Beyond gut instincts: Understanding, rating and comparing self-learning IDSs

Abstract

Today ICT networks are the economy's vital backbone. While their complexity continuously evolves, sophisticated and targeted cyber attacks such as Advanced Persistent Threats (APTs) become increasingly fatal for organizations. Numerous highly developed Intrusion Detection Systems (IDSs) promise to detect certain characteristics of APTs, but no mechanism which allows to rate, compare and evaluate them with respect to specific customer infrastructures is currently available. In this paper, we present BAESE, a system which enables vendor independent and objective rating and comparison of IDSs based on small sets of customer network data.