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Deepmnemonic: Password Mnemonic Genera- Tion Via Deep Attentive Encoder-Decoder Model

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Deepmnemonic: Password Mnemonic Genera- Tion Via Deep Attentive Encoder-Decoder Model 1 DeepMnemonic: Password Mnemonic Genera- tion via Deep Attentive Encoder-Decoder Model Yao Cheng, Chang Xu, Zhen Hai*, and Yingjiu Li Abstract—Strong passwords are fundamental to the security of password-based user authentication systems. In recent years, much effort has been made to evaluate the password strength or to generate strong passwords. Unfortunately, the usability or memorability of the strong passwords has been largely neglected. In this paper, we aim to bridge the gap between strong password generation and the usability of strong passwords. We propose to automatically generate textual password mnemonics, i.e., natural language sentences, which are intended to help users better memorize passwords. We introduce DeepMnemonic, a deep attentive encoder-decoder framework which takes a password as input and then automatically generates a mnemonic sentence for the password. We conduct extensive experiments to evaluate DeepMnemonic on the real-world data sets. The experimental results demonstrate that DeepMnemonic outperforms a well-known baseline for generating semantically meaningful mnemonic sentences. Moreover, the user study further validates that the generated mnemonic sentences by DeepMnemonic are useful in helping users memorize strong passwords. Index Terms—Password mnemonic generation, deep neural network, attention mechanism, neural machine translation. F 1 INTRODUCTION OWADAYS, user authentication is the key to ensuring made to address the memorability of strong passwords. N the security of user accounts for most online services, Strong passwords are usually difficult for users to memorize such as social media, e-commerce, and online banking. because their entropy values are beyond many users’ mem- Although various authentication schemes have emerged orability [13] [14] [15]. The memorability of strong password in recent years, e.g., pattern-based or biometric-based au- has become one of the biggest hindrances to the wide thentication, the password-based authentication remains a adoption of strong passwords in real-world applications. prevailing choice in most real-world applications, whose One possible approach to addressing this problem is security relies on the difficulty in cracking passwords. to generate passwords that are not only secure but also Choosing strong passwords becomes extremely important easy to remember. Different strategies have been applied to and necessary. generating word-based memorable passwords, such as pro- In reality, service providers often present password nounceable passwords [16] [17], meaningful passwords [18], policies to aid users in creating strong passwords. Such and passwords concatenating random words [19]. Unfor- policies may require that a password be longer than a pre- tunately, some of such strategies were demonstrated to be defined minimum length, or contain multiple types of characters vulnerable to certain attacks [18] [20], and moreover, there (letters, numbers, and special characters). Such policies are is no theoretical guarantee that the generated passwords are expected to guide the generation of passwords that are indeed strong. In addition, various user-defined rules were resistant to password attacks [1], but users tend to choose adopted for the generation of expression-based memorable passwords that are easy to memorize in practice [2]. As passwords [21] [20] [22]. One limitation of this approach a result, password policies may not be as effective as is that the strength of the generated passwords largely arXiv:2006.13462v1 [cs.CR] 24 Jun 2020 expected [3]. depends on the uncertainty of the phrases chosen by users. Much effort has been made to evaluate the strength of This approach also tends to suffer from the low quality of passwords [4] [5] [6] or to generate strong passwords [7] the generation rules [22]. [8] [9]. Various methods, on the other hand, have been Instead of generating strong and memorable passwords, presented to crack passwords, assess whether passwords recent effort has been made to assist users in remembering are sufficiently strong or not, or reduce password guess- passwords using external tools, such as helper cards [23], ability [10] [11] [12]. However, no rigorous effort has been hint images [24] [25] and engaging games [26]. In particular, Jeyaraman and Topkara [27] proposed a heuristic method that relies on textual hint headlines to deal with the • Yao Cheng is with Huawei International, Singapore. E-mail: [email protected] memorability issue of strong passwords. Given a password, • Chang Xu is with Data61, CSIRO, Australia. E-mail: they proposed to search and find an existing natural language [email protected] headline that suggests the password from a given corpus • *Corresponding author. Zhen Hai is with Institute for Infocomm Research, (i.e., Reuter Corpus Volume 1). If the search is unsuccessful, A*STAR, Singapore. E-mail: [email protected] • Yingjiu Li is with Department of Computer and Information Science, they would then use an external semantic lexicon named University of Oregon, Eugene, Oregon, United States. E-mail: WordNet [28] to replace certain words in an existing headline [email protected] with their synonyms. In this way, they could find a headline Manuscript submitted on June 3, 2019; accepted on April 2, 2020. or a variant headline and use it as a hint for memorizing a 2 given password. This approach is subject to the following Our experimental results show that DeepMnemonic limitations: (i) It can only handle the passwords composed achieves 99.09% MP (Mnemonic Proportion) and of alphabetic characters. Manual intervention is needed to 16.47 BLEU-4 (BiLingual Evaluation Understudy), tackle digits or special characters, which are non-trivial outperforming an n-gram language model baseline for the composition of strong passwords in reality. (ii) (83.62% MP and 5.09 BLEU-4). Meaningful hint headlines are often missing by simply • We conduct a user study to qualitatively evaluate the searching the given corpus while creating variant headlines helpfulness of DeepMnemonic. The results demon- at the word level by using an external lexicon may result in strate that DeepMnemonic helps users with 54.47% syntactically incorrect or semantically inconsistent headline decrease in time spent on remembering passwords sentences. (iii) It only works for short passwords that consist and 57.14% decrease in recall error measured by of 6 or 7 characters due to the limited lengths of the the edit distance between each pair of the given headlines in the corpus. password and its recalled version. In this work, given strong passwords, we propose to The rest of this paper is organized as follows. Section 2 automatically generate mnemonics, i.e., natural language describes the password mnemonic generation problem sentences, to bridge the gap between security and mem- and its application scenario. Section 3 introduces orability of the passwords. Specifically, we introduce a DeepMnemonic, a deep attentive encoder-decoder new mnemonic generation system named DeepMnemonic, system that can generate mnemonic sentences for the which is capable of generating a semantically meaningful given textual passwords. In Section 4 and Section 5, we mnemonic sentence for each given textual password. The evaluate DeepMnemonic quantitatively and qualitatively in core of DeepMnemonic is an attentive encoder-decoder experiment and user study, respectively. Section 6 discusses neural network. It learns to transform any given user several usability issues of DeepMnemonic. Section 7 password into a mnemonic sentence by first encoding summarizes the related work, and Section 8 concludes this the password character sequence and then decoding the paper. encoded information into the mnemonic sentence via a specially designed attention strategy. Both encoder and decoder are implemented with recurrent neural networks 2 PROBLEM STATEMENT AND APPLICATION SCE- that can capture the contextual dependency among pair- NARIO wise input passwords and mnemonic sentences. The key insight of DeepMnemonic is inspired by a cognitive psy- Given a strong textual password that often consists of chology theory [29], which states that the human ability to random characters, the aim of this work is to assist common memorize and recall certain information is positively influ- users in remembering the password properly. According enced by associating additional semantic content with the to a famous cognitive psychology theory [29], in order to information [27]. The natural language mnemonic sentences memorize a piece of information, it is helpful to associate generated by DeepMnemonic can provide such semantically some external tools or contextual contents with the informa- meaningful content for users to remember and recall the tion. More specifically, the cognitive psychology theory [29] given strong passwords. explains that the information to be remembered can be a list Different from the existing textual password hint sys- of items, for example, a list of characters in a password. For tems [27], DeepMnemonic enjoys several unique properties: the semantic content, it is not limited to a vivid image rep- (i) Feature-free: DeepMnemonic is an end-to-end solution, resenting, symbolizing, or suggesting each item of semantic which aims to directly map a given textual password to its information. It indicates that, in order to better memorize corresponding mnemonic sentence, and no manual feature a piece of information, users can construct such semantic engineering is needed in the
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