Hashcat: Advanced password recovery. https://hashcat.net/hashcat/
Hashcat: Advanced password recovery – Attacks Wiki. https://hashcat.net/wiki/
Hashcat: Advanced password recovery – Mask attack. https://hashcat.net/wiki/doku.php?id=mask_attack
Hashcat: Advanced password recovery – Rule-based attack. https://hashcat.net/wiki/doku.php?id=rule_based_attack
Hashcat: Advanced password recovery – Slow candidates mode. https://github.com/hashcat/hashcat/blob/master/docs/slow-candidates-mode.md
Bailey, D.V., Dürmuth, M., Paar, C.: Statistics on password re-use and adaptive strength for financial accounts. In: Abdalla, M., De Prisco, R. (eds.) SCN 2014. LNCS, vol. 8642, pp. 218–235. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10879-7_13
Blocki, J., Harsha, B., Zhou, S.: On the economics of offline password cracking. In: 2018 IEEE Symposium on Security and Privacy (SP), pp. 853–871. IEEE (2018)
Bond-Taylor, S., Leach, A., Long, Y., Willcocks, C.G.: Deep generative modelling: a comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models. IEEE Trans. Pattern Anal. Mach. Intell. 44, 7327–7347 (2021)
Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877–1901 (2020)
de Carné de Carnavalet, X., Mannan, M.: From very weak to very strong: analyzing password-strength meters. In: Network and Distributed System Security Symposium (NDSS 2014). Internet Society (2014)
Carnavalet, X.D.C.D., Mannan, M.: A large-scale evaluation of high-impact password strength meters. ACM Trans. Inf. Syst. Secur. (TISSEC) 18(1), 1–32 (2015)
Castelluccia, C., Dürmuth, M., Perito, D.: Adaptive password-strength meters from markov models. In: NDSS (2012)
Chowdhery, A., et al.: Palm: scaling language modeling with pathways. arXiv preprint arXiv:2204.02311 (2022)
Ciaramella, A., D’Arco, P., De Santis, A., Galdi, C., Tagliaferri, R.: Neural network techniques for proactive password checking. IEEE Trans. Depend. Secure Comput. 3(4), 327–339 (2006)
Das, A., Bonneau, J., Caesar, M., Borisov, N., Wang, X.: The tangled web of password reuse. In: NDSS, vol. 14, pp. 23–26 (2014)
Dell’Amico, M., Michiardi, P., Roudier, Y.: Password strength: an empirical analysis. In: 2010 Proceedings IEEE INFOCOM, pp. 1–9. IEEE (2010)
Dürmuth, M., Angelstorf, F., Castelluccia, C., Perito, D., Chaabane, A.: OMEN: faster password guessing using an ordered markov enumerator. In: Piessens, F., Caballero, J., Bielova, N. (eds.) ESSoS 2015. LNCS, vol. 8978, pp. 119–132. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15618-7_10
Feldmeier, D.C., Karn, P.R.: UNIX password security – ten years later. In: Brassard, G. (ed.) CRYPTO 1989. LNCS, vol. 435, pp. 44–63. Springer, New York (1990). https://doi.org/10.1007/0-387-34805-0_6
Golla, M., Beuscher, B., Dürmuth, M.: On the security of cracking-resistant password vaults. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications security, pp. 1230–1241 (2016)
Golla, M., Dürmuth, M.: On the accuracy of password strength meters. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 1567–1582 (2018)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc. (2014). https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
Greenbag, A.: Hackers are passing around a megaleak of 2.2 billion records (2019). https://www.wired.com/story/collection-leak-usernames-passwords-billions/
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. Adv. Neural Info. Process. Syst. 30 (2017)
Hitaj, B., Gasti, P., Ateniese, G., Perez-Cruz, F.: PassGAN: a deep learning approach for password guessing. In: Deng, R.H., Gauthier-Umaña, V., Ochoa, M., Yung, M. (eds.) ACNS 2019. LNCS, vol. 11464, pp. 217–237. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21568-2_11
Melicher, W., Ur, B., Segreti, S.M., Komanduri, S., Bauer, L., Christin, N., Cranor, L.F.: Fast, lean, and accurate: modeling password guessability using neural networks. In: 25th USENIX Security Symposium (USENIX Security 16), pp. 175–191 (2016)
Morris, R., Thompson, K.: Password security: a case history. Commun. ACM 22(11), 594–597 (1979)
Narayanan, A., Shmatikov, V.: Fast dictionary attacks on passwords using time-space tradeoff. In: Proceedings of the 12th ACM Conference on Computer and Communications Security, pp. 364–372 (2005)
OpenAI: Chatgpt: Optimizing language models for dialogue (2022). https://openai.com/blog/chatgpt/
Openwall: John the ripper markov generator. https://openwall.info/wiki/john/markov
Openwall: John the ripper password cracker. https://www.openwall.com/john/
Pagnotta, G., Hitaj, D., De Gaspari, F., Mancini, L.V.: PassFlow: Guessing passwords with generative flows. In: 2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). pp. 251–262. IEEE (2022)
Pal, B., Daniel, T., Chatterjee, R., Ristenpart, T.: Beyond credential stuffing: password similarity models using neural networks. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 417–434. IEEE (2019)
Pasquini, D., Ateniese, G., Bernaschi, M.: Interpretable probabilistic password strength meters via deep learning. In: Chen, L., Li, N., Liang, K., Schneider, S. (eds.) ESORICS 2020. LNCS, vol. 12308, pp. 502–522. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58951-6_25
Pasquini, D., Cianfriglia, M., Ateniese, G., Bernaschi, M.: Reducing bias in modeling real-world password strength via deep learning and dynamic dictionaries. In: 30th USENIX Security Symposium (USENIX Security 21), pp. 821–838 (2021)
Pasquini, D., Gangwal, A., Ateniese, G., Bernaschi, M., Conti, M.: Improving password guessing via representation learning. In: 2021 IEEE Symposium on Security and Privacy (SP), pp. 1382–1399. IEEE (2021)
Paterson, K.G., Stebila, D.: One-time-password-authenticated key exchange. In: Steinfeld, R., Hawkes, P. (eds.) ACISP 2010. LNCS, vol. 6168, pp. 264–281. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14081-5_17
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. California Univ San Diego La Jolla Inst for Cognitive Science, Technical report (1985)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Adv. Neural Inf. Process. Syst. 27 (2014)
Tomczak, J.M.: Deep Generative Modeling. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-030-93158-2
Touvron, H., et al.: Llama: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)
Ur, B., et al.: How does your password measure up? the effect of strength meters on password creation. In: USENIX Security Symposium, pp. 65–80 (2012)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)
Wayman, J.L., Jain, A.K., Maltoni, D., Maio, D.: Biometric Systems: Technology, Design and Performance Evaluation. Springer, Heidelberg (2005). https://doi.org/10.1007/b138151
Weir, M., Aggarwal, S., De Medeiros, B., Glodek, B.: Password cracking using probabilistic context-free grammars. In: 2009 30th IEEE Symposium on Security and Privacy, pp. 391–405. IEEE (2009)
Wheeler, D.L.: zxcvbn: low-budget password strength estimation. In: USENIX Security Symposium, pp. 157–173 (2016)
Whitney, L.: Billions of passwords leaked online from past data breaches (2021). https://www.techrepublic.com/article/billions-of-passwords-leaked-online-from-past-data-breaches/
Wikipedia: 2012 linkedin hack (2023). https://en.wikipedia.org/wiki/2012_LinkedIn_hack. Accessed 21 Jan 2023
Wikipedia: Rockyou (2023). https://en.wikipedia.org/wiki/RockYou#Data_breach. Accessed 21 Jan 2023
WikiSkull: Password datasets (2023). https://wiki.skullsecurity.org/index.php/Passwords. Accessed 21 Jan 2023
Wolf, T., et al.: Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38–45. Association for Computational Linguistics (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.6
Xu, M., Wang, C., Yu, J., Zhang, J., Zhang, K., Han, W.: Chunk-level password guessing: towards modeling refined password composition representations. In: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, pp. 5–20 (2021)
Yu, J., et al.: Vector-quantized image modeling with improved vqgan. arXiv preprint arXiv:2110.04627 (2021)