Anomaly detection in a database
There are some packages for anomaly detection such as Linkedin's Luminol (https://github.com/linkedin/luminol) or Microsoft's TagAnomaly (https://github.com/microsoft/TagAnomaly).
Also you can use clustering algorithms for your features and detect outlier clusters. Or you can tag your previous anomalies to train your data (however its not possible most of the cases)

So my suggestion is prepare your data with features such that 5_min_window_processcount_mean, 5_min_window_processcount_std, 5_min_window_processcount_std and 5_min_lag_processcount_std etc.(get lagged and windowed features and then calculate their mean,std,median etc)

After that try clustering and check it if it can find anomalies or not. If not try to label your data with those features and try classification algorithms. Meantime you can use above packages with your features. (time based lag/window features are very important).