1. A computer-implemented method comprising: initiating, by a first node of a networked computing environment, each of a plurality of different types of different man-in-the middle (MITM) detection tests to determine whether communications between first and second nodes of a computing network are likely to have been subject to an interception or an attempted interception by a third node; determining, by the first node, that at least one of the tests indicate that the communications are likely to have been intercepted by a third node; and providing, by the first node, data characterizing the determinations; wherein at least one of the different types of MITM detection tests utilizes at least one machine learning model trained by: receiving training data comprising a plurality of samples that each characterize interception of data traffic to a computing device over a network; grouping the plurality of samples characterizing the interception of data traffic into a plurality of clusters; labeling at least a portion of the samples to characterize a likelihood of each such sample as relating to an unauthorized interception of data traffic; assigning each cluster with a label corresponding to a majority of samples within such cluster; and training the at least one machine learning model using the assigned labeled clusters which, once trained, determines a likelihood of future samples as relating to an unauthorized interception of data traffic to a corresponding computing device.