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.

Spam in SMS dataset.

Classification of SMS messages in "spam" (rubbish text) or "ham" (good text).

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