Datasets and code for TransGraphNet: Robust detection of malicious encrypted network traffic via transformer and graph neural models.
We provide the raw dataset and the processed dataset, which are stored in two folders respectively. The raw dataset can be obtained from the following link.
1、The MTA dataset (malware-traffic-analysis) can be obtained at malware-traffic-analysis.net.
2、The Stratosphere dataset can be obtained at Malware Capture Facility Project.
3、The CIC-IOT-2023 dataset can be obtained at Canadian Institute for Cybersecurity.
1、raw_dataset We provide the raw MTA and CIC-IoT-2023 datasets used in this study.
1、processed_dataset: We provide the processed MTA and CIC-IoT-2023 datasets. Both datasets have been preprocessed using the improved Power Law Division (PLD) algorithm and modeled using the Adaptive Flow-level Burst Graph (AFG) structure. The datasets are available in both .bin and .pkl formats.
2、code：We have integrated the data reading and classification modules into a single Python file.
git clone https://github.com/TransGraphNet/TransGraphNet.git
git lfs pull
python 3.9
requirement：requirement.txt
cd code
python RDNet.py