DeepMnemonic: Password Mnemonic Generation via Deep Attentive Encoder-Decoder Model

TL;DR

DeepMnemonic is a deep learning model designed to generate natural language mnemonics for passwords, improving memorability without compromising strength, validated through experiments and user studies.

Contribution

It introduces a novel deep attentive encoder-decoder framework for automatic mnemonic generation, bridging password strength and usability.

Findings

DeepMnemonic outperforms baseline models in generating meaningful mnemonics.

User studies show mnemonics from DeepMnemonic aid password memorization.

The approach enhances password usability without sacrificing security.

Abstract

Strong passwords are fundamental to the security of password-based user authentication systems. In recent years, much effort has been made to evaluate password strength or to generate strong passwords. Unfortunately, the usability or memorability of the strong passwords has been largely neglected. In this paper, we aim to bridge the gap between strong password generation and the usability of strong passwords. We propose to automatically generate textual password mnemonics, i.e., natural language sentences, which are intended to help users better memorize passwords. We introduce \textit{DeepMnemonic}, a deep attentive encoder-decoder framework which takes a password as input and then automatically generates a mnemonic sentence for the password. We conduct extensive experiments to evaluate DeepMnemonic on the real-world data sets. The experimental results demonstrate that DeepMnemonic outperforms a well-known baseline for generating semantically meaningful mnemonic sentences. Moreover, the user study further validates that the generated mnemonic sentences by DeepMnemonic are useful in helping users memorize strong passwords.