CLAD is the first deep learning framework for log anomaly detection that operates directly on compressed byte streams using a dilated convolutional encoder, hybrid Transformer-mLSTM, and two-stage training, achieving 0.9909 average F1-score across five datasets.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
EventADL introduces the first open-box framework for detecting anomalies and localizing root causes in cloud event data by learning semantic and frequency patterns from unlabeled historical events.
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CLAD: Efficient Log Anomaly Detection Directly on Compressed Representations
CLAD is the first deep learning framework for log anomaly detection that operates directly on compressed byte streams using a dilated convolutional encoder, hybrid Transformer-mLSTM, and two-stage training, achieving 0.9909 average F1-score across five datasets.
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EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems
EventADL introduces the first open-box framework for detecting anomalies and localizing root causes in cloud event data by learning semantic and frequency patterns from unlabeled historical events.