论文标题

成本 - 对称内存硬密码哈希

Cost-Asymmetric Memory Hard Password Hashing

论文作者

Bai, Wenjie, Blocki, Jeremiah, Ameri, Mohammad Hassan

论文摘要

在过去的十年中,数十亿个用户密码暴露于离线密码破解攻击的危险威胁。偷走了用户密码加密哈希的一个离线攻击者可以检查密码猜测的数量,因为他/他喜欢仅受他/他愿意投资以破解密码的资源的限制。胡椒粉和钥匙拉伸是两种技术,通过增加猜测成本来阻止离线攻击者。胡椒确保拒绝不正确密码猜测的成本高于验证正确密码猜测的(预期)成本。这很有用,因为大多数离线攻击者的猜测都是不正确的。不幸的是,正如我们观察到传统的辣椒防御似乎与现代记忆硬键伸的算法(例如Argon2或Scrypt)不相容。我们介绍了一种替代胡椒的替代方法,我们称之为成本 - 对称内存硬密码身份验证,该验证受益于与经典的辣椒防御的相同成本空调的好处,即拒绝不正确密码的成本猜测不正确的密码猜测比预期的成本要大于确定正确的密码猜测的预期成本。正确配置后,我们证明我们的机制只能减少由理性离线攻击者破裂的用户密码的百分比,该密码的目标是最大化(预期)利润,即破裂密码的总价值减去总猜测成本。我们评估了我们的机制对经验密码数据集的有效性,以对有理的离线攻击者进行评估。我们的经验分析表明,我们的机制可以大大减少由理性攻击者破解高达10%的用户密码的百分比。

In the past decade, billions of user passwords have been exposed to the dangerous threat of offline password cracking attacks. An offline attacker who has stolen the cryptographic hash of a user's password can check as many password guesses as s/he likes limited only by the resources that s/he is willing to invest to crack the password. Pepper and key-stretching are two techniques that have been proposed to deter an offline attacker by increasing guessing costs. Pepper ensures that the cost of rejecting an incorrect password guess is higher than the (expected) cost of verifying a correct password guess. This is useful because most of the offline attacker's guesses will be incorrect. Unfortunately, as we observe the traditional peppering defense seems to be incompatible with modern memory hard key-stretching algorithms such as Argon2 or Scrypt. We introduce an alternative to pepper which we call Cost-Asymmetric Memory Hard Password Authentication which benefits from the same cost-asymmetry as the classical peppering defense i.e., the cost of rejecting an incorrect password guess is larger than the expected cost to authenticate a correct password guess. When configured properly we prove that our mechanism can only reduce the percentage of user passwords that are cracked by a rational offline attacker whose goal is to maximize (expected) profit i.e., the total value of cracked passwords minus the total guessing costs. We evaluate the effectiveness of our mechanism on empirical password datasets against a rational offline attacker. Our empirical analysis shows that our mechanism can reduce significantly the percentage of user passwords that are cracked by a rational attacker by up to 10%.

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