Traditional physical layer protocols (e.g. WiFi, WiMax, etc.) are well established and are often optimal in a wide variety of channel conditions. Unfortunately, this same optimality encourages the potential for catastrophic cyber or physical attacks due to prolific knowledge of underlying physical layers. Any truly resilient communications protocol must be capable of immediate redeployment after such an event. Through software-defined radios, and deep modulation, system blocks are replaced with machine learning graphs that can be trained, used, and then discarded as needed. Simulation and experimental hardware show how deep modulation can converge to viable communications links, using the same machine intelligence, in vastly different channels.