id: 06035792
dt: j
an: 06035792
au: Zhang, Ji; Gao, Qigang; Wang, Hai; Wang, Hua
ti: Detecting anomalies from high-dimensional wireless network data streams: a
    case study.
so: Soft Comput. 15, No. 6, 1195-1215 (2011).
py: 2011
pu: Springer-Verlag, Berlin
la: EN
cc: 
ut: outlier detection; high-dimensional data; subspaces; data streams
ci: 
li: doi:10.1007/s00500-010-0575-1
ab: Summary: In this paper, we study the problem of anomaly detection in
    wireless network streams. We have developed a new technique, called
    Stream Projected Outlier deTector (SPOT), to deal with the problem of
    anomaly detection from multi-dimensional or high-dimensional data
    streams. We conduct a detailed case study of SPOT in this paper by
    deploying it for anomaly detection from a real-life wireless network
    data stream. Since this wireless network data stream is unlabeled, a
    validating method is thus proposed to generate the ground-truth results
    in this case study for performance evaluation. Extensive experiments
    are conducted and the results demonstrate that SPOT is effective in
    detecting anomalies from wireless network data streams and outperforms
    existing anomaly detection methods.
rv: