LogMILP enables both bag-level anomaly detection and instance-level localization in logs using only bag-level labels via prototype-guided structural modeling and counterfactual perturbation regularization.
Loghub: A large collection of system log datasets towards automated log analytics
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IOCRegex-gen automates IOC-to-regex conversion with LLMs via group-aware grouping and multi-stage validation, reporting 99.1% hit rate and 0.8% false-positive rate on 3000+ CTI reports and 2400 ground-truth strings.
AnomalyGen synthesizes realistic labeled log sequences from source code via Log-Oriented Control Flow Graphs and LLM CoT verification to boost F1 scores of 12 anomaly detection models on HDFS and Zookeeper.
A benchmark finds prompt-based LLMs achieve F1 scores of 0.82-0.91 for log anomaly detection in zero-shot settings without any labeled training data, while fine-tuned transformers reach 0.96-0.99.
citing papers explorer
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Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation
LogMILP enables both bag-level anomaly detection and instance-level localization in logs using only bag-level labels via prototype-guided structural modeling and counterfactual perturbation regularization.
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From IOCs to Regex: Automating CTI Operationalization for SOC with LLMs
IOCRegex-gen automates IOC-to-regex conversion with LLMs via group-aware grouping and multi-stage validation, reporting 99.1% hit rate and 0.8% false-positive rate on 3000+ CTI reports and 2400 ground-truth strings.
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AnomalyGen: Enhancing Log-Based Anomaly Detection with Code-Guided Data Augmentation
AnomalyGen synthesizes realistic labeled log sequences from source code via Log-Oriented Control Flow Graphs and LLM CoT verification to boost F1 scores of 12 anomaly detection models on HDFS and Zookeeper.
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LLM-Enhanced Log Anomaly Detection: A Comprehensive Benchmark of Large Language Models for Automated System Diagnostics
A benchmark finds prompt-based LLMs achieve F1 scores of 0.82-0.91 for log anomaly detection in zero-shot settings without any labeled training data, while fine-tuned transformers reach 0.96-0.99.