Lifelong Autonomous Botnet Detection

Botnet-driven attacks have attracted attention due to their diversity, high potential to cause damage and massive data generation. Existing botnet detection solutions are usually specific to a type of attack behavior. This particularity makes attack detection challenging because it involves a high operational overhead for manually calibrating and managing a large set of solutions for different attacks and variations. Hence, this work presents LBDS, a botnet detection system that acts autonomously in dynamic environments. It relies on concept drift and AutoML, two main techniques that consider dynamic behavior on data distribution. The LBDS evaluation has followed a diverse set of attacks and protocols. Results demonstrate that the system detects botnets utilizing different detection techniques, indicating its ability to consider various aspects of data and attacks.