Towards an Early Warning System for Network Attacks Using Bayesian Inference

Abstract

The Internet has become the most vulnerable part of critical civil infrastructures. Proactive measures such as early warnings are required to reduce the risk of disasters that can be created using it. With the continuous growth in scale, complexity and variety of networked systems the quality of data is continuously decreasing. This paper investigates the ability to employ Bayesian inference for network scenario analysis with low quality data to produce early warnings. Theoretical account of the approach and experimental results using a real world attack scenario and a real network traffic capture is presented.