low false positive rate; negative selection; nonself space; self proteins; tolerance process; valid detectors; Artificial immune systems; Computer networks; Computer security; Data mining; Data security; Detectors; IP networks; Immune system; Intrusion detection; Proteins;
Conference_Titel :
Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
Print_ISBN :
0-7803-7508-4
DOI :
10.1109/ICMLC.2002.1176811
