Been there, done that, found much better ideas, tested them out, and much more.

I started in such things via artificial intelligence at IBM and gave a paper on the work at an AAAI IAAI conference at Stanford.

Later I took a different approach:

Sure, we want:

(1) To exploit data on several variables, jointly, easily seen as much more powerful than processing the variables one at a time. Processing the variables one at a time usually means that the geometrical region where do not raise an alarm has to be a box, just a box, with edges parallel to the coordinate axes. Bummer.

My work does great even if the region where we raise an alarm is a fractal, e.g., the Mandelbrot set or its complement, yes, in two dimensions or 20 or more.

(2) To be able to select and set false alarm rate and get that rate exactly.

(3) Know the 'severity' of a detection, say, the lowest false alarm rate for which would still have a detection.

(4) Do well on detection rate -- clearly we can't do as well as in the best possible Neyman-Pearson result, but still there is a powerful sense in which we can be the best.

For the above, I worked out how and wrote the code.

I tested the technology and code on some data from a cluster of servers at a large, famous company. My work performed just as predicted.