Title :
Intrusion Detection by New Data Description Method
Author :
GhasemiGol, Mohammad ; Monsefi, Reza ; Yazdi, Hadi Sadoghi
Author_Institution :
Dept. of Comput. Eng., Ferdowsi Univ. of Mashhad (FUM), Mashhad, Iran
Abstract :
This paper presents a new approach in data description for intrusion detection based on Support Vector Data Description (SVDD). The SVDD is a well-known kernel method which tries to fit a hypersphere around the target objects and more precise boundary is depending on using proper kernel functions. In the proposed method we find a minimal hyperellipse around the normal objects to describe them. The overall experiments show prominence of our proposed method in comparison with the standard SVDD.
Keywords :
pattern classification; security of data; support vector machines; intrusion detection; kernel method; one-class classification; support vector data description; Computational modeling; Computer simulation; Data engineering; Intelligent systems; Intrusion detection; Kernel; Principal component analysis; Reconstruction algorithms; Support vector machine classification; Support vector machines; Intrusion detection; One-class classification; Support vector data description;
Conference_Titel :
Intelligent Systems, Modelling and Simulation (ISMS), 2010 International Conference on
Conference_Location :
Liverpool
Print_ISBN :
978-1-4244-5984-1
DOI :
10.1109/ISMS.2010.11
