HSMLog: Small Language Model-Assisted Hardware Security Module Log Anomaly Detection with Behavioral Analysis
HSMLog applies a small language model to detect anomalies across HSM event sequences by correlating key lifecycles, sessions, and temporal patterns—moving beyond isolated-event alerting to behavioural analysis of cryptographic operations.
Summary written by editorial AI · Source link below
arXiv:2608.29773v1 Announce Type: new Abstract: Hardware Security Module (HSM) logs capture security-critical behavior, but anomalies emerge from relationships across event sequences, keys, object states, sessions, and temporal patterns rather than isolated events. Existing methods separate detection from HSM-specific evidence validation and reporting. In this paper, we present HSMLog, a two-stage framework for HSM log anomaly detection with retrieval-grounded behavioral analysis. In Stage 1, a
Editorial Analysis
HSMs underpin key management for payments, PKI, and regulated workloads across Europe; behavioural anomaly detection could catch subtle key-misuse patterns that rule-based monitoring misses.
Assess whether your HSM monitoring includes behavioural anomaly detection across key lifecycles and session patterns, not just threshold-based alerts.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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