ASGNet: Adaptive Semantic Gate Networks for Log-Based Anomaly Diagnosis
<|reference_start|>ASGNet: Adaptive Semantic Gate Networks for Log-Based Anomaly Diagnosis: Logs are widely used in the development and maintenance of software systems. Logs can help engineers understand the runtime behavior of systems and diagnose system failures. For anomaly diagnosis, existing methods generally use log event data extracted from historical logs to build diagnostic models. However, we find that existing methods do not make full use of two types of features, (1) statistical features: some inherent statistical features in log data, such as word frequency and abnormal label distribution, are not well exploited. Compared with log raw data, statistical features are deterministic and naturally compatible with corresponding tasks. (2) semantic features: Logs contain the execution logic behind software systems, thus log statements share deep semantic relationships. How to effectively combine statistical features and semantic features in log data to improve the performance of log anomaly diagnosis is the key point of this paper. In this paper, we propose an adaptive semantic gate networks (ASGNet) that combines statistical features and semantic features to selectively use statistical features to consolidate log text semantic representation. Specifically, ASGNet encodes statistical features via a variational encoding module and fuses useful information through a well-designed adaptive semantic threshold mechanism. The threshold mechanism introduces the information flow into the classifier based on the confidence of the semantic features in the decision, which is conducive to training a robust classifier and can solve the overfitting problem caused by the use of statistical features. The experimental results on the real data set show that our method proposed is superior to all baseline methods in terms of various performance indicators.<|reference_end|>
@article{yang2024asgnet:,
    title={ASGNet: Adaptive Semantic Gate Networks for Log-Based Anomaly Diagnosis},
    author={Haitian Yang, Degang Sun, Wen Liu, Yanshu Li, Yan Wang, Weiqing Huang},
    journal={arXiv preprint arXiv:2402.11841},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11841},
    primaryClass={cs.SE}
}