Title :
Neuralised intrusion detection system
Author :
Jinny, S. Vinila ; Kumari, J. Jaya
Author_Institution :
Comput. Sci. & Eng., Noorul Islam Univ. Kumaracoil, Thuckalay, India
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;
Conference_Titel :
Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), 2011 International Conference on
Conference_Location :
Thuckafay
Print_ISBN :
978-1-61284-654-5
DOI :
10.1109/ICSCCN.2011.6024530