伯努利诱饵密码
Bernoulli honeywords
AI总结：
本文提出将诱饵密码选择视为Bernoulli过程的策略，证明其能解析推导误报率且不受攻击者信息影响，并在两种现有系统设计中展现出有效性与性能提升。
摘要：
植入凭证数据库中的诱饵密码（即“honeywords”）一旦在登录尝试中被提交，就能向网站发出其遭到入侵的警报。为了发挥效用，一些honeywords必须看起来至少与真实密码一样像是用户选择的密码，并且在未入侵数据库的情况下极难被猜中，以防止产生错误的入侵警报。然而，对于启发式的honeyword生成算法而言，这些目标已被证明难以实现。在本文中，我们探索了一种替代策略：防御者将honeyword选择视为一个Bernoulli过程，其中每个可能的密码（用户选择的密码除外）都以某个固定概率被独立选作honeyword。我们展示了如何将Bernoulli honeywords集成到两种利用honeywords的现有系统设计中：一种基于honeychecker（在账户密码列表中存储用户所选密码的秘密索引），另一种则完全不利用秘密状态。我们证明了Bernoulli honeywords能够对错误入侵检测概率进行解析推导，无论攻击者收集了关于站点用户的何种信息；其真实和错误的入侵检测概率展现出了令人信服的有效性；并且Bernoulli honeywords甚至能够在现代honeyword系统设计中实现性能提升。
英文摘要：
Decoy passwords, or "honeywords," planted in a credential database can alert a site to its breach if ever submitted in a login attempt. To be effective, some honeywords must appear at least as likely to be user-chosen passwords as the real ones, and honeywords must be very difficult to guess without having breached the database, to prevent false breach alarms. These goals have proved elusive, however, for heuristic honeyword generation algorithms. In this paper we explore an alternative strategy in which the defender treats honeyword selection as a Bernoulli process in which each possible password (except the user-chosen one) is selected as a honeyword independently with some fixed probability. We show how Bernoulli honeywords can be integrated into two existing system designs for leveraging honeywords: one based on a honeychecker that stores the secret index of the user-chosen password in the list of account passwords, and another that does not leverage secret state at all. We show that Bernoulli honeywords enable analytic derivation of false breach-detection probabilities irrespective of what information the attacker gathers about the sites' users; that their true and false breach-detection probabilities demonstrate compelling efficacy; and that Bernoulli honeywords can even enable performance improvements in modern honeyword system designs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
