Errors in data, part 2!

A few weeks ago, the sigma module was launched, featuring methods to model and repair sigma rules. Following this methodology, an end-user still needs to encode constraints in the form of sigma rules. In some cases, this can be troublesome: the number of rules can be very high or some rules might be missed, allowing errors to remain unnoticed.

In our newest module, some algorithms are implemented to automatically discover sigma rules on a given dataset. This module is called dino, short for Discovery of Inconsistencies and Outliers. Code, documentation, examples and license information can be found in the dino repository on gitlab.