Searching for Unknown Anomalies in Hierarchical Data Streams

Tomer Gafni, Kobi Cohen, Qing Zhao

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء

ملخص

We consider the problem of anomaly detection among a large number of processes, where the probabilistic models of anomalies are unknown. At each time, aggregated noisy observations can be taken from a chosen subset of processes, where the chosen subset conforms to a tree structure. The observation distribution depends on the chosen subset and the absence/presence of anomalies. We develop a sequential search strategy using a hierarchical Kolmogorov-Smirnov (KS) statistics. Referred to as Tree-based Anomaly Search using KS statistics (TASKS), the proposed strategy is order-optimal with respect to the size of the search space and the detection accuracy.

اللغة الأصليةإنجليزيّة أمريكيّة
الصفحات (من إلى)1774-1778
عدد الصفحات5
دوريةIEEE Signal Processing Letters
مستوى الصوت28
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 1 يناير 2021

All Science Journal Classification (ASJC) codes

  • !!Signal Processing
  • !!Electrical and Electronic Engineering
  • !!Applied Mathematics

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