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Anomaly Detection Scheme Using Data Mining in Mobile Environment

  • Kwang-jin Park
  • Hwang-bin Ryou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2668)

Abstract

For detecting the intrusion effectively, many researches have developed data mining framework for constructing intrusion detection modules. Traditional anomaly detection techniques focus on detecting anomalies in new data after training on normal data. To detect anomalous behavior, precise normal pattern is necessary. For this, the understanding of the characteristics of data on network is inevitable. In this paper we propose to use clustering and association rules as the basis for guiding anomaly detection in mobile environment. We present dynamic transaction for generating more effectively detection patterns. For applying entropy to filter noisy data, we present a technique for detecting anomalies without training on normal data.

Keywords

Association Rule Intrusion Detection Intrusion Detection System Unlabeled Data Mobile Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Kwang-jin Park
    • 1
  • Hwang-bin Ryou
    • 2
  1. 1.Dept. of Computer ScienceKwangwoon UniversitySeoulKorea
  2. 2.Dept. of Computer ScienceKwangwoon UniversitySeoulKorea

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