license: other
license_name: ids
license_link: https://www.unb.ca/cic/datasets/ids-2017.html
task_categories:
- text-classification
language:
- en
pretty_name: Network-Intrusion-Detection
This directory consists on 24x24 images
- train folder have total 1548421 images from 10 classes
- test folder have 663609 images from 10 classes
This Dataset is Spectrogram converted images using method explained in our research article XYZ. Dataset Used: Intrusion detection evaluation dataset (CIC-IDS2017) Image Size: 28x28
Classes:
- BENIGN
- Bot
- DDoS
- DoS GoldenEye
- DoS Hulk
- DoS Slowhttptest
- DoS slowloris
- Heartbleed
- Infiltration
- PortScan
License: https://www.unb.ca/cic/datasets/ids-2017.html
The CICIDS2017 dataset consists of labeled network flows, including full packet payloads in pcap format, the corresponding profiles and the labeled flows (GeneratedLabelledFlows.zip) and CSV files for machine and deep learning purpose (MachineLearningCSV.zip) are publicly available for researchers. If you are using our dataset, you should cite our related paper which outlining the details of the dataset and its underlying principles:
Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018.