This takes #IDS and #Netflow anomaly detection to a whole new level…

How to scale your phishing detection to millions of attacks per day- with just $4,000 and a laptop.

I've seen presentations that prove this, and the AI does a better job at crafting phishing urls with a higher success rates than most humans do. This is where we start thinking of the larger AI frameworks as layers..

The real problem we're trying to solve here is context. We're lifting a bunch of "tokens", that usually have more than 3 characters, surrounding them with context and applying a probability value to them. All this with the express purpose of taking the high value indicators and applying them to our defenses in real-time. Not trivial, but not hard either. I'm not an SKLearn or NLTK expert- but I do know what it feels like to block accidentally netflix.com at the border….

Pretty soon, you find yourself back, staring at this "snort signatures" pattern problem. A small, elegant mathematical formula representing something your sensors should be detecting. All it's missing is a little normalization and a bit of an ever evolving data model behind it, representing the current state of the Internet…

The less noise your hunters have to weed through, the more focused they become. The more focused they are, the more likely they'll find that needle. Often times, as is the case with most breaches, enough positive edge is all it takes….

The problem wasn't trying to manage and automate the code deployment, as much as it became managing the playbooks that deployed the application(s). We could have kept those playbooks in with the core code, but that's more over-head in the repo and more people touching the core code that didn't need to....