| Abstrakt: |
Honey vaults are useful tools for password management. A vault usually contains usernames for each domain, and the corresponding passwords, encrypted with a master password chosen by the owner. By generating decoy vaults for incorrect master password attempts, honey vaults force attackers with the vault's storage file to engage in online verification to distinguish the real vaults, thus thwarting offline guessing attacks. However, sophisticated attackers can acquire additional information, such as personally identifiable information (PII) and partial passwords contained within the vault from various data breaches. Since many users tend to incorporate PII in their passwords, attackers may utilize PII to distinguish the real vault. Furthermore, if attackers may learn partial passwords included in the real vault, it can exclude numerous decoy vaults without the need for online verification. Indeed, both leakages pose serious threats to the security of the existing honey vault schemes. In this paper, we explore two attack variants of the inspired attack scenario, where the attacker gains access to the vault's storage file along with acquiring PII and partial passwords contained within the real vault, and design a new honey vault scheme. For security assurance, we prove that our scheme is secure against one of the aforementioned attack variants. Moreover, our experimental findings suggest enhancements in security against the other attack. In particular, to evaluate the security in multiple leakage cases where both the vault's storage file and PII are leaked, we propose several new practical attacks (called PII-based attacks), building upon the existing practical attacks in the traditional single leakage case where only the vault's storage file is compromised. Our experimental results demonstrate that certain PII-based attacks achieve a 63–70% accuracy in distinguishing the real vault from decoys in the best-performing honey vault scheme (Cheng et al. in Incrementally updateable honey password vaults, pp 857–874, 2021). Our scheme reduces these metrics to 41–50%, closely approaching the ideal value of 50%. [ABSTRACT FROM AUTHOR] |
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