GRAIN: Graph neural network and reinforcement learning aided causality discovery for multi-step attack scenario reconstruction

Abstract

Correlating individual alerts to reconstruct attack scenarios has become a critical issue in identifying multi-step attack paths. Most of existing reconstruction approaches depend on external expertise, such as attack templates or attack graphs, to identify known attack patterns, which are incapable of uncovering unknown attack patterns that exceed prior knowledge. Recently, several expertise-independent methods utilize alert similarity or statistical correlations to reconstruct multi-step attacks. However, these methods often miss rare but high-risk events. The key to overcoming these drawbacks lies in discovering the potential causalities between security alerts. In this paper, we propose GRAIN, a novel graph neural network and reinforcement learning aided causality discovery approach for multi-step attack scenario reconstruction, which does not rely on any external expertise or prior knowledge. By matching the similarity between alerts’ attack semantics, we first remove redundant alerts to alleviate alert fatigue. Then, we correlate these alerts as alert causal graphs that embody the causalities between attack incidents via causality discovery. Afterwards, we employ a graph neural network to evaluate the causal effect between correlated alerts. In light of the fact that the alerts triggered by multi-step attacks have the maximum causal effect, we utilize reinforcement learning to screen out authentic causal relationships. Extensive evaluations on 4 public multi-step attack datasets demonstrate that GRAIN significantly outperforms existing methods in terms of accuracy and efficiency, providing a robust solution for identifying and analyzing sophisticated multi-step attacks.