  In this paper, we explore the use of machine learning methods as an efficient
alternative to correlation in performing packet detection. Targeting
satellite-based massive machine type communications and internet of things
scenarios, our focus is on a common channel shared among a large number of
terminals via a fully asynchronous ALOHA protocol to attempt delivery of short
data packets. In this setup, we test the performance of two algorithms, neural
networks and random forest, which are shown to provide substantial improvements
over {traditional} techniques. Excellent performance is demonstrated in terms
of detection and false alarm probability also in the presence of collisions
among user transmissions. The ability of machine learning to extract further
information from incoming signals is also studied, discussing the possibility
to classify detected preambles based on the level of interference they undergo.
