Learning Framework for Guessing Alphanumeric Passwords on Mobile Phones Based on User Context and Fragment Semantics

Abstract

When conducting a criminal investigation, accessing mobile phone data is crucial for law enforcement. However, encryption mechanisms and user locks are becoming increasingly complex and more challenging for forensic examiners. Although there are tools that can perform brute-force attacks to crack passwords on mobile phones, it becomes difficult when faced with alphanumeric passwords. The challenge is not only the algorithm but also the use of a customized dictionary. It is impractical to use a complete dictionary with all possible combinations as the attack conditions are very restrictive, and the time it takes to crack the password becomes too long depending on its length. In this article, we present a learning framework based on a set of dictionaries, variation rules, and fragment permutations. Dictionaries are organized from different perspectives of personal data, open sources, and groups of contexts. The naming and ordering of the dictionary help digital forensics examiners strategize and improve their chances of success in cracking alphanumeric passwords.