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

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

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.

In this dataset, we shed light on the important features that have proved to be sound and effective in predicting phishing websites.

Source

Phishing Websites Data Set at UCI Machine Learning Repository

More info

• Mohammad, Rami, McCluskey, T.L. and Thabtah, Fadi (2012) An Assessment of Features Related to Phishing Websites using an Automated Technique. In: International Conferece For Internet Technology And Secured Transactions. ICITST 2012 . IEEE, London, UK, pp. 492-497. ISBN 978-1-4673-5325-0

• Mohammad, Rami, Thabtah, Fadi Abdeljaber and McCluskey, T.L. (2014) Predicting phishing websites based on self-structuring neural network. Neural Computing and Applications, 25 (2). pp. 443-458. ISSN 0941-0643

• Mohammad, Rami, McCluskey, T.L. and Thabtah, Fadi Abdeljaber (2014) Intelligent Rule based Phishing Websites Classification. IET Information Security, 8 (3). pp. 153-160. ISSN 1751-8709

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.

Dataset of urls labeled as malicious or non-malicious

Source

Github repo

Recopilación de los delitos sexuales en Bogotá en el año 2016