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Mapping the Urban Wireless Landscape with Argos

Ian Rose, Matt Welshianrose@eecs.harvard.edu

mdw@eecs.harvard.edu

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Motivation

WiFi devices are extremely popular; usage continues to grow dramatically.

Wireless is increasingly pervasive – no longer just indoors – and diverse.

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Motivation

Suppose we had a global view of a city's wireless traffic... What kind of questions might we ask?

What are user's mobility patterns? How does traffic and usage vary by device type

(phone vs. laptop) or setting (cafe vs. bus)? How much malware is present in wireless

networks?

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The Big Picture

Deploy WiFi sniffers across a large urban area Sniffers capture wireless traffic, merge

individual traces into a global view, run custom user queries

Our deployment: CitySense network 26 sniffers in Cambridge, MA using wireless

mesh for network connectivity

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Hardware Implementation

SBC: Soekris net4826 or ALIX 2c2

CM9 2.4 GHz + 8 dBi antenna for sniffer

XR9 900 MHz + 6 dBi antenna for mesh

Power from streetlights or wall sockets

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Deployment

13 sniffers

9 sniffers

2 sniffers

2 sniffers

5 km

8.5 km

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Challenges

Poor packet capture rates from individual sniffers

Scalability, esp. regarding sniffer nodes' backhaul connectivity.

Monitored population is quite diverse, exhibits large temporal and spatial variance

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Privacy Concerns

There is an (obvious) big privacy concern here. One goal: understand privacy vs. research

tradeoffs Also, understand the capabilities of systems like

this (whether for “good” or for “evil”) -- what are the actual risks/dangers?

Identifying fields obfuscated by the system (IP address, MAC)

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Architecture: User Queries

User queries are expressed as a dataflow graph of packet processing operators.

Think Click Modular Router or stream processing engines

Let's consider a simple example: “stolen laptop finder”

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Architecture: Collecting Packets

Naive method:All sniffers stream captured packets to server for merging and user queries.

Sniffer network w/ wireless mesh backhaul:

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Architecture: Trace Merging

Goal: Obtain complete, network-wide view of captured traffic.

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Trace merging like this is pretty standard practice (e.g. Jigsaw, Wit, Yeo et al. '04)

In wired sniffer networks: all captured packets are collected at a central location for merging

Architecture: Trace Merging

Expensive or impossible to do with a low-bw backhaul!

How can we merge in the network?

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Architecture: In-Network Processing

Option #1: Centralized Merging Option #2: In-Network MergingThis reduces b/w somewhat, as it eliminates duplicates, but we can do much better!

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Architecture: User Queries

Split user queries into sniffer and server dataflows (similar to Wishbone [NSDI '09])

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Option #3: In-Network Merging and user queriesSo how does this help?

Big b/w savings by sending only query outputs back to server.(90% is common)

Architecture: In-Network Processing

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Main points: Merging packets in-network saves some b/w But the big savings come from running user

queries in-network A few complications glossed over here

(discussed in paper)

Architecture: In-Network Processing

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Architecture: Sniffer Nodes

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There are 11 radio channels (802.11b/g) We need channel policies to determine when to

change the radio channel

Architecture: Channel Management

When particularly interesting traffic is detected, sniffers can also recruit nearby sniffers to focus all on one channel to maximize capture

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Sample Query

Would not work right with packets from merged tap -- requires all (and only) locally captured packets.

Done!

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Performance Evaluation: Summary

In-network Traffic Processing leads to a more even distribution of traffic load over

network links; bottleneck links greatly reduced allows sniffer networks to scale to a greater offered

load (monitored population) Channel Focusing

increases network-wide capture of small windows of “interesting” traffic in some cases (no advantage in other cases)

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Performance Evaluation

In-network processing evaluated analytically 25 sniffers in grid Wired sink in center Variable # sources

(placed randomly) Empirically-derived

traffic model

Max. link load 8x higher in centralized case

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Case Studies

Popular websites and search patterns Malicious traffic Tracking public transportation services

Commuter trains Private bus lines

Wireless client fingerprinting

Popular websites and search patterns Malicious traffic Tracking public transportation services

Commuter trains Private bus lines

Wireless client fingerprinting

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Case Study: Train Tracking

LTRX_IBSS^@^@^@^@^@MuffinMITRA-PC_NetworkshafanaliSaleeqahpsetupBrooklineWirelessARTSHOPMBTA_WiFi_Coach0365_Box-076MBTA_WiFi_Coach0389_Box-180Free Public WiFiVerizon MiFi MNRGaneshLINKSYSMBTA_WiFi_Coach0227_Box-038MBTA_WiFi_Coachnnnn_Box-050Coach0385_Box-068skandoMBTA_Wifi_Coach1612_Box-143

LTRX_IBSS^@^@^@^@^@MuffinMITRA-PC_NetworkshafanaliSaleeqahpsetupBrooklineWirelessARTSHOPMBTA_WiFi_Coach0365_Box-076MBTA_WiFi_Coach0389_Box-180Free Public WiFiVerizon MiFi MNRGaneshLINKSYSMBTA_WiFi_Coach0227_Box-038MBTA_WiFi_Coachnnnn_Box-050Coach0385_Box-068skandoMBTA_Wifi_Coach1612_Box-143

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Case Study: Train Tracking

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Case Study: Train Tracking

From captured traffic, try to predict: when trains passed by their direction of travel

Use published train schedule as “gold standard” (probably not 100% accurate!)

Over a 24 hour test, all 34 trains detected successfully

time estimates usually accurate to within ~5 min. direction estimates: 25 correct, 4 incorrect

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Case Study: User Fingerprinting

WiFi devices send Probe Requests to search for known networks

By capturing these and geolocating the named networks (via www.wigle.net) we can fingerprint user's movements

Rank Unique Nets Locatable1 7431 49 282 87 48 113 370 46 104 632 47 105 120 47 0

Probe ReqsTulsa OKChicago ILUKBelgium

trainsOregonMass.Austin TX

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Related Work

Wardriving: wide spatial coverage, but no temporal coverage (Akella et al. - MobiCom '05, Han et al. - IMC '08)

Dense indoor monitoring: good temporal coverage and high capture fidelity, but limited spatial coverage (Jigsaw & Wit – Sigcomm '06)

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Conclusions

Urban wireless capture is a difficult business – Argos shows that the technique is possible via: in-network merging & user queries to reduce traffic intelligent 802.11 channel control

Our case studies demonstrate Argos' utility, but many more opportunities exist

Future work: improved anonymity guarantees other sniffer types (vehicular, mobile phone, etc.)

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Ian Roseianrose@eecs.harvard.eduhttp://eecs.harvard.edu/~ianrose

Matt Welshmdw@eecs.harvard.eduhttp://eecs.harvard.edu/~mdw

Thanks!