Abstract
Password guessing describes the process of finding a password for a secured system. Use cases include password recovery, IT forensics and measuring password strength. Commonly used tools for password guessing work with passwords leaks and use these lists for candidate generation based on handcrafted or inferred rules. These methods are often limited in their capability of producing entirely novel passwords, based on vocabulary not included in the given password lists. However, there are often semantic similarities between words and phrases of the given lists that are highly relevant for guessing the actual used passwords. In this paper, we propose SePass, a novel method that utilizes word embeddings to discover and exploit these semantic similarities. We compare SePass to a number of competitors and illustrate that our method not only is on par with these competitors, but also generates a significant higher amount of entirely novel password candidates. Using SePass in combination with existing methods, such as PCFG, improves the number of correctly guessed passwords considerably.
M. Hünemörder and L. Schäfer—Contributed equally to this research.
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References
Almeida, F., Xexéo, G.: Word embeddings: A survey. CoRR abs/1901.09069 (2019). http://arxiv.org/abs/1901.09069
Biesner, D., Cvejoski, K., Georgiev, B., Sifa, R., Krupicka, E.: Generative deep learning techniques for password generation (2020)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)
Burns, W.J.: Common password list (rockyou.txt) (2019). https://www.kaggle.com/wjburns/common-password-list-rockyoutxt
Cubrilovic, N.: Rockyou hack: From bad to worse (2009). https://techcrunch.com/2009/12/14/rockyou-hack-security-myspace-facebook-passwords/
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
Grave, E., Bojanowski, P., Gupta, P., Joulin, A., Mikolov, T.: Learning word vectors for 157 languages. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018) (2018)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. CoRR abs/1704.00028 (2017). http://arxiv.org/abs/1704.00028
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., et al.: Fast, lean, and accurate: Modeling password guessability using neural networks. In: Proceedings of the 25th USENIX Conference on Security Symposium, pp. 175–191. SEC’16, USENIX Association, USA (2016)
Miller, G.A.: WordNet: An electronic lexical database. MIT press (1998)
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, CCS 2005, pp. 364–372. Association for Computing Machinery, New York (2005)
Steube, J.: hashcat (2002). https://hashcat.net/hashcat/
Veras, R., Collins, C., Thorpe, J.: On the semantic patterns of passwords and their security impact, January 2014
Veras, R., Collins, C., Thorpe, J.: A large-scale analysis of the semantic password model and linguistic patterns in passwords. ACM Trans. Priv. Secur. 24(3), April 2021
Wang, S., Zhou, W., Jiang, C.: A survey of word embeddings based on deep learning. Computing 102(3), 717–740 (2020)
Weir, M., Aggarwal, S., Medeiros, B.d., Glodek, B.: Password cracking using probabilistic context-free grammars. In: 2009 30th IEEE Symposium on Security and Privacy, pp. 391–405 (2009)
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Hünemörder, M., Schäfer, L., Schüler, NS., Eichberg, M., Kröger, P. (2022). SePass: Semantic Password Guessing Using k-nn Similarity Search in Word Embeddings. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13726. Springer, Cham. https://doi.org/10.1007/978-3-031-22137-8_3
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