Resources

Software and datasets

“Greater complexity implies greater potential for knowledge, but we must be aware of this fact to properly process the data obtained”.

At Codas Lab we take dissemination of knowledge very seriously and therefore, we make a series of resources available to the scientific community for download.

Below you can learn about each of them:

▪︎ Software

▪︎ Datasets

Software

Feature as a Counter Parser

Multivariate Exploratory Data Analysis (MEDA) Toolbox for its use in Matlab

MVBatch for its use in Matlab

Joint work with Dr. José María González Martínez (Shell) and Prof. Alberto Ferrer (MSERG)

Datasets

UGR16 Feature data. This repository contains the four feature data variants of UGR'16 used in the following papers:
- Camacho, Wasielewska, Espinosa, Fuentes-García. Quality In / Quality Out: Data quality more relevant than model choice in anomaly detection with the UGR’16. IEEE/IFIP Network Operations and Management Symposium. Miami, USA. 2023.
- Camacho, Wasielewska, Fuentes-García, Rodríguez-Gómez. Quality In / Quality Out: Assessing Data Quality in an Anomaly Detection Benchmark. arXiv preprint arXiv:Camacho, Wasielewska, Fuentes-García, Rodríguez-Gómez. Quality In / Quality Out: Assessing Data Quality in an Anomaly Detection Benchmark. arXiv preprint arXiv:2305.19770 [cs.LG], 2023.
Please, make sure to reference the last paper when using the data, and also the original paper of UGR'16:
- Maciá-Fernández, G., Camacho, J., Magán-Carrión, R., García-Teodoro, P., Therón, R. Ugr'16: a new dataset for the evaluation of cyclostationarity-based network IDSs. Computer & Security, 2018, 73: 411-424.
Dartmouth Feature data. This repository contains the feature data of Dartmouth dataset used in the following papers:
- Camacho, J., Wasielewska, K., Bro R., Kotz, D., Interpretable Learning in Multivariate Big Data Analysis for Network Monitoring. Preprint arXiv:1907.02677 [cs.NI].
- Camacho, J., Wasielewska, K., Bro R., Kotz, D., Extracting Knowledge from Network Data: Multivariate Visualizations of Network Analytics based on Matrix Factorization, Submitted to ACM Internet Measurement Conference, 2023.
Please, make sure to reference the first paper when using the data, and also the original paper of the Dartmouth dataset:
- Camacho, J. , McDonald, C., Peterson, R., Zhou, X. Longitudinal Analysis of a Campus Wi-Fi Network . Computer Networks. 2020, 179, 107103.

If you want to receive information about the resources available from Codas Lab, do not hesitate to contact us.

Resources

Software and datasets

Software

Datasets

INFORMATION

POLICIES