UNPCC: A novel unsupervised classification scheme for network intrusion detection Conference

Xie, Z, Quirino, T, Shyu, ML et al. (2006). UNPCC: A novel unsupervised classification scheme for network intrusion detection . Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, 743-750. 10.1109/ICTAI.2006.115

cited authors

  • Xie, Z; Quirino, T; Shyu, ML; Chen, SC; Chang, LW

authors

abstract

  • 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. © 2006 IEEE.

publication date

  • December 1, 2006

published in

Digital Object Identifier (DOI)

start page

  • 743

end page

  • 750