Not All Relations Are Equal: Relation-Balanced and Calibrated Graph Learning for Provenance-Based Intrusion Detection
RECAL improves provenance-based APT detection with relation-balanced masked graph learning and calibrated errors, reaching 99.99% F1 on DARPA E3 datasets.
RECAL is an unsupervised framework for provenance-based intrusion detection that uses relation-balanced masked graph learning to capture rare interaction patterns, addressing statistical heterogeneity where relation frequencies differ by roughly 140,000X in CADETS. It calibrates reconstruction errors against each relation's benign error distribution to produce comparable anomaly evidence and reduce false alarms. On three DARPA E3 datasets, RECAL achieves F1 scores of 99.99%, 99.93%, and 99.99%, outperforming the best baseline on each dataset, and reduces mean false positive rate by approximately 105X, 4X, and 41X versus the lowest-FPR baseline.
- Relation-balanced masked graph learning captures rare interaction patterns in provenance data
- Calibrates reconstruction errors per relation to reduce false alarms
- F1 scores of 99.99%, 99.93%, and 99.99% on three DARPA E3 datasets
- Reduces mean false positive rate by up to 105X versus lowest-FPR baseline
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Provenance-Based Intrusion Detection Systems (PIDSs) detect Advanced Persistent Threats (APTs) by analyzing system interactions. However, existing methods largely treat relations uniformly, overlooking statistical heterogeneity; in CADETS, relation frequencies differ by approximately $140{,}000\times$. This may cause PIDSs to focus more on frequent relations and overlook differences in normal error levels across relations, increasing the risk of false alarms and missed detections. We present RECAL, an unsupervised framework using relation-balanced masked graph learning to better capture rare interaction patterns. It further calibrates reconstruction errors against each relation's benign error distribution to produce comparable anomaly evidence, helping distinguish attacks from benign behavior and reduce false alarms. On three DARPA E3 datasets, RECAL achieves F1 scores of 99.99\%, 99.93\%, and 99.99\%, outperforming the best baseline on each dataset by 0.88, 0.82, and 0.42 percentage points, respectively. Compared with the baseline reporting the lowest FPR, RECAL reduces mean FPR by approximately $105\times$, $4\times$, and $41\times$.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.16462