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URCA: Pulling out Anomalies by their Root Causes Fernando Silveira and Christophe Diot.

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Presentation on theme: "URCA: Pulling out Anomalies by their Root Causes Fernando Silveira and Christophe Diot."— Presentation transcript:

1 URCA: Pulling out Anomalies by their Root Causes Fernando Silveira and Christophe Diot

2 URCA: Pulling out Anomalies by their Root Causes Presenter: Fernando Silveira UPMC and Technicolor Joint work with Christophe Diot Presented at INFOCOM 2010 – San Diego, USA

3 URCA: Pulling out Anomalies by their Root Causes Time Packet counts Traffic Anomaly Detection 3Friday, February 19, 2010 Traffic Data Alarm Anomaly Detector Anomaly Anomalous traffic

4 URCA: Pulling out Anomalies by their Root Causes Obtaining information about an anomaly’s cause. Automating root cause analysis is important… Manual analysis is tedious and error prone Study from Arbor Networks with 67 ISPs Average ISP observes ~ 19 anomalies/day … but it is also a hard problem. Most detectors do not provide any information beyond an alarm Root Cause Analysis of Traffic Anomalies 4Friday, February 19, 2010

5 URCA: Pulling out Anomalies by their Root Causes Anomaly detection methods with properties that facilitate root cause analysis tasks Anomaly classification Lakhina et al. - SIGCOMM’05 Based on clustering entropy residuals Limited to anomalies found in entropy Anomalous flow identification Schweller et al. - IMC’04, Li et al. - IMC’06 Based on reversible sketches Complexity of choosing and computing sketches Limited to anomalies found in sketches Related Work 5Friday, February 19, 2010

6 URCA: Pulling out Anomalies by their Root Causes Our Contribution 6Friday, February 19, 2010 URCA (Unsupervised Root Cause Analysis) a tool that finds an anomaly’s root cause can be used with different anomaly detectors It provides accurate and fast results: anomalies are analyzed as fast as they are detected (1-5 minutes)

7 URCA: Pulling out Anomalies by their Root Causes Outline 7Friday, February 19, 2010 Algorithms for URCA Performance Evaluation

8 URCA: Pulling out Anomalies by their Root Causes Source IPDestination IP Source PortDestination Port Source ASDestination AS Previous Hop ASNext Hop AS Incoming Router InterfaceOutgoing Router Interface 8 Our Approach Friday, February 19, 2010 URCA has two steps: anomalous flow identification root cause classification Our methods rely on flow features

9 URCA: Pulling out Anomalies by their Root Causes Step 1: Anomalous Flow Identification 9Friday, February 19, 2010 Traffic Data Alarm Anomaly Detector Filter 80/TCP 443/TCP 1614/TCP 22/TCP 53/UDP 25/TCP … Candidate Anomalous Flows Destination Port

10 URCA: Pulling out Anomalies by their Root Causes Flow Identification - Example 10Friday, February 19, 2010 Time Packet counts Output Interface (2 values) Destination AS (3 values) eth0 eth1 AS 3354 AS 1277 AS 2108 Candidate flows Normal flows Anomalous flows Normal flows Anomaly

11 URCA: Pulling out Anomalies by their Root Causes11 Visualizing Root Cause Flows Friday, February 19, 2010 Network scan Routing change

12 URCA: Pulling out Anomalies by their Root Causes Step 2: Root Cause Classification 12Friday, February 19, 2010 aaaabbccbc We compute metrics from each anomaly number of source IP’s, ASN’s, flow sizes, packet sizes, etc. Hierarchical Clustering known anomalies + 1 unknown Bootstrapping labels helped by visualization ?

13 URCA: Pulling out Anomalies by their Root Causes Outline 13Friday, February 19, 2010 Algorithms for URCA Performance Evaluation

14 URCA: Pulling out Anomalies by their Root Causes Traces from links in GEANT2 Anomalies obtained with the ASTUTE anomaly detector Experimental Methodology 14Friday, February 19, 2010

15 URCA: Pulling out Anomalies by their Root Causes Identification Accuracy - Trace A 15Friday, February 19, 2010

16 URCA: Pulling out Anomalies by their Root Causes Identification Accuracy - Traces B-F 16Friday, February 19, 2010 * 90-percentile averaged across traces

17 URCA: Pulling out Anomalies by their Root Causes Classification Accuracy - Trace A 17Friday, February 19, % Correct 15% Misclass. 15% Misclassified = 5% first occurrences of an event type + 10% routing changes mistaken for link failures 5% require visualization

18 URCA: Pulling out Anomalies by their Root Causes What you’ll find in the paper: Algorithms for both identification and classification Experimental evaluation with 6 traces URCA can be applied to other anomaly detectors Ongoing and Future Work: URCA with an EWMA-based detector Using other sources of data (e.g., routing data) Wrapping Up 18Friday, February 19, 2010

19 URCA: Pulling out Anomalies by their Root Causes Special thanks to: DANTE / GEANT2 - Ricardo UCLA - More information at: The End 19Friday, February 19, 2010

20 URCA: Pulling out Anomalies by their Root Causes Backup Slides 20Friday, February 19, 2010

21 URCA: Pulling out Anomalies by their Root Causes21 Classification results for ASTUTE Friday, February 19, 2010

22 URCA: Pulling out Anomalies by their Root Causes22 Classifying the Unknown ASTUTE Anomalies Friday, February 19, 2010

23 URCA: Pulling out Anomalies by their Root Causes23 Results with EWMA Friday, February 19, 2010


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