Is it possible to create a machine learning classifier to generate Mock interfaces for systems testing? I'm investigating whether it is feasible to be able to learn a system interface by watching network traffic (assuming the usual problems are solved e.g. encryption etc)

I haven't been able to find any literature on this - but there might be a cross-discipline piece of work which could assist.

The main purpose here would be to allow complex enterprise systems to be refactored independently. A simple replay of data doesn't always work because we want to learn the request-response interface - though e.g. something like a market trade having multiple fills is an example of very complex orchestration that might make the whole thing unpractical.
A classical result from automaton theory shows that even the problem of finding minimum-size DFA consistent with given positive and negative samples is NP-hard. This was shown by Gold in 1978. Your problem is much harder, as in practice you would have to learn data relationships in addition to any finite-state control of the system interface.