Event Details
Adaptive Physical Layer Authentication using Machine Learning with Antenna Diversity
Presenter: Mohammed Hammouda
Supervisor:
Date: Fri, October 7, 2022
Time: 09:30:00 - 10:30:00
Place: ZOOM - Please see below.
ABSTRACT
Note: Please log in to Zoom via SSO and your UVic Netlink ID.
Join Zoom Meeting: https://uvic.zoom.us/j/85439846602?pwd=Q3JmRDhrZkY2b3ZUeUlWdjNyNGg5QT09
Meeting ID: 854 3984 6602
Password: 949869
One tap mobile
+16475580588,,85439846602#,,,,0#,,949869# Canada
+17789072071,,85439846602#,,,,0#,,949869# Canada
Dial by your location
+1 647 558 0588 Canada
+1 778 907 2071 Canada
Meeting ID: 854 3984 6602
Password: 949869
Find your local number: https://uvic.zoom.us/u/kd5anLa2eq
Abstract: The heterogeneous characteristics of wireless mobile networks within the Internet of things (IoT) create authentication challenges due to the large number of devices with diverse requirements and capabilities. Physical layer authentication (PLA) can provide solutions for this heterogeneous environment using wireless channel attributes. In the seminar, an adaptive physical layer authentication scheme is proposed using machine learning (ML). Antenna diversity at the receiver is exploited to increase the number of features to achieve a high authentication rate (AR). A one-class classifier support vector machine (OCC-SVM) is used with the magnitude and real and imaginary parts of the received signal at each receive antenna as features. One-class classification is a ML technique for outlier and anomaly detection which uses only legitimate training data. The sounding reference signal (SRS) in the 5G uplink radio frame is employed to obtain the features. The proposed scheme is evaluated in an urban environment under different mobility conditions. Results are presented which show that this scheme provides a high AR with sufficient antenna diversity.