Sven Krasser's Blog

Yesterday I posted a new article on the CrowdStrike blog with some follow-up thoughts to the Machine Learning webcast. The post covers another concrete example on how combining weak indicators can generally yield stronger ones. (If you are familiar with ML, this won’t make you raise an eyebrow.) It also covers various areas of application in the security space. Specifically, for cloud-based security, there is an opportunity to go beyond the small data sets that e.g. AV can leverage and look beyond the first few seconds of execution on a single machine.

One question that I got after the webcast was what toolchain I used. Long story short, I processed the final data in IPython using scikit-learn. For the figures, I used matplotlib with seaborn. For feature extraction, we’ve used both Python and Pig. Any questions or feedback, tweet me @SvenKrasser.

Since the 140 characters that Twitter grants you felt a bit limiting at times, I’ve decided to set up this blog to convey the occasional more verbose thoughts on technology and the Internet security space. Based on my current research interests and area of work, expect a slant towards Machine Learning and Big Data topics.

Just to get started, here’s a recording of a recent webcast on Machine Learning fundamentals and its applications at CrowdStrike that I did jointly with Dmitri.