Mechanisms to detect insecure password through GANs based on data mining from leaks in the deep web

• Grant number: 18/08673-8
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: August 01, 2019
• End date: August 31, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Rafael Mira de Oliveira Libardi
• Grantee: Rafael Mira de Oliveira Libardi
• Company: Noleak Tecnologia da Informação Ltda
• CNAE: Desenvolvimento e licenciamento de programas de computador customizáveis ; Consultoria em tecnologia da informação
• Associated researchers: Emanuel Carlos de Alcantara Valente ; Lucas da Silva Assis
• Associated scholarship(s): 19/05116-3 - Mechanisms to detect insecure password through GANs based on data mining from leaks in the deep web, BP.PIPE; 19/05473-0 - Mechanisms to detect insecure password through GANs based on data mining from leaks in the deep web, BP.TT; 19/07730-0 - Mechanisms to detect insecure password through GANs based on data mining from leaks in the deep web, BP.TT

Abstract

Despite several systems force the usage of strong passwords, leaks, such as logins and credentials are growing. According to Verizon, in 2016 over 3 bilion passwords were stolen. According to the same report, 64 of data violations were consequence of stolen credentials. In 2016, the financial losses from these credential leakes were 1,5 trilion reais. Our proposal is to create a business risk intelligence platform from these leaks to protect the companies passwords. (AU)