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Who Is Peeping at Your Passwords at Starbucks?

To Catch an Evil Twin Access Point

DSN 2010Yimin Song, Texas A&M UniversityChao Yang, Texas A&M UniversityGuofei Gy, Texas A&M University

Agenda

2

Introduction

Analysis

Algorithm

Evaluation

Conclusion

Agenda

3

Introduction• Wireless Network Review• Evil Twin Attack

Analysis

Algorithm

Evaluation

Conclusion

Wireless Network Review

4

Wireless terminology• AP – Access Point• SSID – Service Set Identifier• RSSI – Received Signal Strength Indication

BSS 1

BSS 2

Internet

hub, switchor routerAP

AP

802.11 CSMA/CA• DIFS – Distributed Inter-Frame Spacing• SIFS – Short Inter-Frame Spacing• BF – Random Backoff Time

sender receiver

BF

data

SIFS

ACK

DIFS

Evil Twin Attack

5

A phishing Wi-Fi AP that looks like a legitimate one (with the same SSID name).

Typically occurred near free hotspots, such as airports, cafes, hotels, and libraries.

Hard to trace since they can be launched and shut off suddenly or randomly, and last only for a short time after achieving their goal.

Evil Twin Attack (cont.)

6

Related work• Monitors radio frequency airwaves and/or

additional information gathered at router/switches and then compares with a known authorized list.

• Monitors traffic at wired side and determines if a machine uses wired or wireless connections. Then compare the result with an authorization list to detect if the associated AP is a rogue one.

Agenda

7

Introduction

Analysis• Network Setting in This Model• Problem Description• Server IAT (Inter-packet Arrival Time)

Algorithm

Evaluation

Conclusion

Network Setting in This Model

8

Table 1: Variables and settings in this model

Problem Description

9

An evil twin typically still requires the good twin for Internet access. Thus, the wireless hops for a user to access Internet are actually increased.

Fig. 1: Illustration of the target problem in this paper

• What statistics can be used to effectively distinguish one-hop and two-hop wireless channels on user side?

• Are there any dynamic factors in a real network environment that can affect such statistics?

• How to design efficient detection algorithms with the consideration of these influencing factors?

Server IAT

10

Server IAT (cont.)

11Fig. 2: Server IAT illustration in the normal AP scenario

Server IAT (cont.)

12Fig. 2: Server IAT illustration in the normal AP scenario

Server IAT (cont.)

13

Server IAT (cont.)

14

Fig. 5: IAT distribution under RSSI=50%

Fig. 4: IAT distribution under RSSI=100%

Agenda

15

Introduction

Analysis

Algorithm• TMM (Trained Mean Matching Algorithm)• HDT (Hop Differentiating Technique)• Improvement by Preprocessing

Evaluation

Conclusion

TMM

16

Trained Mean Matching Algorithm (TMM) requires knowing the distribution of Server IAT as a prior knowledge.

Given a sequence of observed Server IATs, if the mean of these Server IATs has a higher likelihood of matching the trained mean of two-hop wireless channels, we conclude that the client uses two wireless network hops to communicate with the remote server indicating a likely evil twin attack, and vice versa.

TMM (cont.)

17

TMM (cont.)

18

TMM (cont.)

19

HDT

20

HDT (cont.)

21

Fig. 2: Server IAT illustration in the normal AP scenario

Fig. 6: 6-AP IAT illustration in the normal AP scenario

HDT (cont.)

22

HDT (cont.)

23

Improvement by Preprocessing

24

Agenda

25

Introduction

Analysis

Algorithm

Evaluation• Environment Setup• Datasets• Effectiveness• Cross Validation

Conclusion

Environment Setup

26

Fig. 8: Environment for evil twin APFig. 7: Environment for normal AP

Datasets

27

Range A B+ B- C+ C- D E

Upper 100% 80% 70% 60% 50% 40% 20%

Lower 80% 70% 60% 50% 40% 20% 0%

Algorithm Protocol A B+ B- C+ C- D

HDT802.11g 0.8% 0.86% 3.91% 3.72% 4.69% 7.09%

802.11b 1.38% 1.44% 5.61% 6.17% 9.42% 10.36%

TMM802.11g 0.62% 0.68% 2.59% 2.66% 3.30% 6.02%

802.11b 0.99% 1.04% 3.33% 4.72% 7.44% 8.29%

Table 3: The percentage of filtered packets

Table 2: RSSI ranges and corresponding levels

Effectiveness

28

Table 5: False positive rate for HDT and TMM

Table 4: Detection rate for HDT and TMM

Algorithm Protocol A B+ B- C+ C- D

HDT802.11g 99.08% 98.72% 93.53% 94.31% 87.29% 81.39%

802.11b 99.92% 99.99% 99.96% 99.95% 96.05% 94.64%

TMM802.11g 99.39% 99.97% 99.49% 99.5% 98.32% 94.36%

802.11b 99.81% 95.43% 94.81% 96.09% 91.94% 85.71%

Algorithm Protocol A B+ B- C+ C- D

HDT802.11g 2.19% 1.41% 2.06% 1.93% 2.48% 6.52%

802.11b 8.39% 8.74% 5.39% 6.96% 5.27% 5.15%

TMM802.11g 1.08% 1.76% 1.97% 1.48% 1.75% 1.73%

802.11b 0.78% 1% 1.07% 1.27% 6.65% 7.01%

Effectiveness (cont.)

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Fig. 9: Cumulative probability of the number of decision rounds for HDT to output a correct result

Effectiveness (cont.)

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Table 7: False positive rate when number of input data in one decision round is 50

Table 6: Detection rate when number of input data in one decision round is 50

Algorithm Protocol A B+ B- C+ C- D

multi-HDT802.11g 99.62% 100% 100% 99.95% 100% 100%

802.11b 100% 100% 100% 100% 100% 100%

multi-TMM802.11g 100% 99.11% 98.73% 99.88% 95.83% 88%

802.11b 100% 100% 100% 100% 100% 100%

Algorithm Protocol A B+ B- C+ C- D

multi-HDT802.11g 0% 0.77% 0% 0% 0% 0%

802.11b 0% 0.03% 0.02% 0.11% 0.73% 0.1%

multi-TMM802.11g 0% 0.96% 0.16% 0.13% 0.55% 0.96%

802.11b 0% 1.07% 1.16% 1.02% 1.36% 1.41%

Table 7: False positive rate when number of input data in one decision round is 100

Effectiveness (cont.)

31

Fig. 10: Detection rate for multi-HDT using different numbers of input data in one decision round

Cross Validation

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Fig. 11: Detection rate for TMM under different RSSI ranges

Cross Validation (cont.)

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Fig. 12:Detection rate under different 802.11g networks

Cross Validation (cont.)

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Fig. 13: False positive rate under different 802.11g networks

Agenda

35

Introduction

Analysis

Algorithm

Evaluation

Discussion and Conclusion• Discussion• Conclusion

Discussion

36

More wired hops?• Several studies showed that the delays from the

wired link is not comparable to those in the wireless link.

• We can trade-off for more decision rounds.• Use a server within small hops.• Maybe use techniques similar to “traceroute” to

know the wired transfer time and then exclude/subtract them to minimize the noisy effect at wired side.

Discussion (cont.)

37

Will attacker increase IAT to avoid detection?• Users don’t like a slow connection.

Eq. 1: Attacker may delay the packet to reduce the SAIR

What if some evil twin AP connect to wired network instead of using normal AP?• That’s our future work.

Conclusion

38

We propose TMM and HDT to detect evil twin attack where TMM requires trained data and HDT doesn’t.

HDT is particularly attractive because it doesn’t rely on trained knowledge or parameters, and is resilient to changes in wireless environments.

The End