Automatically Inferring Patterns of Resource Consumption in Network Traffic

1. Automatically Inferring Patterns of Resource Consumption in Network Traffic In Proceedings of SIGCOMM 2003 Reviewed By Michael Attig http://www.acm.org/sigs/sigcomm/sigcomm2003/papers.html#p137-estan Authors: Cristian Estan, Stefan Savage, George Varghese Note: Portions of this presentation are from the author’s SIGCOMM presentation slides available at: http://www.cs.ucsd.edu/~cestan/

2. Organization • Motivation • Traffic Clusters • Algorithms • Unidimensional • Multidimensional • AutoFocus • Experimental Results • Conclusions

3. Motivation • New applications  new traffic patterns • Network Admins need to know how being used • provide better service • Reconfigure network elements to recognize traffic model • Difficult to know how being used, must infer characteristics • Currently use pre-defined list of patterns to identify • This paper focuses on the standard 5-tuple • Allows comparison to Cisco’s FlowScan

4. Goals • Reduce the amount of detail in reports • Easier to read and act upon • Provide multiple dimensions to report / aggregate on • 1 dimension  lose info • Example: If aggregate on protocol or src port fields, may conclude p2p in wide use, when only small set of hosts responsible • Need for Automation - detect/contain Worms/DoS • Network pushback needs automated technique to distinguish malicious flows

5. How to report? • “top ten” • Could lose important info • Aggregate individual flows into common category (single dimension) • Choose wrong dimension  lose important characteristics • Aggregate on multiple fields (multiple dimensions) • Better • Dynamically define traffic clusters to report

6. Traffic Clusters • Traffic clusters are the multidimensional traffic aggregates identified by reports • Dimensionality • Detail • Utility • A cluster is defined by a range for each field • The ranges are from natural hierarchies (e.g. IP prefix hierarchy) – meaningful aggregates • Example • Traffic aggregate: incoming web traffic for a subnet • Traffic cluster: ( SrcIP=*, DestIP in 128.252.153.0/24, Proto=TCP, SrcPort=80, DestPort=high )

7. Traffic Reports • A list of clusters to be presented • Restrict what to report by volume • # bytes • # packets • Could restrict based on other criteria, they focus on volume

8. What Must be done to report? • Compute • Identify clusters with a traffic volume above threshold, H • Compress • Remove cluster C if can infer it’s traffic from C’ • Compare • show how traffic changes over time • Prioritize • Sort in terms of potential level of interest • Use unexpectedness metric

9. Algorithms • Core of AutoFocus tool • k = number of fields • Sets for each field form a natural hierarchy • parent always smallest superset of children • Leaves are individual values the field had • Root is set of all possible values (*) • di = depth of hierarchy of the i-th of the k fields • s = T/H, where T = total traffic, H = threshold • Think of this as the number of reports you can have • n = number flow records of input • m = number distinct values of the fields in the n flow records

10. Data Source • Use simplified NetFlow records as raw data • Has key that specifies exact values for all 5 fields • Has two counters • One to count packets that matched key during measurement interval • One to count number of bytes in those packets

11. Unidimensional Compute • Clusters can overlap • Bad  Can have up to 1+20*25 = 501 reports • H = 5%  s = 20, hierarchy size is 25+1 • Only consider prefixes of size 32 down to 8 • Number of reports of clusters above H at most 1 + (d-1)s • Two approaches: • When number of sets in hierarchy small, apply a brute force approach: keep count for each set and traverse all n flow records • At end, list clusters who exceed H

12. Unidimensional Compute – Approach 2 • As go through flow records: • Build leaf nodes that correspond to IP addresses that actually appear in trace • Do trace updating counter of all leaf nodes • In second pass (post-order), determine which clusters over threshold H • Add traffic to that of its parent • Memory required is O(md) • Running time O(n + md)

13. Unidimensional report example 500 500 10.0.0.0/28 10.0.0.0/29 10.0.0.8/29 120 120 380 380 10.0.0.0/30 10.0.0.4/30 10.0.0.8/30 50 70 305 305 75 10.0.0.10/31 10.0.0.2/31 10.0.0.4/31 10.0.0.8/31 50 70 270 270 35 75 160 110 Threshold=100 Hierarchy 10.0.0.12/30 10.0.0.14/31 40 35 15 35 30 160 110 75 10.0.0.2 10.0.0.3 10.0.0.4 10.0.0.5 10.0.0.8 10.0.0.9 10.0.0.10 10.0.0.14

14. Unidimensional Compress • Reduce redundant info • Example: a /30 network may have bit more traffic than /31 it covers, but not much • Reporting the /30 adds nothing to the report • Define compression threshold C as amount by which a cluster can be off (used C=H) • Trade-off: accuracy versus reduced size • The number of clusters above the threshold in a non-redundant compressed report is at most s. • Brings reports of previous example down to 20 instead of 501

15. Unidimensional report example 500 10.0.0.0/28 120 380 305 10.0.0.8/30 270 10.0.0.8/31 160 110 Compression 380-270≥100 120 380 10.0.0.0/29 10.0.0.8/29 305-270<100 160 110 10.0.0.8 10.0.0.9

16. Unidimensional Compare • How has structure of traffic changed? • Absolute change • Number bytes or packets by which clusters increase/decrease • Only if measurement interval constant • Relative change • Comparison of traffic mixes over different measurement intervals • Must normalize for comparison • Do a pass over the trace to compute the exact traffic in both intervals • If estimate lower or larger by H than actual change, report - a compression step • Estimate based on reports already added to report

17. Unidimensional Prioritize • Unexpectedness • deviation from an uniform model • Example • 50% of all traffic is web • Node B receives 20% of all traffic • The web traffic received by node B is 15% instead of 50%*20%=10% • unexpectedness label is 15%/10%=150% • This is used to flag interesting data in a report

18. Multidimensional Structure Example Source net Application All traffic All traffic US EU Web Mail CA NY GB DE US Web Nodes (clusters) have multiple parents Nodes (clusters) overlap US CA Web

19. Another way to understand multi-dim. case y hierarchy decreasing x-specificity x hierarchy “leaf”nodes decreasing y-specificity “root”node

20. Pruning the hierarchy x hierarchy significant 1d nodes potentially significant 2d nodes y hierarchy

21. Multidimensional Compute • For each cluster, examine n flows and report those above the threshold • Can’t do brute force – ~ n P di clusters • Evaluate all clusters generated by n flows? Too many! • Restrict search based on optimizations that prune search space • Consider only if all unidimensional ancestors above H • Solve k unidimensional problems • Combine clusters

22. Greedy compression algorithm Add estimate counters of children along all dimensions Set this node’s estimate to largest of sums If above threshold, report

23. System: AutoFocus names categories Cluster miner Web based GUI Grapher Traffic parser Packet header trace

25. Experience • Small network exchange point – SD-NAP • Slammer Worm Worm traffic separated (black) Uncharacterized spike in “other” category indicates something fishy

26. Analysis of unusual events • UCSD to UCLA route change • Sapphire/SQL Slammer worm Site 2

27. Conclusions • Multidimensional traffic clusters using natural hierarchies describe traffic aggregates • Traffic reports using thresholding identify automatically conspicuous resource consumption at the right granularity • Compression produces compact traffic reports and unexpectedness labels highlight non-obvious aggregates • Their prototype system, AutoFocus, provides insights into the structure of regular traffic and unexpected events