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While digitization of distribution grids through information andcommunications technology brings numerous benefits, it also increases thegrid's vulnerability to serious cyber attacks. Unlike conventional systems,attacks on many industrial control systems such as power grids often occur inmultiple stages, with the attacker taking several steps at once to achieve itsgoal. Detection mechanisms with situational awareness are needed to detectorchestrated attack steps as part of a coherent attack campaign. To provide afoundation for detection and prevention of such attacks, this paper addressesthe detection of multi-stage cyber attacks with the aid of a graph-based cyberintelligence database and alert correlation approach. Specifically, we proposean approach to detect multi-stage attacks by leveraging heterogeneous data toform a knowledge base and employ a model-based correlation approach on thegenerated alerts to identify multi-stage cyber attack sequences taking place inthe network. We investigate the detection quality of the proposed approach byusing a case study of a multi-stage cyber attack campaign in afuture-orientated power grid pilot.

On Holistic Multi-Step Cyberattack Detection via a Graph-based Correlation Approach