TY - GEN
T1 - Pilot Contamination Attack Detection in 5G Massive MIMO Systems Using Generative Adversarial Networks
AU - Banaeizadeh, Fatemeh
AU - Barbeau, Michel
AU - Garcia-Alfaro, Joaquin
AU - Kranakis, Evangelos
AU - Wan, Tao
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Reliable and high throughput communication in Massive Multiple-Input Multiple-Output (MIMO) systems strongly depends on accurate channel estimation at the Base Station (BS). However, the channel estimation process in massive MIMO systems is vulnerable to pilot contamination attacks, which not only degrade the efficiency of channel estimation, but also increase the probability of information leakage. In this paper, we propose a defence mechanism against pilot contamination attacks using a deep-learning model, namely Generative Adversarial Networks (GAN), to detect invalid uplink connections at the BS. Training of the models is performed via legitimate data, which consists of received signals from valid users and real channel matrices. The simulation results show that the proposed method is able to detect the pilot contamination attack with 98% accuracy in the best scenario.
AB - Reliable and high throughput communication in Massive Multiple-Input Multiple-Output (MIMO) systems strongly depends on accurate channel estimation at the Base Station (BS). However, the channel estimation process in massive MIMO systems is vulnerable to pilot contamination attacks, which not only degrade the efficiency of channel estimation, but also increase the probability of information leakage. In this paper, we propose a defence mechanism against pilot contamination attacks using a deep-learning model, namely Generative Adversarial Networks (GAN), to detect invalid uplink connections at the BS. Training of the models is performed via legitimate data, which consists of received signals from valid users and real channel matrices. The simulation results show that the proposed method is able to detect the pilot contamination attack with 98% accuracy in the best scenario.
KW - Generative Adversarial Network
KW - Massive MIMO
KW - Network Security
KW - Pilot Contamination Attack
U2 - 10.1109/MeditCom49071.2021.9647674
DO - 10.1109/MeditCom49071.2021.9647674
M3 - Conference contribution
AN - SCOPUS:85124453994
T3 - 2021 IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2021
SP - 479
EP - 484
BT - 2021 IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2021
Y2 - 7 September 2021 through 10 September 2021
ER -