As network security issues continue gaining prominence, password security has become crucial in safeguarding personal information and network systems. This study first introduces various methods for system password cracking, outlines password defense strategies, and discusses the application of machine learning in the realm of password security. Subsequently, we conduct a detailed public password database analysis, uncovering standard features and patterns among passwords. We extract multiple characteristics of passwords, including length, the number of digits, the number of uppercase and lowercase letters, and the number of special characters. We then experiment with six different machine learning algorithms: support vector machines, logistic regression, neural networks, decision trees, random forests, and stacked models, evaluating each model's performance based on various metrics, including accuracy, recall, and F1 score through model validation and hyperparameter tuning. The evaluation results on the test set indicate that decision trees and stacked models excel in accuracy, recall, and F1 score, making them a practical option for the strong and weak password classification task.

翻译：随着网络安全问题日益凸显，密码安全在保护个人信息和网络系统方面变得至关重要。本研究首先介绍了系统密码破解的各种方法，概述了密码防御策略，并探讨了机器学习在密码安全领域的应用。随后，我们对公开密码数据库进行了详细分析，揭示了密码间的常见特征与模式。我们提取了密码的多种特征，包括长度、数字数量、大写与小写字母数量以及特殊字符数量。接着，我们实验了六种不同的机器学习算法：支持向量机、逻辑回归、神经网络、决策树、随机森林以及堆叠模型，通过模型验证和超参数调优，基于准确率、召回率和F1分数等多种指标评估了每个模型的性能。在测试集上的评估结果表明，决策树和堆叠模型在准确率、召回率和F1分数方面表现优异，使其成为强弱密码分类任务中的实用选择。