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Attack classification based on data mining technique and its application for reliable medical sensor communication

Title
Attack classification based on data mining technique and its application for reliable medical sensor communication
Authors
Oh H.Doh I.Chae K.
Ewha Authors
채기준도인실
SCOPUS Author ID
채기준scopus; 도인실scopus
Issue Date
2009
Journal Title
International Journal of Computer Science and Applications
ISSN
0972-9038JCR Link
Citation
vol. 6, no. 3, pp. 20 - 32
Indexed
SCOPUS scopus
Abstract
Detecting network intrusion has been not only important but also difficult in the network security research area. In Medical Sensor Network(MSN), network intrusion is critical because the data delivered through network is directly related to patients' lives. Traditional supervised learning techniques are not appropriate to detect anomalous behaviors and new attacks because of temporal changes in network intrusion patterns and characteristics in MSN. Therefore, unsupervised learning techniques such as SOM (Self-Organizing Map) are more appropriate for anomaly detection. In this paper, we propose a real-time intrusion detection system based on SOM that groups similar data and visualize their clusters. Our system labels the map produced by SOM using correlations between features. We experiments our system with KDD Cup 1999 data set because MSN data is not available yet. Our system yields the reasonable misclassification rates and takes 0.5 seconds to decide whether a behavior is normal or attack. © Technomathematics Research Foundation.
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엘텍공과대학 > 컴퓨터공학과 > Journal papers
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