RAPIER

Paper: Low-Quality Training Data Only? A Robust Framework for Detecting Encrypted Malicious Network Traffic

Preprocessing:

cd ./Preprocess
python Feature_Extract.py "input_dir" "sequence_data_path" "ext"
python get_origin_flow_data.py "sequence_data_path" "save_dir" "data_type"

• input_dir: The directory of all raw pcap files of one kind of data, e.g. benign, malicious, or test.
• sequence_data_path: The sequence data of all flows in pcap files, without zero-padding. (The values are prefix-cumulative values, and will be further processed by get_origin_flow_data.py)
• ext: The extension name of pcap files to process (e.g. pcap, pcapng).
• save_dir: The save directory of the processed sequence data of all flows.
• data_type: The kind of the processed data, e.g. w, b and test.

Output: A sequence numpy file of data_type in save_dir. i.e., {save_dir}/{data_type}.npy, the dimension of each sample is 50. Need to add the 51st dimension for detection.

Detection/Prediction:

cd ./main
python main.py

The argument can be modified in main.py are:

• data_dir: The directory of all sequence data.
• feat_dir: The directory of all feature data.
• made_dir: The directory of all results calculated by MADE.
• model_dir: The directory of all trained models.
• result_dir: The directory of the detection/prediction result of the test data.

The required input files:

• {data_dir}/{benign.npy}: The benign preprocessed training data.
• {data_dir}/{malicious.npy}: The malicious preprocessed training data.
• {data_dir}/{test.npy}: The preprocessed testing data.

All data in {data_dir} should have dimensions of (n, 51), where n is the number of samples. Each sample is a 51-dimension vector, where first 50 dimensions are time-series data of traffic and the last one is the true label for the sample (used to evaluate. 0 is for benign and 1 is for malicious). If RAPIER is used to predict, the last dimension can be any value.

Output:

• {result_dir}/{prediction.npy}: The prediction of all testing data. 1 is malicious and 0 is benign.