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FS-Net

Implementation of "FS-Net: A Flow Sequence Network For Encrypted Traffic Classification".

If you find this method helpful for your research, please cite this paper:

@inproceedings{LiuHXCL19,
  author    = {Chang Liu and
               Longtao He and
               Gang Xiong and
               Zigang Cao and
               Zhen Li},
  title     = {FS-Net: {A} Flow Sequence Network For Encrypted Traffic Classification},
  booktitle = {{IEEE} Conference on Computer Communications (INFOCOM), 2019},
  pages     = {1171--1179},
  year      = {2019}
}

Requirement

  • python >= 3.4
  • numpy == 1.14.5
  • tqdm
  • tensorflow == 1.8.0

Dataset Format

The dataset consists of multiple files, and each file contains all the flow records of a specific application. And the files are ended with .num. For example

origin_data
	|---- alicdn.num
	|---- baidu.num

For a specific application, each flow record is consists with two parts, for example

50	3	7	5	5	5	;2920	167	51	78	968	38

There are two sequences in a record: the first one is encoded status sequence and the second on is the packet length sequence. The two sequences are separated with ;, and the elements in the sequences are separated with \t.

How to use

Step 1. Pre-Process The Dataset

The dataset is first formalized into .json files, and the train set and development set are split as follows:

python main.py --mode=prepro

The dataset will saved in the record folder, and the files are start with train and test. The setting can be changed with --train_json, --test_json, --train_meta and --test_meta.

Step 2: Train The Model

We can train our model by:

python main.py --mode=train

Note: hyper-parameters (such as batch size, hidden size, layer number) of the model and the training process can be explored in the main.py.

Step 3: Evaluation.

Given the evaluation dataset, we can conduct the evaluation with:

python main.py --mode=test --test_json=xxxxxx --test_model_dir=yyyyy

The model will loaded from the ${test_model_dir}, and the ${test_json} is the test data. The test data have the same format with the results of the Step 1.