Our paper's information can be found here:

• Title: LogTransfer: Robust Log-based Anomaly Detection for Emerging Services
• Authors:
• Paper link: LogTransfer

python 3.6.8
kreas 2.2.4 
tensorflow 1.9.0
numpy 1.16.4
scikit-learn 0.21.2 

├── checkpoint              # Model_checkpoint
    ├── model               # Init train model's checkpoint 
    ├── transfer            # Transfer model's checkpoint 
├── codes                   # Code
├── data_processing         # Data processing code
├── labels                  # Label files dir
├── Logs                    # Training data files dir
├── record                  # Record dir(for backtracking errors)
├── result                  # Predict results dir   
├── README.md               # README file

git clone https://github.com/logtransfergit/LogTransfer.git
cd LogTransfer
pip install -r requirements.txt
cd codes
bash test.sh

• Removing logs which containing keywords and the corresponding labels.
• Script example: python data_keyword_remove.py --rootdir ../Logs --label_dir ../labels --removal log_in
• | Argument | Type | Default | Description | |--------------------|------|-------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------| | rootdir | str | '../Logs' | log data dir| | label_dir | str | '../labels' | label data dir | | removal | str | r'logined the switch|logouted from the switch|logout from|logged in from|login from |logged out from|Service=login-UserName' | keywords to be detect |

• Initially training a model with abundant labeled data.
• Script example: python models.py --single_step 3 --window_size 20 --rootdir ../Logs --label_dir ../labels/S5/ --vector_path ../Logs/template_vec.dat --selected_switchid_list [30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50]
• | Argument | Type | Default | Description | |--------------------|------|-------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------| | window_size | int | 10 | the length of input | | single_step| int | 1 | the step span when choosing train data | | rootdir | str | '../Logs' | train data dir | | label_dir | str | '../labels/' | label data dir | | vector_path | str | '../Logs/template_vec.dat' | pretrained template vector | | selected_switchid_list | list | [] | train data file lists |

• Revising the trained model with a small amount of specific data.
• Script example: python transferlearning.py --single_step 3 --window_size 20 --rootdir ../Logs --label_dir ../labels/B6220/ --vector_path ../Logs/template_vec.dat --selected_switchid_list [1,2,3,4,5]
• | Argument | Type | Default | Description | |--------------------|------|-------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------| | window_size | int | 10 | the length of input | | single_step| int |1| the step span when choosing train data | | rootdir | str |'../Logs'| train data dir| | label_dir| str | '../labels/B6220/' | label data dir | | vector_path| str | '../Logs/template_vec.dat' | pretrained template vector | | selected_switchid_list | list | [] | train data file lists|

• Predicting whether anomalies with trained models and evaluating predictions.
• Script example: python predict.py --single_step 3 --window_size 20 --rootdir ../Logs --label_dir ../labels/B6220/ --vector_path ../Logs/template_vec.dat --selected_switchid_list [6,7,0] --save_model_dir ../checkpoint/transfer/ --save_result_dir ../result2/ --mode predict
• | Argument | Type | Default | Description | |--------------------|------|-------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------| | window_size | int | 10 | the length of input | | single_step | int |1 | the step span when choosing train data | | rootdir | str | '../Logs' | train data dir| | label_dir | str | '../labels/B6220/' | label data dir | | vector_path | str | '../Logs/template_vec.dat' | pretrained template vector | | selected_switchid_list | list | [] | train data file lists | | save_model_dir | str |'../checkpoint/transfer/'| model checkpoint dir | | save_result_dir | str |'../result/' | save result dir | | mode | str | 'predict' | choose wether to predict or calculate performance |

• Some tool functions.