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1 Distributed Detection of Network-Wide Traffic Anomalies Ling Huang* XuanLong Nguyen* Minos Garofalakis § Joe Hellerstein* Michael Jordan* Anthony Joseph*

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Presentation on theme: "1 Distributed Detection of Network-Wide Traffic Anomalies Ling Huang* XuanLong Nguyen* Minos Garofalakis § Joe Hellerstein* Michael Jordan* Anthony Joseph*"— Presentation transcript:

1 1 Distributed Detection of Network-Wide Traffic Anomalies Ling Huang* XuanLong Nguyen* Minos Garofalakis § Joe Hellerstein* Michael Jordan* Anthony Joseph* Nina Taft § *UC Berkeley § Intel Research

2 Detection of Network-wide Anomalies A volume anomaly is a sudden change in an Origin-Destination flow: example Given link traffic measurements, diagnose the volume anomalies PCA approach to separate normal from anomalous traffic  Normal traffic is well approximated as occupying a low dimensional subspace

3 Traffic vector of all links at a particular point in time Normal traffic vector Residual traffic vector The Subspace Method Normal Subspace : space spanned by the first k principal components Anomalous Subspace : space spanned by the remaining principal components Then, decompose traffic on all links by projecting onto and to obtain: Where P is the matrix of top K eigenvectors

4 Detection Illustration Value of over time (all traffic) over time (SPE) Value of SPE at anomaly time points clearly stand out

5 Capture size of vector using squared prediction error: Assuming Gaussian data, we can find bounds which SPE should only exceed 1- % of the time Result due to [Jackson and Mudholkar, 1979] Detection Traffic on Link 1 Traffic on Link 2

6 Distributed Detection   aintain  1  accurate PCA decomposition   2 -accurate detection via distributed triggers

7 The Tracking Framework

8 Background: Matrix Norm

9 Background: Matrix Perturbation Theory Perturbation bound on eigenvalues

10 Background: Matrix Perturbation Theory II

11 For orthogonal projection, we have:

12 The Root Mean Square of Eigen Error

13 Individual Eigen Errors

14 Filtering Error and Δ Recall that monitors have the distributed m x n matrix

15 Filtering Error and Eigen Error Recall that

16 The F-Norm of Perturbation Error I The assumptions: Lemma:

17 The F-Norm of Perturbation Error II Lemma

18 The F-Norm of Perturbation Error III

19 Lemma

20 How Good is The Model? Slack

21 Application to Network Anomaly Detection


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