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MalRank: A Measure of Maliciousness in SIEM-based Knowledge Graphs
In this paper, we formulate threat detection in SIEM environments as a large-scale graph inference problem. We introduce a SIEM-based knowledge graph which models global associations among entities observed in proxy and DNS logs, enriched with related open source intelligence (OSINT) and cyber threat intelligence (CTI). Next, we propose MalRank, a graph-based inference algorithm designed to infer a node maliciousness score based on its associations to other entities presented in the knowledge graph, e.g., shared IP ranges or name servers.
After a series of experiments on real-world data captured from a global enterprise's SIEM (spanning over 3TB of disk space), we show that MalRank maintains a high detection rate (AUC =96%) outperforming its predecessor, Belief Propagation, both in terms of accuracy and efficiency. Furthermore, we show that this approach is effective in identifying previously unknown malicious entities such as malicious domain names and IP addresses. The system proposed in this research can be implemented in conjunction with an organization's SIEM, providing a maliciousness score for all observed entities, hence aiding SOC investigations.