Abstract
WiFi networks are vulnerable to rogue AP attacks in which an attacker sets up an imposter AP to lure mobile users to connect. The attacker can eavesdrop on the communication, severely threatening users' privacy. Existing rogue AP detection solutions are confined to some specific attack scenarios (e.g., by relaying the traffic to a target AP) or require additional hardware. In this paper, we propose a crowdsensing based approach, named CRAD, to detect rogue APs in camouflage without specialized hardware requirement. CRAD exploits the spatial correlation of RSS to identify a potential imposter, which should be at a different location from the legitimate one. The RSS measurements collected from the crowd facilitate a robust profile and minimize the inaccuracy effect of a single RSS value. As a result, CRAD can filter out abnormal samples sensed in the realtime by dynamically matching the profile. We evaluate our approach with both a public dataset and a real prototype. The results show that CRAD can yield 90% detection accuracy and precision with proper crowd presence, even when the rogue AP is launched close to the legitimate one (e.g., within 1m).
Original language | English |
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Title of host publication | Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017 |
Publisher | IEEE |
Pages | 2327-2332 |
Number of pages | 6 |
ISBN (Electronic) | 9781538617915 |
DOIs | |
Publication status | Published - 13 Jul 2017 |
Event | 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 - J.W. Marriott Hotel, Atlanta, United States Duration: 5 Jun 2017 → 8 Jun 2017 |
Conference
Conference | 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 |
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Country/Territory | United States |
City | Atlanta |
Period | 5/06/17 → 8/06/17 |
Keywords
- Mobile Crowdsensing
- Rogue AP
- RSS
- Wireless Networks
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Networks and Communications