Neural Networks Help Users Pick More-Secure Passwords

Neural networks trained to learn attackers’ approaches to brute-force password guessing can be used as a way to enforce minimal password security without resorting to large blocklists and cumbersome combinations of letters, numbers, and special symbols, a research team at Carnegie Mellon University conclude in a new paper. Using a neural network model built into a password-strength meter and recruiting users through Amazon’s Mechanical Turk, the researchers at CMU’s CyLab Security and Privacy Institute evaluated a series of different password recommendations, from eight-character passwords using a single class (letters, for example) to 16-character passwords using four classes — lowercase letter, uppercase letter, numbers, and symbols — as well as different blocklists.

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