Honeywords are decoy passwords associated with each user account to timely detect password leakage. The key issue lies in how to generate honeywords that are hard to be differentiated from real passwords. This security mechanism was first introduced by Juels and Rivest at CCS’13, and has been covered by hundreds of media and adopted in dozens of research domains. Existing research deals with honeywords primarily in an ad hoc manner, and it is challenging to develop a secure honeyword-generation method and well evaluate (attack) it. In this work, we tackle this problem in a principled approach. We first propose four theoretic models for characterizing the attacker $\mathcal{A}$’s best distinguishing strategies, with each model based on a different combination of information available to $\mathcal{A}$ (e.g., public datasets, the victim’s personal information and registration order). These theories guide us to design effective experiments with real-world password datasets to evaluate the goodness (flatness) of a given honeyword-generation method.Armed with the four best attacking theories, we develop the corresponding honeyword-generation method for each type of attackers, by using various representative probabilistic password guessing models. Through a series of exploratory investigations, we show the use of these password models is not straightforward, but requires creative and significant efforts. Both empirical experiments and user-study results demonstrate that our methods significantly outperform prior art. Besides, we manage to resolve several previously unexplored challenges that arise in the practical deployment of a honeyword method. We believe this work pushes the honeyword research towards statistical rigor.

Password strength meters (PSMs) are being widely used, but they often give conflicting, inaccurate and misleading feedback, which defeats their purpose. Except for fuzzyPSM, all PSMs assume passwords are newly constructed, which is not true in reality. FuzzyPSM considers password reuse, six major leet transformations and initial capitalization, and performs the best as evaluated by Golla and Dürmuth at ACM CCS’18. On the basis of fuzzyPSM, we propose a new PSM based onReuse,Leet andSeparation, namely RLS-PSM. First, we classify password reuse behaviors into capitalization and those that use special characters for leet or separation, and calculate the corresponding probabilities. Then, to balance efficiency and precision, we use Long Short-Term Memory to calculate the probabilities of alphanumeric strings. Besides, we propose to usebenchmark passwordsto show therelative strengthof a password. Due to the varied impacts of different service types and diversified economic value of websites, we consider parameter settings of RLS-PSM under six different service types. Finally, we use the Monte Carlo method and weighted Spearman coefficient to measure and compare the robustness and accuracy of RLS-PSM, leading PSMs (including Markov-based PSM, PCFG-based PSM, fuzzyPSM, RNN, and Zxcvbn), and password cracking tools (including JtR and Hashcat). We find that the robustness of RLS-PSM is significantly higher than all counterparts whenevaluating attempts> 104(e.g., on average, Fraction of Successfully Evaluated passwords of RLS-PSM is 18.9% higher than fuzzyPSM). The accuracy of RLS-PSM is also better than other mainstream PSMs used for comparison in this paper, except for fuzzyPSM.

As an important application of the Internet of Things, smart home has greatly facilitated our life. Since the communication channels of smart home are insecure and the transmitted data are usually sensitive, a secure and anonymous user authentication scheme is required. Numerous attempts have been taken to design such authentication schemes. Recently, Shuai et al. (Computer & Security 86(2019):132146) designed an anonymous authentication scheme for smart home using elliptic curve cryptography. They claimed that the proposed scheme is secure against various attacks and provides ideal attributes. However, we show that their scheme cannot resist inside attack and offline dictionary attack and also fails to achieve forward secrecy. Furthermore, we give some suggestions to enhance the security of the scheme. These suggestions also apply to other user authentication schemes with similar flaws.