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for *Ops"],"sub_categories":["Observability \u0026 Monitoring with AI"],"readme":"# logdeep\r\n\r\n## Introduction\r\n\r\nLogDeep is an open source deeplearning-based log analysis toolkit for automated anomaly detection.\r\n\r\n![Framework of logdeep](data/semantic_vec.png)\r\n\r\n*Note: This repo does not include log parsing，if you need to use it, please check [logparser](https://github.com/logpai/logparser)*\r\n\r\n## Major features\r\n\r\n- Modular Design\r\n\r\n- Support multi log event features out of box\r\n\r\n- State of the art(Including resluts from deeplog,loganomaly,robustlog...)\r\n\r\n## Models\r\n\r\n| Model | Paper reference |\r\n| :--- | :--- |\r\n|DeepLog| [**CCS'17**] [DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning](https://www.cs.utah.edu/~lifeifei/papers/deeplog.pdf)|\r\n|LogAnomaly| [**IJCAI'19**] [LogAnomaly: UnsupervisedDetectionof SequentialandQuantitativeAnomaliesinUnstructuredLogs](https://www.ijcai.org/Proceedings/2019/658)|\r\n|RobustLog| [**FSE'19**] [RobustLog-BasedAnomalyDetectiononUnstableLogData](https://dl.acm.org/doi/10.1145/3338906.3338931)\r\n\r\n## Requirement\r\n\r\n- python\u003e=3.6\r\n- pytorch \u003e= 1.1.0\r\n  \r\n## Quick start\r\n\r\n```\r\ngit clone https://github.com/donglee-afar/logdeep.git\r\ncd logdeep\r\n```\r\n\r\nExample of building your own log dataset  \r\n[SAMPLING_EXAMPLE.md](data/sampling_example/README.md)\r\n\r\nTrain \u0026 Test DeepLog example\r\n\r\n```\r\ncd demo\r\n# Train\r\npython deeplog.py train\r\n# Test\r\npython deeplog.py test\r\n```\r\n\r\nThe output results, key parameters and train logs will be saved under `result/` path\r\n\r\n## DIY your own pipeline\r\n\r\nHere is an example of the key parameters of the loganomaly model which in `demo/loganomaly.py`  \r\nTry to modify these parameters to build a new model!\r\n\r\n```\r\n# Smaple\r\noptions['sample'] = \"sliding_window\"\r\noptions['window_size'] = 10\r\n\r\n# Features\r\noptions['sequentials'] = True\r\noptions['quantitatives'] = True\r\noptions['semantics'] = False\r\n\r\nModel = loganomaly(input_size=options['input_size'],\r\n                    hidden_size=options['hidden_size'],\r\n                    num_layers=options['num_layers'],\r\n                    num_keys=options['num_classes'])\r\n```\r\n\r\n## Benchmark results\r\n\r\n|       |            | HDFS |     | |\r\n| :----:|:----:|:----:|:----:|:----:|\r\n| **Model** | **feature** | **Precision** | **Recall** | **F1** |\r\n| DeepLog(unsupervised)| seq |0.9583 | 0.9330 | 0.9454 |\r\n| LogAnomaly(unsupervised) | seq+quan|0.9690 |0.9825 |0.9757 |\r\n| RobustLog(supervised)| semantic |0.9216 |0.9586 |0.9397 |\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fd0ng1ee%2Flogdeep","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fd0ng1ee%2Flogdeep","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fd0ng1ee%2Flogdeep/lists"}