  In today's data-driven world, the proliferation of publicly available
information raises security concerns due to the information leakage (IL)
problem. IL involves unintentionally exposing sensitive information to
unauthorized parties via observable system information. Conventional
statistical approaches rely on estimating mutual information (MI) between
observable and secret information for detecting ILs, face challenges of the
curse of dimensionality, convergence, computational complexity, and MI
misestimation. Though effective, emerging supervised machine learning based
approaches to detect ILs are limited to binary system sensitive information and
lack a comprehensive framework. To address these limitations, we establish a
theoretical framework using statistical learning theory and information theory
to quantify and detect IL accurately. Using automated machine learning, we
demonstrate that MI can be accurately estimated by approximating the typically
unknown Bayes predictor's log-loss and accuracy. Based on this, we show how MI
can effectively be estimated to detect ILs. Our method performs superior to
state-of-the-art baselines in an empirical study considering synthetic and
real-world OpenSSL TLS server datasets.
