  One of the principal uses of physical-space sensors in public safety
applications is the detection of unsafe conditions (e.g., release of poisonous
gases, weapons in airports, tainted food). However, current detection methods
in these applications are often costly, slow to use, and can be inaccurate in
complex, changing, or new environments. In this paper, we explore how machine
learning methods used successfully in cyber domains, such as malware detection,
can be leveraged to substantially enhance physical space detection. We focus on
one important exemplar application--the detection and localization of
radioactive materials. We show that the ML-based approaches can significantly
exceed traditional table-based approaches in predicting angular direction.
Moreover, the developed models can be expanded to include approximations of the
distance to radioactive material (a critical dimension that reference tables
used in practice do not capture). With four and eight detector arrays, we
collect counts of gamma-rays as features for a suite of machine learning models
to localize radioactive material. We explore seven unique scenarios via
simulation frameworks frequently used for radiation detection and with physical
experiments using radioactive material in laboratory environments. We observe
that our approach can outperform the standard table-based method, reducing the
angular error by 37% and reliably predicting distance within 2.4%. In this way,
we show that advances in cyber-detection provide substantial opportunities for
enhancing detection in public safety applications and beyond.
