Contextual multi-armed bandits for web server defense

In this paper we argue that contextual multi-armed bandit algorithms could open avenues for designing self-learning security modules for computer networks and related tasks. The paper has two contributions: a conceptual and an algorithmical one. The conceptual contribution is to formulate the real-world problem of preventing HTTP-based attacks on web servers as a one-shot sequential learning problem, namely as a contextual multi-armed bandit. Our second contribution is to present CMABFAS, a new and computationally very cheap algorithm for general contextual multi-armed bandit learning that specifically targets domains with finite actions. We illustrate how CMABFAS could be used to design a fully self-learning meta filter for web servers that does not rely on feedback from the end-user (i.e., does not require labeled data) and report first convincing simulation results.