A Study of Probabilistic Password Models
By Jerry Ma, Weining Yang, Min Luo, Ninghui Li. IEEE Symposium on Security and Privacy, 2014
315 citations recorded by the source corpus.
Subfields: Information Systems, Signal Processing
View Paper: https://www.semanticscholar.org/paper/c2d3d087579d86838bd6f0f8b38539c187baf8ba
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Ping Wang
- Ding Wang
- Saranga Komanduri
- Xinyi Huang
- Lorrie Faith Cranor
- Giuseppe Ateniese
- Jeff Yan
- William Melicher
In Signal Processing
- Ping Wang
- Ding Wang
- Fernando Perez-Cruz
- Xinyi Huang
- Lorrie Faith Cranor
- Giuseppe Ateniese
- Jeff Yan
- Briland Hitaj
Citations in the corpus
- Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks (2016, methodology): “Like in Markov models [34, 65], we rely on a special password-ending symbol to model the probability of ending a password after a sequence of characters.”
- PassGAN: A Deep Learning Approach for Password Guessing (2017, methodology): “t, require no special hardware or software, and are familiar to users and developers [37]. Unfortunately, multiple password database leaks have shown that users tend to choose easy-to-guess passwords [15, 20, 49], primarily composed of common strings (e.g., password, 123456, iloveyou), and variants ”
- A Security Analysis of Honeywords (2018, unknown): “A number of smoothing methods have been proposed for language modeling, such as Laplace and Good-Turing, and they have been widely used in password studies [22], [28].”
- Targeted Online Password Guessing: An Underestimated Threat (2016, methodology): “Among the nine algorithms to be evaluated, three (i.e., Markov [18], PCFG [27] and Trawling optimal [5]) only need some training passwords, four (i.e., Das et al.’s algo-rithm [11], TarGuess-II (cid:24) IV) work on password pairs of the same user, and four (i.e., Personal-PCFG [17], TarGuess-I, III…”
- Zipf’s Law in Passwords (2017, unknown): “[33] introduced natural language processing techniques, such as smoothing and normalization into Markov-based password cracking algorithms.”
- Two Birds with One Stone: Two-Factor Authentication with Security Beyond Conventional Bound (2018, background): “As a result, even if passwords are stored in salted-hash, this poses no real obstacle for an attacker to recover them by an overwhelming percentage (see [9]) by using modern probabilistic cracking techniques [10] and common hardware like GPUs.”