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Smarter Password Guessing Techniques Leveraging Contextual Information and OSINT
Author(s)
Date Issued
2020-06-19
Date Available
2023-09-20T15:00:59Z
Abstract
In recent decades, criminals have increasingly used the web to research, assist and perpetrate criminal behaviour. One of the most important ways in which law enforcement can battle this growing trend is through accessing pertinent information about suspects in a timely manner. A significant hindrance to this is the difficulty of accessing any system a suspect uses that requires authentication via password. Password guessing techniques generally consider common user behaviour while generating their passwords, as well as the password policy in place. Such techniques can offer a modest success rate considering a large/average population. However, they tend to fail when focusing on a single target - especially when the latter is an educated user taking precautions as a savvy criminal would be expected to do. Open Source Intelligence is being increasingly leveraged by Law Enforcement in order to gain useful information about a suspect, but very little is currently being done to integrate this knowledge in an automated way within password cracking. The purpose of this research is to delve into the techniques that enable the gathering of the necessary context about a suspect and find ways to leverage this information within password guessing techniques.
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2020 IEEE
Web versions
Language
English
Status of Item
Peer reviewed
Journal
International Conference on Cyber Security and Protection of Digital Services, Cyber Security 2020
Conference Details
The 2020 IEEE International Conference on Cyber Security and Protection of Digital Services, Dublin, Ireland (held online due to coronavirus outbreak), 15-17 June 2020
ISBN
9781728164281
This item is made available under a Creative Commons License
File(s)
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Name
Katerina__Cyber_Science_2020 (3).pdf
Size
92.22 KB
Format
Adobe PDF
Checksum (MD5)
147cf67a1aff89c5066d4e06b68c48f5
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