Network Intrusion Detection Via Machine Learning

Computer security is an increasingly important, yet complex task. It takes significant skills to configure systems properly such that they are safe from malicious attacks. The proposed project aims at designing automatic systems which are able to adapt to an existing network configuration and which detect novel and unusual events. For this purpose we will use modern machine learning techniques, mainly based on kernels. In particular, recently developed algorithms to estimate the support of a distribution and detect rare events will be employed in this context. The project is in cooperation with Dr. Ralf Herbrich (Microsoft Research, Cambridge).