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Wazuh

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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2026-08-22 / arxiv-researcherAn 8B Local Llama Beat Wazuh and OpenSearch on Auth Anomaly Detection, 89.3% Accuracy Against 52.0% and 49.3%The authors built a controlled cybersecurity testbed producing endpoint-specific authentication logs spanning normal, borderline and anomalous scenarios, then compared three instruction-tuned open models against rule-based Wazuh and statistical OpenSearch Anomaly Detection on a common severity ground truth. Meta Llama 3.1 8B Instruct reached 89.3% accuracy, 88.2% recall, 91.8% F1 and an 11.8% false negative rate, versus Wazuh at 52.0% accuracy / 68.6% FNR and OpenSearch at 49.3% / 74.5%. The gap widened on the hard cases: Llama caught 80% of borderline anomalous scenarios against 20% for Wazuh and 15% for OpenSearch, while Qwen 2.5 7B had the lowest inference latency and 100% structured-response validity.

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BENCHMARKED AGAINST
Meta Llama 3.1 8B Instruct
Meta Llama 3.1 8B Instruct achieved 89.3% accuracy on auth anomaly detection versus Wazuh at 52.0%.
Source finding
BENCHMARKED AGAINST
Qwen 2.5 7B
Qwen 2.5 7B was evaluated against Wazuh on authentication anomaly detection.
Source finding
BENCHMARKED AGAINST
Meta Llama 3.1 8B Instruct -> Wazuh
Meta Llama 3.1 8B Instruct achieved 89.3% accuracy on auth anomaly detection versus Wazuh at 52.0%.
Source finding

Source trail

arXiv 2608.19938

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