Building a DGA Classifier: Part 1, Data Preparation

The purpose of building a DGA classifier isn’t specifically for takedowns of botnets, but to discover and detect the use on our network or services. If we can you have a list of domains resolved and accessed at your organization, it is possible now to see which of those are potentially generated and used by malware. The dataset consists of three sources (as decribed in the Data-Driven Security blog): Alexa: For samples of legitimate domains, an obvious choice is to go to the Alexa list of top web sites. But it’s not ready for our use as is. If you grab the top 1 Million Alexa domains and parse it, you’ll find just over 11 thousand are full URLs and not just domains, and there are thousands of domains with subdomains that don’t help us (we are only classifying on domains here). So after I remove the URLs, de-duplicated the domains and clean it up, I end up with the Alexa top 965,843. “Real World” Data from OpenDNS: After reading the post from Frank Denis at OpenDNS titled “Why Using Real World Data Matters For Building Effective Security Models”, I grabbed their 10,000 Top Domains and their 10,000 Random samples. If we compare that to the top Alexa domains, 6,901 of the top ten thousand are in the alexa data and 893 of the random domains are in the Alexa data. I will clean that up as I make the final training data set. DGA do: The Click Security version wasn’t very clear in where they got their bad domains so I decided to collect my own and this was rather fun. Because I work with some interesting characters (who know interesting characters), I was able to collect several data sets from recent botnets: “Cryptolocker”, two seperate “Game-Over Zues” algorithms, and an anonymous collection of malicious (and algorithmically generated) domains. In the end, I was able to collect 73,598 algorithmically generated domains. ;