This post was written by the following Avast researchers:
Petr Somol, Avast Director AI Research
Tomáš Pevný, Avast Principal AI Scientist
Viliam Lisý, Avast Principal AI Scientist
Branislav Bošanský, Avast Principal AI Scientist
Andrew B. Gardner, Avast VP Research & AI
Michal Pěchouček, Avast CTO
One of the biggest unaddressed challenges in machine learning (ML) for security is how to process large-scale and dynamically created machine data. Machine data — data generated by machines for machine processing — gets less attention in ML research than video, sound and text, yet it is as prevalent in our digital world and is as important as the dark matter in the universe. In security, machine data is the primary source of information about attacks and other anomalous behavior on the internet. Even so, it’s notoriously hard to learn from it automatically, to discover unknown patterns, and to adapt the learning process to the scale, complexity, and ever-changing nature of machine data. In this post, the Avast AI Research Lab reports on our solution to the problem.