QIAN Yaguan, WU Chunming, YANG Qiang, WANG Bin. Network Traffic Anomaly Detection Based on Maximum Entropy Model[J]. Chinese Journal of Electronics, 2012, 21(3): 579-582.
Citation: QIAN Yaguan, WU Chunming, YANG Qiang, WANG Bin. Network Traffic Anomaly Detection Based on Maximum Entropy Model[J]. Chinese Journal of Electronics, 2012, 21(3): 579-582.

Network Traffic Anomaly Detection Based on Maximum Entropy Model

  • Received Date: 2011-07-01
  • Rev Recd Date: 2011-10-01
  • Publish Date: 2012-07-25
  • In this paper, a novel network traffic anomaly detection approach by adopting the Machine learning (ML) method based on Maximum entropy (ME) principle has been exploited. The final feature set is generated by extracting features from 1% of a public released dataset KDD 99 with Correlation-based feature selection (CFS) algorithm. The Bound-constrained limited memory variable metric (BLMVM) algorithm is employed to estimate the parameters to obtain an exponential model. The model is further studied in comparison with other ML methods. The proposed approach is assessed through a set of numerical experiments and the result demonstrates that the ME model exhibits enhanced classification efficiency for network traffic anomaly, even under the condition of training data with limited size.
  • loading
  • 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

    Article Metrics

    Article views (518) PDF downloads(1947) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return