  The development of reconfigurable intelligent surfaces (RIS) is a
double-edged sword to physical layer security (PLS). Whilst a legitimate RIS
can yield beneficial impacts including increased channel randomness to enhance
physical layer secret key generation (PL-SKG), malicious RIS can poison
legitimate channels and crack most of existing PL-SKGs. In this work, we
propose an adversarial learning framework between legitimate parties (namely
Alice and Bob) to address this Man-in-the-middle malicious RIS (MITM-RIS)
eavesdropping. First, the theoretical mutual information gap between legitimate
pairs and MITM-RIS is deduced. Then, Alice and Bob leverage generative
adversarial networks (GANs) to learn to achieve a common feature surface that
does not have mutual information overlap with MITM-RIS. Next, we aid signal
processing interpretation of black-box neural networks by using a symbolic
explainable AI (xAI) representation. These symbolic terms of dominant neurons
aid feature engineering-based validation and future design of PLS common
feature space. Simulation results show that our proposed GAN-based and
symbolic-based PL-SKGs can achieve high key agreement rates between legitimate
users, and is even resistant to MITM-RIS Eve with the knowledge of legitimate
feature generation (NNs or formulas). This therefore paves the way to secure
wireless communications with untrusted reflective devices in future 6G.
