Despite the adoption of complex password policies, users often create passwords that follow predictable patterns involving dictionary words, numbers, and symbols. Traditional tokenization techniques used for password analysis frequently overlook or misclassify symbolic and numeric elements, resulting in incomplete strength evaluations and less effective cracking strategies. This study presents a Breadth-First Search (BFS)-based tokenization framework that systematically segments passwords into meaningful components, including words from dictionaries, numeric sequences, and symbolic tokens. The BFS algorithm examines all possible substring combinations to identify the most comprehensive segmentation path. Remaining unmatched symbols and numbers are processed in a dedicated post-analysis phase to ensure complete token representation. Experiments conducted on a real-world dataset of 100,000 passwords demonstrate that the proposed approach outperforms baseline tokenizers in terms of token coverage and segmentation accuracy, while maintaining efficient processing times. The improved tokenization results contribute to a more accurate assessment of password complexity and support the development of stronger password-cracking models. These findings emphasize the importance of structure-aware parsing methods in advancing password security analysis.
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