Dataset that classifies malicious vs benign web characteristics based on application layer and network characteristics.

Source: kaggle. Originally from the following paper:

Urcuqui, C., Navarro, A., Osorio, J., & Garcıa, M. (2017). Machine Learning Classifiers to Detect Malicious Websites. CEUR Workshop Proceedings. Vol 1950, 14-17.

Todos los movimientos de las tarjetas Black de Caja Madrid, con sus implicados, fechas e importes.

Fuente

Dataset capturado en un artículo de El Pais usando import.io

The dataset is aimed to classify the malware/beningn Android permissions.

A binary vector of permissions is used for each application analyzed {1=used, 0=no used}. Moreover, the samples of malware/benign were devided by "Type"; 1 malware and 0 non-malware.

Source: kaggle. Originally from the following paper:

Urcuqui, C., & Navarro, A. (2016, April). Machine learning classifiers for android malware analysis. In Communications and Computing (COLCOM), 2016 IEEE Colombian Conference on (pp. 1-6). IEEE.

A dataset on the type of glass and it's chemical composition. Useful in forensics when broken glass is found at a crime scene.

Intentional homicide, number and rate per 100,000 population

Sources

UNODC Homicide Statistics 2012 | United Nations Office on Drugs and Crime

Recopilación de los delitos sexuales en Bogotá en el año 2016

Small version of the credit cards fraud dataset from Kaggle: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

Credit cards fraud dataset from Kaggle: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud