TY - GEN
T1 - Pilot contamination attacks in massive MIMO systems
AU - Akgun, Berk
AU - Krunz, Marwan
AU - Koyluoglu, O. Ozan
N1 - Funding Information:
This research was supported in part by the National Science Foundation (grants # CNS-1409172, CNS-1513649, llP-1265960, and CNS-1617335) and by Qatar Foundation (grant # NPRP 8-052-2-029). Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the author(s) and do not necessarily reflect the views of the NSF and QF. 978-1-5386-0683-4/17/$31.00 @2017 IEEE
Funding Information:
This research was supported in part by the National Science Foundation (grants # CNS-1409172, CNS-1513649, llP-1265960, and CNS-1617335) and by Qatar Foundation (grant # NPRP 8-052-2-029).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/19
Y1 - 2017/12/19
N2 - We consider a single-cell massive multiple-input multiple-output (MIMO) system in which a base station (BS) with a large number of antennas simultaneously transmits to K single-antenna users in the presence of an attacker. Massive MIMO systems often operate in a time division duplexing (TDD) fashion. The BS estimates the channel state information (CSI) at receivers based on their uplink pilot transmissions. Downlink transmission rates are highly dependent on these estimates, as the BS utilizes the CSI to exploit the beamforming gain offered by massive MIMO. However, this CSI estimation phase is vulnerable to malicious attacks. Specifically, an attacker can contaminate the uplink pilot sequences by generating identical pilot signals to those of legitimate users. We formulate a denial of service (DoS) attack in which the attacker aims to minimize the sum-rate of downlink transmissions by contaminating the uplink pilots. We also consider another attack model where the attacker generates jamming signals in both the CSI estimation and data transmission phases by exploiting in-band full-duplex techniques. We study these attacks under two power allocation strategies for downlink transmissions. Our analysis is conducted when the attacker knows or does not know the locations of the BS and users. When the attacker does not have perfect location information, stochastic optimization techniques are utilized to assess the impact of the attack. The formulated problems are solved using interior-point, Lagrangian minimization, and game-theoretic methods. We obtain a closed-form solution for a special case of the problem. Our results indicate that even though the attacker does not have the perfect location information, proposed pilot contamination attacks degrade the throughput of a massive MIMO system by more than 50%, and reduce fairness among users significantly. In addition, we show that increasing the number of pilot symbols does not prevent the proposed attacks, if the BS uniformly allocates powers for downlink transmissions.
AB - We consider a single-cell massive multiple-input multiple-output (MIMO) system in which a base station (BS) with a large number of antennas simultaneously transmits to K single-antenna users in the presence of an attacker. Massive MIMO systems often operate in a time division duplexing (TDD) fashion. The BS estimates the channel state information (CSI) at receivers based on their uplink pilot transmissions. Downlink transmission rates are highly dependent on these estimates, as the BS utilizes the CSI to exploit the beamforming gain offered by massive MIMO. However, this CSI estimation phase is vulnerable to malicious attacks. Specifically, an attacker can contaminate the uplink pilot sequences by generating identical pilot signals to those of legitimate users. We formulate a denial of service (DoS) attack in which the attacker aims to minimize the sum-rate of downlink transmissions by contaminating the uplink pilots. We also consider another attack model where the attacker generates jamming signals in both the CSI estimation and data transmission phases by exploiting in-band full-duplex techniques. We study these attacks under two power allocation strategies for downlink transmissions. Our analysis is conducted when the attacker knows or does not know the locations of the BS and users. When the attacker does not have perfect location information, stochastic optimization techniques are utilized to assess the impact of the attack. The formulated problems are solved using interior-point, Lagrangian minimization, and game-theoretic methods. We obtain a closed-form solution for a special case of the problem. Our results indicate that even though the attacker does not have the perfect location information, proposed pilot contamination attacks degrade the throughput of a massive MIMO system by more than 50%, and reduce fairness among users significantly. In addition, we show that increasing the number of pilot symbols does not prevent the proposed attacks, if the BS uniformly allocates powers for downlink transmissions.
KW - Massive MEMO
KW - game theory
KW - physical layer security
KW - pilot contamination attack
KW - stochastic optimization
UR - http://www.scopus.com/inward/record.url?scp=85046533895&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046533895&partnerID=8YFLogxK
U2 - 10.1109/CNS.2017.8228655
DO - 10.1109/CNS.2017.8228655
M3 - Conference contribution
AN - SCOPUS:85046533895
T3 - 2017 IEEE Conference on Communications and Network Security, CNS 2017
SP - 1
EP - 9
BT - 2017 IEEE Conference on Communications and Network Security, CNS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Conference on Communications and Network Security, CNS 2017
Y2 - 9 October 2017 through 11 October 2017
ER -