CLAD: Efficient Log Anomaly Detection Directly on Compressed Representations

• URL: http://arxiv.org/abs/2604.13024v1
• Date: Tue, 14 Apr 2026 17:57:01 GMT
• Title: CLAD: Efficient Log Anomaly Detection Directly on Compressed Representations
• Abstract summary: We introduce CLAD, the first deep learning framework to perform LAD directly on compressed byte streams.<n>We propose a purpose-built architecture integrating a dilated convolutional byte encoder, a hybrid Transformer--mLSTM, and four-way aggregation pooling.<n> evaluated across five datasets, CLAD achieves a state-of-the-art average F1-score of 0.9909 and outperforms the best baseline by 2.72 percentage points.
• Score: 2.487363589269089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The explosive growth of system logs makes streaming compression essential, yet existing log anomaly detection (LAD) methods incur severe pre-processing overhead by requiring full decompression and parsing. We introduce CLAD, the first deep learning framework to perform LAD directly on compressed byte streams. CLAD bypasses these bottlenecks by exploiting a key insight: normal logs compress into regular byte patterns, while anomalies systematically disrupt them. To extract these multi-scale deviations from opaque bytes, we propose a purpose-built architecture integrating a dilated convolutional byte encoder, a hybrid Transformer--mLSTM, and four-way aggregation pooling. This is coupled with a two-stage training strategy of masked pre-training and focal-contrastive fine-tuning to effectively handle severe class imbalance. Evaluated across five datasets, CLAD achieves a state-of-the-art average F1-score of 0.9909 and outperforms the best baseline by 2.72 percentage points. It delivers superior accuracy while completely eliminating decompression and parsing overheads, offering a robust solution that generalizes to structured streaming compressors.

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