Downloads

Software

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

Feature as a Counter Parser

Multivariate Big Data Analysis (MBDA) Service based on jupyter

Datasets

1. UGR16 Feature data This repository contains the four feature data variants of UGR'16 used in the following papers:

   • Camacho, J., Wasielewska, K., Espinosa, P., Fuentes-García, M. 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, J., Rodríguez-Gómez, R.A. Data quality tools to optimize an anomaly detection benchmark. Submitted to Data, 2024.

   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.

2. Dartmouth Feature data This repository contains the feature data of Dartmouth dataset used in the following paper:

   • Camacho, J., Wasielewska, K., Bro R., Kotz, D. Interpretable Learning in Multivariate Big Data Analysis for Network Monitoring. IEEE Transactions of Network and Service Management, 2024, 21(3):2926-2943.

   Please, make sure to reference the 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.