• Journal of Internet Computing and Services
    ISSN 2287 - 1136(Online) / ISSN 1598 - 0170 (Print)
    http://jics.or.kr/

Design and Evaluation of an Anomaly Detection Method based on Cross-Feature Analysis using Rough Sets for MANETs


Ihn-Han Bae, Hwa-Ju Lee, Journal of Internet Computing and Services, Vol. 9, No. 6, pp. 27-36, Dec. 2008
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Keywords: Anomaly Detection, Rough Sets, Cross-feature Analysis, Inter-feature Correlation Patterns, MANETs

Abstract

With the proliferation of wireless devices, mobile ad-hoc networking (MANETS) has become a very exciting and important technology. However, MANET is more vulnerable than wired networking. Existing security mechanisms designed for wired networks have to be redesigned in this new environment. In this paper, we discuss the problem of anomaly detection in MANET. The focus of our research is on techniques for automatically constructing anomaly detection models that are capable of detecting new or unseen attacks. We propose a new anomaly detection method for MANETs. The proposed method performs cross-feature analysis on the basis of Rough sets to capture the inter-feature correlation patterns in normal traffic. The performance of the proposed method is evaluated through a simulation. The results show that the performance of the proposed method is superior to the performance of Huang method that uses cross-feature based on the probability of feature attribute value. Accordingly, we know that the proposed method effectively detects anomalies.


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Cite this article
[APA Style]
Ihn-Han Bae and Hwa-Ju Lee (2008). Design and Evaluation of an Anomaly Detection Method based on Cross-Feature Analysis using Rough Sets for MANETs. Journal of Internet Computing and Services, 9(6), 27-36.

[IEEE Style]
I. Bae and H. Lee, "Design and Evaluation of an Anomaly Detection Method based on Cross-Feature Analysis using Rough Sets for MANETs," Journal of Internet Computing and Services, vol. 9, no. 6, pp. 27-36, 2008.

[ACM Style]
Ihn-Han Bae and Hwa-Ju Lee. 2008. Design and Evaluation of an Anomaly Detection Method based on Cross-Feature Analysis using Rough Sets for MANETs. Journal of Internet Computing and Services, 9, 6, (2008), 27-36.