adding a feature as "generic"
If an information is never available for one of the classes, it's not a usable indication. So it seems to me that the login name is simply irrelevant for the task, so it shouldn't be included as a feature.
Mixing different sources of data can be ok in some cases, but only if the different datasets provide consistent features and are generally representative of the distribution you would expect (another important problem). It looks like you have no username nor timestamp in the &quot;bad&quot; one, do you? if so it's impossible to use these features.
No you cannot give priority to any feature. This wouldn't make sense, since the point of ML is to let the algorithm find patterns in the data. If you already know how the decision should be made then you should simply write a regular deterministic program (this is called a heuristic).
Don't forget that you can decide to add any feature that you think could be relevant:

you could have a boolean feature indicating whether a login name is known or unknown, instead of setting the value to 0. But be careful that by definition in your external dataset they are all unknown whereas they are all known in your &quot;good&quot; dataset, that's an clear construction bias.
if you have a timestamp you could think of many other features, for instance number of repeated attempts in the past N seconds, regular time patterns for login, etc.