DocumentCode :
3378096
Title :
Neuralised intrusion detection system
Author :
Jinny, S. Vinila ; Kumari, J. Jaya
Author_Institution :
Comput. Sci. & Eng., Noorul Islam Univ. Kumaracoil, Thuckalay, India
fYear :
2011
fDate :
21-22 July 2011
Firstpage :
137
Lastpage :
139
Abstract :
Internet brings in marvelous turning point to business in terms of new finders. But it also brings in lot of loop hole to the business. Best known risk is intrusion, also referred as hacking or cracking. Intrusion detection method are anomaly detection and misuse detection. Our interest here is in anomaly detection and we have proposed a scalable solution for detecting network based anomalies. Application of a dynamic clustering method with enhanced support vector machine improves the performance of existing intrusion detection system. This work reviewed the existing SVM and presents a study for further enhancement of SVM and have noted the next research direction.
Keywords :
pattern clustering; security of data; support vector machines; Internet; cracking; dynamic clustering method; hacking; intrusion detection system; support vector machine; Anomaly detection; Association rule mining; Dynamic clustering; intrusion detection; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), 2011 International Conference on
Conference_Location :
Thuckafay
Print_ISBN :
978-1-61284-654-5
Type :
conf
DOI :
10.1109/ICSCCN.2011.6024530
Filename :
6024530
Link To Document :
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