Vargas Martin, MiguelSannihith Lingutla, Satya2023-08-252023-08-252023-07-01https://hdl.handle.net/10155/1657Passwords play a major role in the field of network security and play as a first line of defense against attackers who gain unauthorized access to the profiles. However, passwords are vulnerable to various types of attacks making it essential to ensure that they are strong, unique, and confidential. One of the major techniques that evolved over time to enhance password security is the use of honeywords that are decoy passwords designed to alert the administrator when a data breach has happened. The main goal of this project is to addresses one of the limitations of a honeyword generation technique, called Chunk-GPT3, by performing better password segmentation through a re-engineered chunking algorithm that maps digits into characters, and which would seem to lead to better honeywords. We justify our re-engineering method and generate honeywords that we compare to those generated by Chunk-GPT3. Nonetheless, after evaluating honeywords using the HWSimilarity metric, the results suggest that improved chunking does not necessarily lead to better honeywords in all cases.enAuthenticationIntrusion detectionHoneywordsPasswordsLanguage modelsMaster's Project