TY - GEN
T1 - Secure RSS-fingerprint-based indoor positioning
T2 - 6th IEEE Conference on Communications and Network Security, CNS 2018
AU - Yuan, Lizhou
AU - Hu, Yidan
AU - Li, Yunzhi
AU - Zhang, Rui
AU - Zhang, Yanchao
AU - Hedgpeth, Terri
N1 - Funding Information:
ACKNOWLEDGMENT We would like to thank the anonymous reviewers for their insightful comments that help improve the quality of this paper. This work was supported in part by the US National Science Foundation under grants CNS-1700032, CNS-1700039, CNS1651954 (CAREER), CNS-1718078, CNS-1514381, CNS-1619251, CNS1421999, and CNS-1320906.
PY - 2018/8/10
Y1 - 2018/8/10
N2 - Indoor positioning systems (IPS) based on RSS fingerprints have received significant attention in recent years, but they are unfortunately vulnerable to RSS attacks that cannot be thwarted by conventional cryptographic means. In this paper, we identify two practical RSS attacks on RSS-fingerprint-based IPS (RSS-IPS. In both attacks, the attacker learns the RSS-fingerprint database at the IPS server by acting as a normal user repeatedly issuing location queries and then impersonates selected APs with fake ones under his control. By carefully tuning the locations and transmission power of fake APs, the attacker is able to control the RSS experienced by victim users at target locations, leading to either a large location error or the IPS server misled into returning a fake location of the attacker's choice. We further design a fingerprint-matching mechanism based on a novel truncated distance metric as the countermeasure. Trace-driven simulation studies based on real RSS measurement data demonstrate the severe impact of the proposed attacks and also the effectiveness of our countermeasure.
AB - Indoor positioning systems (IPS) based on RSS fingerprints have received significant attention in recent years, but they are unfortunately vulnerable to RSS attacks that cannot be thwarted by conventional cryptographic means. In this paper, we identify two practical RSS attacks on RSS-fingerprint-based IPS (RSS-IPS. In both attacks, the attacker learns the RSS-fingerprint database at the IPS server by acting as a normal user repeatedly issuing location queries and then impersonates selected APs with fake ones under his control. By carefully tuning the locations and transmission power of fake APs, the attacker is able to control the RSS experienced by victim users at target locations, leading to either a large location error or the IPS server misled into returning a fake location of the attacker's choice. We further design a fingerprint-matching mechanism based on a novel truncated distance metric as the countermeasure. Trace-driven simulation studies based on real RSS measurement data demonstrate the severe impact of the proposed attacks and also the effectiveness of our countermeasure.
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U2 - 10.1109/CNS.2018.8433131
DO - 10.1109/CNS.2018.8433131
M3 - Conference contribution
AN - SCOPUS:85052575153
SN - 9781538645864
T3 - 2018 IEEE Conference on Communications and Network Security, CNS 2018
BT - 2018 IEEE Conference on Communications and Network Security, CNS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 30 May 2018 through 1 June 2018
ER -