Research on Password-Cracking Methods Based on Machine Learning

Password Security is the first line of defence in the digital world. Traditional password defence relies on preset rules. Attackers use massive leaked data to train models to mine the Semantic Association rules of human passwords deeply; the automatic generation of highly personalised obfuscation rules not only greatly improves the cracking efficiency but also significantly enhances the concealment and adaptability of attacks, making the traditional protection methods based on the blacklist and rule-based increasingly ineffective. Therefore, this paper discusses the technical path of machine learning in password cracking, aiming to provide a reference for constructing a dynamic defence framework and developing a human-computer collaborative password design paradigm.