I built (and tried to get funding for) http://gameref.io/ a few years ago. Many critics argued that a deep learning or AI solution would be better. For what it's worth, I also played Counter-Strike at a professional level from '04-'07 -- my CS:Source team was ranked #7 in North America in 2005 (according to GotFrag.com).

After some research and a few POCs I am 100% certain a ML solution will not work in detecting cheats other than completely trivial "spinbots" or "aim-lock" aimbots. For example, it by definition cannot catch an entire class of cheats (wall hacks), but it even falters when trying to catch aimbots; the presentation here argues for a "heuristic-based" method, but, again, these methods often fail when (if you look at the source), aimbots are often coded to (a) move smoothly/naturally and (b) introduce random noise.

In my case, I tried to at the very least combat one class of cheats with close to 100% confidence. These kinds of talks are great at conferences, but, as they say, the proof is in the pudding. Cheaters are very hard to catch, and cheat coders are much smarter than generalized AI methods.