  In this article, physical layer security (PLS) in an intelligent reflecting
surface (IRS) assisted multiple-input multiple-output multiple antenna
eavesdropper (MIMOME) system is studied. In particular, we consider a practical
scenario without instantaneous channel state information (CSI) of the
eavesdropper and assume that the eavesdropping channel is a Rayleigh channel.
To reduce the complexity of currently available IRS-assisted PLS schemes, we
propose a low-complexity deep learning (DL) based approach to design
transmitter beamforming and IRS jointly, where the precoding vector and phase
shift matrix are designed to minimize the secrecy outage probability.
Simulation results demonstrate that the proposed DL-based approach can achieve
a similar performance of that with conventional alternating optimization (AO)
algorithms for a significant reduction in the computational complexity.
