In my previous post, Trend Analysis with the Netwitness Suite, I've presented an approach to develop a baseline and perform a trend analysis with ESA. As mentioned many times, every threat is different and detection techniques not only can, but must vary to effectively protect the businesses of our organizations.
There are situations in which threat patterns can be identified by simply reporting on new values of a given meta key, without the need of performing complicated statistical analysis. For example if a new browser or a new TLD never seen before shows up in our environment.
The Netwitness reporting engine has a very handy function called show_whats_new() which is doing the job for you. However, if you want to leverage the power of ESA to achieve the same, it will be more challenging since there is the need to work with large timeframes which must be handle with care within ESA.
By using the same approach detailed in my previous post, the attached EPL can safely look at the last 30 days of every meta key you want to monitor and alert once there is a new value. Events are aggregated every minute, hour and day so to limit the impacts on ESA performance and store in memory only the information required for achieving the use case.
Multiple meta keys can be monitored by replicating and customizing the last statement.
From an implementation standpoint, the model creates a history of meta key - value pairs which is checked on a daily basis to alert for each new value found. In order to setup a learning phase, the model internally stores also the current date so to prevent alerting until the warm up period is over.