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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-estanAuthors: 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/

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Organization

• Motivation• Traffic Clusters• Algorithms

– Unidimensional– Multidimensional

• AutoFocus• Experimental Results• Conclusions

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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

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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

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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

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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 )

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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

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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

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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

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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

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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

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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)

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Unidimensional report example

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

15 35 30 40 160 110 35 75

10.0.0.2/31 10.0.0.4/3150 10.0.0.8/31 10.0.0.10/31

70 270 35 75

10.0.0.0/30 10.0.0.4/30 10.0.0.8/30 7530550 70

10.0.0.0/29 10.0.0.8/29120 380

10.0.0.0/28 500500

120 380

305

270

160 110

HierarchyThreshold=100

10.0.0.14/31

10.0.0.12/30

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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

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270

120

500

305

380

160 110

Unidimensional report example

10.0.0.8 10.0.0.9

10.0.0.0/29 10.0.0.8/29

10.0.0.8/31

10.0.0.8/30

10.0.0.0/28

120 380

160 110

Compression

305-270<100

380-270≥100

Source IP Traffic

10.0.0.0/29

120

10.0.0.8/29

380

10.0.0.8 160

10.0.0.9 110

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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

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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

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Multidimensional Structure Example

All traffic All traffic

US EU

CA NY GB DE

Web Mail

Source net Application

US Web

Nodes (clusters) have multiple parents

US

Web

Nodes (clusters) overlap

CA

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Another way to understand multi-dim. case

x hierarchyy h

iera

rchy

“leaf”nodes

“root”node

decreasing x-specificity

decr

easi

ng y

-speci

fici

ty

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Pruning the hierarchy

x hierarchyy h

iera

rchy

significant 1d nodes potentially significant 2d nodes

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Multidimensional Compute

• For each cluster, examine n flows and report those above the threshold

• Can’t do brute force – ~ n 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

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Greedy compression algorithm

•Add estimate counters of children along all

dimensions

•Set this node’s estimate to largest of

sums

•If above threshold, report

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System: AutoFocus

Trafficparser

Web basedGUI

Cluster miner

Grapher

Packet header trace

categories

names

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Experience

• Small network exchange point – SD-NAP

• Slammer Worm

•Worm traffic separated (black)

Uncharacterized spike in “other” category indicates

something fishy

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Analysis of unusual events

• UCSD to UCLA route change• Sapphire/SQL Slammer worm

Site 2

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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