Title :
A hierarchical pea-based anomaly detection model
Author :
Biming Tian ; Merrick, K. ; Shui Yu ; Jiankun Hu
Author_Institution :
Sch. of Eng. & IT, UNSW@ADFA, Canberra, ACT, Australia
Abstract :
A hierarchical intrusion detection model is proposed to detect both anomaly and misuse attacks. In order to further speed up the training and testing, PCA-based feature extraction algorithm is used to reduce the dimensionality of the data. A PCA-based algorithm is used to filter normal data out in the upper level. The experiment results show that PCA can reduce noise in the original data set and the PCA-based algorithm can reach the desirable performance.
Keywords :
data privacy; feature extraction; principal component analysis; security of data; PCA-based feature extraction algorithm; anomaly detection; data dimensionality reduction; hierarchical intrusion detection model; misuse attack detection; noise reduction; normal data filtering; Indexes; Intrusion detection; Principal component analysis; Probes; Training; Training data; Vectors;
Conference_Titel :
Computing, Networking and Communications (ICNC), 2013 International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4673-5287-1
Electronic_ISBN :
978-1-4673-5286-4
DOI :
10.1109/ICCNC.2013.6504158
