Kernel Methods for Conformal Prediction to Detect Botnets

Botnets are networks of compromised computers controlled by malicious actors and are responsible for a wide range of cyberattacks. Detecting botnets is a critical task in cybersecurity, and machine learning techniques have been widely employed for this purpose. In this paper, we present a novel approach for botnet detection based on kernel methods and conformal prediction. Our method leverages the power of kernel functions to capture complex patterns in network traffic data and uses conformal prediction to provide confidence measures for the predictions. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of our approach in accurately identifying botnet activities while maintaining a low false positive rate. Furthermore, we show that our method is robust to concept drift and can adapt to evolving botnet behaviors. Overall, our work contributes to the advancement of machine learning-based botnet detection and provides a reliable tool for safeguarding network systems against botnet threats.