Title :
Construction of low false alarm and high precision RBFNN for detecting flooding based denial of service attacks using stochastic sensitivity measure
Author :
Ng, Wing W Y ; Chan, Aki P F ; Yeung, Daniel S. ; Tsang, Eric C C
Author_Institution :
Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China
Abstract :
A good intrusion detection system (IDS) should have high precision on detecting attacks and low false alarm rates. Machine learning techniques for IDS usually yield high false alarm rate. In this work, we propose to construct host-based IDS for flooding-based denial of service (DoS) attacks by minimizing the generalization error bound of the IDS to reduce its false alarm rate and increase its precision. Experiments using artificial datasets support our claims.
Keywords :
learning (artificial intelligence); radial basis function networks; security of data; stochastic processes; DoS attacks; artificial datasets; false alarm construction; flooding-based denial of service; generalization error; intrusion detection system; machine learning; precision RBFNN; stochastic sensitivity measure; Computer crime; Covariance matrix; Floods; Intrusion detection; Knowledge based systems; Machine learning; Neural networks; Stochastic processes; Stochastic systems; Web and internet services; False Alarm Rate; Flooding-Based DoS; Network Intrusion; Radial Basis Function NN; Stochastic Sensitivity Measure;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527763
