UNPCC: A novel unsupervised classification scheme for network intrusion detection

Zongxing Xie, Thiago Quirino, Mei Ling Shyu, Shu Ching Chen, Li Wu Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProcedings - 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006
Pages743-750
Number of pages8
DOIs
StatePublished - Dec 1 2006
Event18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006 - Arlington, VA, United States
Duration: Oct 13 2006Oct 15 2006

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISSN (Print)1082-3409

Other

Other18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006
CountryUnited States
CityArlington, VA
Period10/13/0610/15/06

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Xie, Z., Quirino, T., Shyu, M. L., Chen, S. C., & Chang, L. W. (2006). UNPCC: A novel unsupervised classification scheme for network intrusion detection. In Procedings - 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006 (pp. 743-750). [4031968] (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI). https://doi.org/10.1109/ICTAI.2006.115