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- W2997976278 abstract "Log data contains very rich and valuable information that records system states and behavior, which can be used to diagnose system failures. Anomaly detection from large-scale log data plays a key role in building secure and trustworthy systems. Anomaly detection model based on machine learning has achieved good results in practical applications. However, logs generated by modern large-scale distributed systems are more complex than ever before in terms of data size and variety. Therefore, the traditional single-machine learning anomaly detection model faces the model aging problem. We design an anomaly detection model that combines multiple machine learning algorithms. By using a conformal prediction, we can calculate the confidence of each algorithm for each log to be detected and use statistical analysis to tag them with a trusted label. The approach was tested on the public HDFS_100k log dataset, and the results show that our model is more accurate." @default.
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- W2997976278 date "2019-01-01" @default.
- W2997976278 modified "2023-09-28" @default.
- W2997976278 title "A Confidence-Guided Anomaly Detection Approach Jointly Using Multiple Machine Learning Algorithms" @default.
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- W2997976278 doi "https://doi.org/10.1007/978-3-030-37352-8_8" @default.
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