EAGER: Detection and Mitigation of Pilot Contamination Attacks and Related Issues in Massive MIMO Systems

PI: J.K. Tugnait, 10/01/16 -- 9/30/18, $200,000.
Supported by the National Science Foundation Grant ECCS-1651133

Mobile data traffic continues to grow at an exponential rate. To meet this data challenge, massive MIMO (multiple-input multiple-output) system technology has recently been proposed where the base station employs a large number of antennas, allowing many users to be served simultaneously. It is regarded as one of the key enablers of future 5G wireless systems. While recent prototypes have demonstrated its feasibility, many significant research challenges remain to be addressed before massive MIMO can be deployed. Successful operation of massive MIMO depends critically on knowledge of the channel state information between the base station and the end users. In practice this would be acquired during the training phase where the users send individual pilot signals to the base station. This phase is challenging due to a large number of end users which leads to pilot reuse causing pilot contamination, and due to vulnerability to attacks by malicious eavesdroppers who may spoof legitimate users by transmitting identical pilot signals. This project is focused on methods to detect and defend against pilot contamination attacks.

Novel, innovative approaches to detect and defend against pilot contamination attacks from active eavesdroppers as well as from spoofing relays are investigated in this research. A key transformative idea introduced is that of self-contamination of pilot sequences by legitimate users to facilitate detection of active eavesdroppers. This project explores various ramifications of this idea in the context of the following research thrusts. (1) Detection of pilot contamination attacks by active eavesdroppers: Assuming the knowledge of the set of training sequences (and nothing else), can one detect if one or more training sequences are under attack? Source enumeration methods based on data correlation function are being exploited. (2) Joint acquisition of channel state information for both legitimate users and eavesdroppers to facilitate effective beamforming designs to enhance reception at legitimate users while degrading reception at eavesdroppers. (3) Detection and mitigation of active eavesdropping via spoofing relay attack where a spoofing relay operates in a full-duplex mode and simply amplifies and forwards the signal from a legitimate user to the base station in a time-division duplex uplink operation.

Author: Jitendra K. Tugnait: tugnajk@eng.auburn.edu

Date of latest revision: wed aug 3 2016