IPSJ SIG Technical Report Vol.2013-ICS-172 No /11/12 1,a), 1,b) Anomaly Detection 1. 1 Nagoya Institute of Technology 1 Presently with Nagoya In

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1 1,a), 1,b) Anomaly Detection 1. 1 Nagoya Institute of Technology 1 Presently with Nagoya Institute of Technology a) otsuka.takanobu@nitech.ac.jp b) ito.takayuki@nitech.ac.jp Anomaly Detection [7] ALSOK [8] [1], [2][3] 1

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8 24 [ ] (??) ( 18) [1] [2] (E) vol.125-e, no.6, pp , June 2005 [3] Vol.102,2003 [4],,, RFID,. IIS,,2009. [5],,,, No,75-760, [6],,,, Vol.122, [7]., [8] ALSOK., price.html [9] Intel-GE Care Innovations, Quiet Care, [10] Takanobu Otsuka, Tatsunosuke Tsuboi, Takayuki Ito, Prototyping and evaluation of a wireless sensor network that aims easy installation,the 26TH INTERNATIONAL CONFERENCE ON INDUS- TRIAL,ENGINEERING & OTHER APPLICATIONS OF APPLIED INTELLIGENT SYSTEMS, [11] Varun Chandola, Arindam Banerjee, and Vipin Kumar, Anomaly Detection: A Survey, Technical Report,Department of Computer Science and Engineering University of Minnesota, TR ,2007. [12] Kumar, V Parallel and distributed computing for cybersecurity. Distributed Systems Online, IEEE 6, [13] Spence, C., Parra, L., and Sajda, P Detection, synthesis and compression in mammo- graphic image analysis with a hierarchical image probability model. In Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. IEEE Computer Society, Washington, DC, USA, 3. [14] Fujimaki, R., Yairi, T., and Machida, K An approach to spacecraft anomaly detection problem using kernel feature space. In Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. ACM Press, New York, NY, USA, [15] Janakiram, D., Reddy, V., and Kumar, A Outlier detection in wireless sensor networks using bayesian belief networks. In First International Conference on Communication SystemSoftware and Middleware. 16. [16] Du. W Fang, L., and Peng, N Lad: localization anomaly detection for wireless sensor networks. J. Parallel Distrib. Comput. 66, 7, [17] Chatzigiannakis, V., Papavassiliou, S., Grammatikou, M., and Maglaris, B Hierarchical anomaly detection in distributed large-scale sensor networks. In ISCC 06: Proceedings of the 11th IEEE Symposium on Computers and Communications. IEEE Computer Society, Washington, DC, USA, [18] Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall,W. Philip Kegelmeyer, SMOTE: Synthetic Minority Over-sampling Technique,Journal of Articial Intelligence Research [19] Rehan Akbani, Stephen Kwek, and Nathalie Japkowicz, Applying Support Vector Machines to Imbalanced Datasets, Lecture Notes in Computer Science Volume 3201, 2004, pp (2002) [20],, JAWS

% 69.5% Support Vector Machine(SVM) [7] ALSOK [8] [1], [2][3] RFID [4] [5] [6], 2.2 Anomaly Detection[10] Anomaly Detect

% 69.5% Support Vector Machine(SVM) [7] ALSOK [8] [1], [2][3] RFID [4] [5] [6], 2.2 Anomaly Detection[10] Anomaly Detect 1,a) 1,b) 1,c) WSNs SVM A Bayesian Network based Method for Reducing Noise-data for Anomaly Detection in Flexible Wireless Sensor Networks Takanobu Otsuka 1,a) Tatsunosuke Tsuboi 1,b) Takayuki Ito 1,c)