Deep Learning-Driven Frequency Hopping for IEEE 802.11a Wi-Fi in the Presence of an Eavesdropper

IEEE 802.11a Wireless-Fidelity is a wireless communications standard used by Internet of Things (IoT) deployments. The IoT's ubiquity and rapid integration often outpace or neglect security mechanisms essential to protecting the people and critical infrastructure it supports. This work presents a deep learning-driven low probability of intercept solution aimed at increasing wireless communications security by employing a frequency hopping scheme learned in the presence of a peer, on-path adversary. The results show the presented LPI approach ensures high synchronization between legitimate users, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt; 99\%$</tex>, while keeping the adversary's probability of intercept low, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt; 17\%$</tex>.