Zhi-Guo Ding, Da-Jun Du and Min-Rui Fei. An Isolation Principle Based Distributed Anomaly Detection Method in Wireless Sensor Networks. International Journal of Automation and Computing, vol. 12, no. 4, pp. 402-412, 2015. https://doi.org/10.1007/s11633-014-0847-9
Citation: Zhi-Guo Ding, Da-Jun Du and Min-Rui Fei. An Isolation Principle Based Distributed Anomaly Detection Method in Wireless Sensor Networks. International Journal of Automation and Computing, vol. 12, no. 4, pp. 402-412, 2015. https://doi.org/10.1007/s11633-014-0847-9

An Isolation Principle Based Distributed Anomaly Detection Method in Wireless Sensor Networks

doi: 10.1007/s11633-014-0847-9
Funds:

This work was supported by the National High Technology Research and Development Program of China (No. 2011AA040103-7), the National Key Scientific Instrument and Equipment Development Project (No. 2012YQ15008703), the Zhejiang Provincial Natural Science Foundation of China (No. LY13F020015), National Science Foundation of China (No. 61104089), Science and Technology Commission of Shanghai Municipality (No. 11JC1404000), and Shanghai Rising-Star Program (No. 13QA1401600).

  • Received Date: 2013-10-09
  • Rev Recd Date: 2014-03-28
  • Publish Date: 2015-08-01
  • Anomaly detection plays an important role in ensuring the data quality in wireless sensor networks (WSNs). The main objective of the paper is to design a light-weight and distributed algorithm to detect the data collected from WSNs effectively. This is achieved by proposing a distributed anomaly detection algorithm based on ensemble isolation principle. The new method offers distinctive advantages over the existing methods. Firstly, it does not require any distance or density measurement, which reduces computational burdens significantly. Secondly, considering the spatial correlation characteristic of node deployment in WSNs, local sub-detector is built in each sensor node, which is broadcasted simultaneously to neighbor sensor nodes. A global detector model is then constructed by using the local detector model and the neighbor detector model, which possesses a distributed nature and decreases communication burden. The experiment results on the labeled dataset confirm the effectiveness of the proposed method.

     

  • loading
  • [1]
    I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci. Wireless sensor networks: A survey. Computer Networks, vol. 38, no. 4, pp. 393-422, 2002.
    [2]
    J. Y. Huang, I. E. Liao, Y. F. Chung, K. T. Chen. Shielding wireless sensor network using Markovian intrusion detection system with attack pattern mining. Information Sciences, vol. 231, pp. 32-44, 2013.
    [3]
    S. Rajasegarar, C. Leckie, M. Palaniswami, Anomaly detection in wireless sensor networks. IEEE Wireless Communications, vol. 15, no. 4, pp. 34-40, 2008.
    [4]
    Y. Zhang. Observing the Unobservable: Distributed Online Outlier Detection in Wireless Sensor Networks, Ph.D. dissertation, University of Twente, The Netherlands, 2010.
    [5]
    Y. Zhang, N. Meratnia, P. Havinga. Outlier detection techniques for wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, vol. 12, no. 2, pp. 159-170, 2010.
    [6]
    W. J. Zhou, M. R. Fei, H. Y. Zhou, Z. Li. A fast detection method for bottle caps surface defect based on sparse representation. Intelligent Computing for Sustainable Energy and Environment, Communications in Computer and Information Science, Berlin, Germany: Springer, pp. 76-84, 2013.
    [7]
    Y. Zhang, N. Meratnia, P. Havinga. An online outlier detection technique for wireless sensor networks using unsupervised quarter-sphere support vector machine. In Proceedings of IEEE Intelligent Sensors, Sensor Networks and Information Processing, IEEE, Sydney, NSW, Australia, pp. 151-156, 2008.
    [8]
    J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. Culler, K. Pister. System architecture directions for networked sensors. In Proceedings of International Conference on Architectural Support for Programming Language and Operating Systems, ACM, Cambrige, Massachusetts, USA, vol. 34, pp. 93-104, 2000.
    [9]
    V. Chandola, A. Banerjee, V. Kumar. Anomaly detection: A survey. ACM Computing Surveys, vol. 41, no. 3, pp. 1-58, 2009.
    [10]
    V. Hodge, J. Austin. A survey of outlier detection methodologies. Artificial Intelligence Review, vol. 22, no. 2, pp. 85-126, 2004.
    [11]
    Y. Zhang, N. A. S. Hamm, N. Meratnia, A. Stein, M. V. D. Voort, P. J. M. Havinga. Statistics-based outlier detection for wireless sensor networks. International Journal of Geographical Information Science, vol. 26, no. 8, pp. 1373-1392, 2012.
    [12]
    S. Suthaharan, M. Alzahrani, S. Rajasegarar, C. Leckie, M. Palaniswami. Labelled data collection for anomaly detection in wireless sensor networks. In Proceedings of the 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE, Brisbane, QLD, Australia, pp. 269-274, 2010.
    [13]
    M. Xie, S. Han, B. M. Tian, S. Parvin. Anomaly detection in wireless sensor networks: A survey. Journal of Network and Computer Applications, vol. 34, no. 4, pp. 1302-1325, 2011.
    [14]
    L. J. Zhao, T. Y. Chai, D. C. Yuan. Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants. International Journal of Automation and Computing, vol. 9, no. 6, pp. 627-633, 2012.
    [15]
    F. T. Liu, K. M. Ting, Z. H. Zhou. Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data, vol. 6, no. 1, Article 3, 2012.
    [16]
    Z. G. Ding, M. R. Fei. An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window. In Proceedings of the 3rd IFAC International Conference on Intelligent Control and Automation Science, IFAC, Chengdu, China, vol. 3, pp. 12-17, 2013.
    [17]
    M. M. Breunig, H. P. Kriegel, R. T. Ng, J. Sander. LOF: Identifying density-based local outliers. In Proceedings of ACM SIGMOD Record on Management of Data, ACM, Dallas, USA, vol. 29, pp. 93-104, 2000.
    [18]
    W. G. Yi, J. Duan, M. Y. Lu. Double-layer bayesian classifier ensembles based on frequent itemsets. International Journal of Automation and Computing, vol. 9, no. 2, pp. 215-220, 2012.
    [19]
    D. E. Knuth. Art of Computer Programming, vol.4, fascicle 4: The Generating All Trees-History of Combinatorial Generation, AddisonWesley Professional, pp. 83-98, 2006.
    [20]
    S. C. Tan, K. M. Ting, T. F. Liu. Fast anomaly detection for streaming data. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence, vol. 2, pp. 1511-1516, 2011.
    [21]
    D. J. Hand, R. J. Till. A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine Learning, vol. 45, no. 2, pp. 171-186, 2001.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    用微信扫码二维码

    分享至好友和朋友圈

    Article Metrics

    Article views (5056) PDF downloads(2334) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return