  This paper considers machine learning for physical layer security design for
communication in a challenging wireless environment. The radio environment is
assumed to be programmable with the aid of a meta material-based intelligent
reflecting surface (IRS) allowing customisable path loss, multi-path fading and
interference effects. In particular, the fine-grained reflections from the IRS
elements are exploited to create channel advantage for maximizing the secrecy
rate at a legitimate receiver. A deep learning (DL) technique has been
developed to tune the reflections of the IRS elements in real-time. Simulation
results demonstrate that the DL approach yields comparable performance to the
conventional approaches while significantly reducing the computational
complexity.
