TY - GEN
T1 - UNPCC
T2 - 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006
AU - Xie, Zongxing
AU - Quirino, Thiago
AU - Shyu, Mei Ling
AU - Chen, Shu Ching
AU - Chang, Li Wu
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
N2 - The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network intrusion detection. Our proposed UNPCC (Unsu-pervised Principal Component Classifier) algorithm is a multi-class unsupervised classifier with absolutely no requirements for any a priori class related data information (e.g., the number of classes and the maximum number of instances belonging to each class), and an inherently natural supervised classification scheme, both which present high detection rates and several operational advantages (e.g., lower training time, lower classification time, lower processing power requirement, and lower memory requirement). Experiments have been conducted with the KDD Cup 99 data and network traffic data simulated from our private network testbed, and the promising results demonstrate that our UNPCC algorithm outperforms several well-known supervised and unsupervised classification algorithms.
AB - The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network intrusion detection. Our proposed UNPCC (Unsu-pervised Principal Component Classifier) algorithm is a multi-class unsupervised classifier with absolutely no requirements for any a priori class related data information (e.g., the number of classes and the maximum number of instances belonging to each class), and an inherently natural supervised classification scheme, both which present high detection rates and several operational advantages (e.g., lower training time, lower classification time, lower processing power requirement, and lower memory requirement). Experiments have been conducted with the KDD Cup 99 data and network traffic data simulated from our private network testbed, and the promising results demonstrate that our UNPCC algorithm outperforms several well-known supervised and unsupervised classification algorithms.
UR - http://www.scopus.com/inward/record.url?scp=38949141928&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38949141928&partnerID=8YFLogxK
U2 - 10.1109/ICTAI.2006.115
DO - 10.1109/ICTAI.2006.115
M3 - Conference contribution
AN - SCOPUS:38949141928
SN - 0769527280
SN - 9780769527284
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 743
EP - 750
BT - Procedings - 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006
Y2 - 13 October 2006 through 15 October 2006
ER -