Source code for ToN'21 paper: "Interactive Anomaly Detection in Dynamic Communication Networks".

• python: 3.9
• numpy: 1.25.0
• pandas: 2.0.2
• matplotlib: 3.7.2
• scikit-learn: 1.3.0
• seaborn: 0.12.0
• tqdm: 4.65.0

1. Run the code with sample data extracted from CICIDS2017.

python3 ucb_haddn.py

2. Labeling results of the time period t and t+1 can be found in ./ucb_old.csv and ./ucb_new.csv, and the anomaly detection results of the time period t+1 can be found in ./ucb_test.csv.

1. Run the code

python3 ts_haddn.py

2. Labeling results of the time period t and t+1 can be found in ./ts_old.csv and ./ts_new.csv, and the anomaly detection results of the time period t+1 can be found in ./ts_test.csv.

Please cite our paper if you use this code in your own work:

@article{MengWWYZ21,
  title        = {Interactive Anomaly Detection in Dynamic Communication Networks},
  author       = {Xuying Meng and
                  Yequan Wang and
                  Suhang Wang and
                  Di Yao and
                  Yujun Zhang},
  journal      = {{IEEE/ACM} Trans. Netw.},
  volume       = {29},
  number       = {6},
  pages        = {2602--2615},
  year         = {2021}
}