Wi-Fi Activity in Open Environments: Tools, Measurements, and Analyses. Thomas Claveirole Ph.D. Defense February 26, 2010

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1 Wi-Fi Activity in Open Environments: Tools, Measurements, and Analyses Thomas Claveirole Ph.D. Defense February 26, 2010 Ana Cavalli Reviewer Prof. TÉLÉCOM & Management SudParis Thierry Turletti Reviewer INRIA Sophia Antipolis Khaldoun Al Agha Examiner Prof. Université Paris-Sud 11 Guillaume Chelius Examiner INRIA / ENS Lyon Marcelo Dias de Amorim Co-director CNRS / Université Pierre et Marie Curie Serge Fdida Co-director Prof. Université Pierre et Marie Curie

2 Wi-Fi networking Proliferation of Wi-Fi capable devices Laptop, PDAs But also... 2

3 Wi-Fi networking Proliferation of Wi-Fi capable devices Laptop, PDAs Cell phones, portable audio players, handheld game consoles Almost any electronic device (e.g., digital cameras, webcams, alarm clocks) 3 Wi-Fi / IEEE everywhere!

4 Motivations Study new Wi-Fi usages Investigate Better understanding of Wi-Fi in the wild Insight 4 new Wi-Fi enabled environments for improving protocols/applications

5 Existing techniques: intrusive measurements Access point logs / SNMP data (e.g., Kim et al., Infocom 2006) Wired backbone tapping (e.g., Henderson et al., MobiCom 2004) 5

6 Existing techniques: intrusive measurements Access point logs / SNMP data (e.g., Kim et al., Infocom 2006) Wired backbone tapping (e.g., Henderson et al., MobiCom 2004) Focus on a single network Need to access the infrastructure Impractical in many places 6

7 Existing techniques: Wi-Fi sniffing Sniffers Listen spread over an area to all surrounding networks AP1 AP1 SS AP2 AP2 7

8 Existing techniques: Wi-Fi sniffing Sniffers Listen Used spread over an area to all surrounding networks for Protocol analysis (e.g., Romdhanne et al., ROADS 2009, Mahajan et al., SIGCOMM 2006) AP1 AP1 SS Problem diagnosis (e.g., Cheng et al., SIGCOMM 2007) Security (e.g., Bahl et al., MobiSys 2006) 8 AP2 AP2

9 Wi-Fi sniffing Features Entirely passive AP1 AP1 AP2 AP2 Non-intrusive Network equipment Network operation No S1 S1 AP3 AP3 infrastructure needed S2 S2 S3 S3 9

10 Outline Wi-Fi sniffing: issues Contribution 1 Tools for Wi-Fi sniffing: WiPal Contribution 2 Study of Wi-Fi sniffing accuracy Contribution 3 Study of Wi-Fi activity in 3 environments 10

11 Wi-Fi sniffing: issues 11

12 Issue 1 Sniffing accuracy PP11 SS AP AP PP11 PP11 Ideally: AP sends P1 to user S hears P1 User receives P1 12

13 Issue 1 Sniffing accuracy?? SS AP AP PP22 PP22 In practice: AP sends P2 to user S misses P2 User receives P2 13

14 Issue 1 Sniffing accuracy?? SS AP AP PP33?? In practice: AP sends P3 to user S misses P3 User misses P3 14

15 Issue 1 Sniffing accuracy PP44 SS AP AP PP44?? In practice: AP sends P4 to user S hears P4 User misses P4 15

16 Issue 1 Sniffing accuracy PP55 SS AP AP?? PP55 In practice: User sends P5 to AP S hears P5 AP misses P5 16

17 Issue 1 Sniffing accuracy SS AP AP PP11 PP11 PP22 PP11 PP22 PP33 PP44 PP44 PP55 17 PP55

18 Issue 2 Trace merging Goal: only one trace Global vision of all network traffic Temper frame losses for individual sniffers 18

19 Issue 2 Trace merging Complex process Accurate synchronization Identify duplicates 19

20 Contribution 1 Tools for Wi-Fi sniffing: WiPal WiPal: Efficient Offline Merging of IEEE Traces Mobile Computing and Communications Review, 2010 WiPal and WScout: Two Hands-on Tools for Wireless Packet Traces Manipulations and Visualization (demo) ACM Mobicom Workshop on Wireless Networks Testbeds (WiNTECH), 2008 Manipulating Wi-Fi Packet Traces with WiPal: Design and Experience Submitted to Software: Practice and Experience,

21 WiPal Overview C++ library and programs to Manipulate Wi-Fi packet traces Develop new packet processing programs 21

22 WiPal Overview C++ library and programs to Manipulate Wi-Fi packet traces Develop new packet processing programs Includes Hi-level features, e.g., statistics extraction, anonymization, trace merging Low-level features, e.g., frame parsing, trace I/O An original design and innovative features 22

23 Trace merging Overview T1 T2 1. Reference frames appear in both traces Reference frames 23

24 Trace merging Overview T1 T2 T1 T2 1. Reference frames appear in both traces 2. They enable traces synchronization Reference frames 24 Synchro.

25 Trace merging Overview T1 T2 T1 T2 Output 1. Reference frames appear in both traces 2. They enable traces synchronization 3. Compare timestamps to merge Reference frames 25 Synchro. Merging

26 WiPal Trace synchronization Synchronization function: map T1's clock to T2's T2's clock T1's clock Reference frames 26

27 WiPal Trace synchronization Synchronization function: map T1's clock to T2's T2's clock T1's clock Reference frames 27

28 WiPal Trace synchronization Synchronization function: map T1's clock to T2's T2's clock? T1's clock Reference frames 28

29 WiPal Trace synchronization Synchronization function: map T1's clock to T2's T2's clock Work T1's clock Synchronization window or 29 Reference frames on sliding windows of reference frames

30 WiPal Trace synchronization Synchronization T2's clock function: map T1's clock to T2's Ref. frames window Work T1's clock or 30 Reference frames on sliding windows of reference frames

31 WiPal Trace synchronization Synchronization function: map T1's clock to T2's T2's clock Work T1's clock on sliding windows of reference frames Use or 31 Reference frames linear regressions

32 WiPal Trace synchronization Synchronization function: map T1's clock to T2's T2's clock Work T1's clock on sliding windows of reference frames Use or 32 Reference frames linear regressions

33 WiPal Trace synchronization Synchronization function: map T1's clock to T2's T2's clock Work T1's clock on sliding windows of reference frames Use or 33 Reference frames linear regressions

34 WiPal Evaluation 2 criteria Correctness Synchronization accuracy 34

35 WiPal Evaluation 2 criteria Correctness Synchronization accuracy 4 datasets, 4 traces each (17.5 GB total) Different sniffing conditions Environment Hardware Channel 8 35 merge operations

36 WiPal Evaluation Correctness (1/2) Some Real tests using synthetic traces traces: hard to check (no ground-truth) Inconsistencies when merging real traces Look for duplicate data / beacons / probe resp. 1 anomaly (implies at most 4 frames out of 79,000,000) 36

37 WiPal Evaluation Correctness (2/2) 37

38 WiPal Evaluation Correctness (2/2) Linear interpolation Incorrect Larger window sizes Incorrect Best values:

39 WiPal Evaluation Synchronization Linear interpolation worst results Larger window sizes lack precision WiPal Windows of 4 39

40 WiPal Conclusion WiPal packet traces processing framework Designed An for speed and user-friendliness original merging tool More correct than other tools More accurate than other tools 40

41 Contribution 2 Study of sniffing accuracy On the Completeness of Wireless Packet Sniffing Conditional acceptance to IEEE Communications Letters,

42 Accuracy Evaluating completeness Wi-Fi defines valid frame sequences Assume conform implementations Invalid sequence missing frame Detect invalid sequences, infer missing frames 42

43 Accuracy Evaluating completeness Wi-Fi defines valid frame sequences Assume conform implementations Invalid sequence missing frame Detect invalid sequences, infer missing frames Evaluation 43 techniques: 2 categories Frame types data data ack. ack. data? data? ack. ack.

44 Accuracy Evaluating completeness Wi-Fi defines valid frame sequences Assume conform implementations Invalid sequence missing frame Detect invalid sequences, infer missing frames Evaluation 44 techniques: 2 categories Frame types Seq. numbers data data n 42 n 42 ack. ack. n 43? n 43? data? data? ack. ack. n 44? n 44? n 45 n 45

45 Accuracy Experiment setups Experiment 1: sniffers All sniffers on a single desktop Duration 1h Experiment 2: sniffers All sniffers in the same room (grouped by 2) Duration 2h 45

46 Accuracy Evaluation issues Wrong assumption: missing frame sniffer loss 46

47 Accuracy Evaluation issues Wrong assumption: missing frame sniffer loss Wrong assumption: Wi-Fi compliance Sequence numbers often incorrect *: at least one device 5% bias 47

48 Accuracy Evaluation issues Wrong assumption: missing frame sniffer loss Wrong assumption: Wi-Fi compliance Sequence numbers often incorrect *: at least one device 5% bias Frame-type, limitation: PP11 cannot detect loss bursts data? data? ack.? ack.? sigcomm2004: >59% of missing frames *: >80% 48 P P22

49 Accuracy Using multiple sniffers Extract subsets with 1, 2,, N sniffers Compare For subsets each subset of size n, study New frames compared to n = n - 1 Missing frames compared to n = N 49

50 Accuracy Results Most frames shared among sniffers But all sniffers have original frames! High variability Legend Legend Box size: number of frames Red part: original frames 50

51 Accuracy Conclusion Completeness evaluation Frame-type techniques: unreliable Seq. number techniques: need caution Using multiple sniffers Frame loss unavoidable High variability / randomness 51

52 Contribution 3 Study of Wi-Fi activity in 3 environments An Empirical Analysis of Wi-Fi Activity in Three Urban Scenarios PerCom Workshop on Pervasive Wireless Networking (PWN), 2009 Sniffing IEEE Mobility AdHoc-NOW PhD Workshop,

53 Wi-Fi activity Trace collection Office building 3 days Sparse residential area 3 days Dense residential area 10 days High car/pedestrian traffic 53

54 Wi-Fi activity Device discovery 54

55 Wi-Fi activity Device discovery Uneven 55 distribution among channels

56 Wi-Fi activity Device discovery Uneven distribution among channels Day-night 56 pattern

57 Wi-Fi activity Device discovery Uneven distribution among channels Day-night Constant 57 pattern rate during long periods

58 Wi-Fi activity Active durations 58

59 Wi-Fi activity Active durations Uneven distribution among channels 59

60 Wi-Fi activity Active durations Uneven distribution among channels Uneven distribution of durations 60

61 Wi-Fi activity Active durations Uneven distribution among channels Uneven distribution of durations Most devices nearly inactive 61

62 Wi-Fi activity Conclusion Wireless A traces in 3 distinct environments number of expected results Still, a few unexpected behaviors Device growth Few regular users Environment 62 matters!

63 Conclusion WiPal: software tools for packet processing Used by: UCLA, St. Andrews, Keio, UFRJ Study of Wi-Fi sniffing accuracy Issues with existing techniques Accuracy of using multiple sniffers Study of Wi-Fi activity in 3 environments Some unexpected behaviors Environment matters 63

64 Perspectives Extend WiPal Investigate Extend 64 completeness issues analyses of Wi-Fi usages

65 Support slides 65

66 Processing traces: issues Packet processing programs Not generic enough, or slow Examples Wireshark Efficient, understands many protocols Export a library Dedicated to visualization and statistics Scapy Understand many protocols Generic, designed as a Python library Slow, unpractical on large traces (>1GB) 66

67 WiPal Merging (simplified) T1 T2 Output Use a pair of pointers Advance in both traces simultaneously Identify duplicates with content arrival time (±106 μs) 67

68 WiPal Merging (simplified) T1 T2 Output a pair of pointers Advance in both traces simultaneously Use Identify duplicates with content arrival time (±106 μs) 68

69 WiPal Merging (simplified) T1 T2 Output Use a pair of pointers Advance in both traces simultaneously Identify duplicates with content arrival time (±106 μs) 69

70 WiPal Merging (simplified) T1 T2 Output Use a pair of pointers Advance in both traces simultaneously Identify content arrival time (±106 μs) 70 duplicates with

71 WiPal Merging (simplified) T1 T2 Output Use a pair of pointers Advance in both traces simultaneously Identify content arrival time (±106 μs) 71 duplicates with

72 WiPal evaluation Merging speed WiPal 35 minutes to merge all datasets Wit (another offline merger) Use a database backend 8h 20 minutes just to import the datasets 1 import using Wit = 14 merges using WiPal Other 72 software More assumptions unfair comparison

73 Accuracy Results 73

74 Wi-Fi activity Datasets Duration Size Data size Devices ESSIDs Access points Ad Hoc cells Ad Office 3d 10h GB 3.43 GB Residential, sparse Residential, dense 3d 12h 10d 15h 3.67 GB 1.61 GB 4.75 MB MB Hoc cells ill-configured devices Hidden SSIDs / ISPs some oddities Sparse trace bigger than dense trace 74

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