Differential Fault Analysis of NORX

Amit Jana, Dhiman Saha, G. Paul
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引用次数: 2

Abstract

In recent literature, there has been a particular interest in studying nonce-based Authenticated Encryption (AE) schemes in the light of fault-based attacks as they seem to present automatic protection against Differential Fault Attacks (DFA). In this work, we present the first DFA on nonce-based CAESAR scheme NORX (applicable to all the versions v1, v2.0, v3.0). We demonstrate a scenario when faults introduced in NORX in parallel mode can be used to collide the internal branches to produce an all-zero state. We later show how this can be used to replay NORX despite being instantiated by different nonces, messages. Once replayed, we show how the key of NORX can be recovered using secondary faults and using the faulty tags. We use different fault models to showcase the versatility of the attack strategy. A detailed theoretical analysis of the expected number of faults required under various models is also furnished. Under the random bit-flip model, around 1384 faults need to be induced to reduce the key-space from 2128 to 232 while the random byte-flip model requires 332 faults to uniquely identify the key. To the best of our knowledge, this is the first fault attack that uses both internal and classical differentials to mount a DFA on a nonce-based authenticated cipher which is otherwise believed to be immune to DFA.
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NORX的微分故障分析
在最近的文献中,鉴于基于故障的攻击,人们对研究基于非基于故障的身份验证加密(AE)方案特别感兴趣,因为它们似乎提供了针对差分故障攻击(DFA)的自动保护。在这项工作中,我们提出了基于nonce的CAESAR方案NORX(适用于所有版本v1、v2.0、v3.0)上的第一个DFA。我们演示了一个场景,在并行模式下,NORX中引入的故障可以用来碰撞内部分支以产生全零状态。稍后我们将展示如何使用它来重放NORX,尽管它是由不同的消息实例化的。重播后,我们将展示如何使用次要错误和错误标记恢复NORX的密钥。我们使用不同的故障模型来展示攻击策略的多功能性。对各种模型下所需的预期故障数进行了详细的理论分析。在随机比特翻转模型下,大约需要诱导1384个错误才能将密钥空间从2128个减少到232个,而随机字节翻转模型需要诱导332个错误才能唯一识别密钥。据我们所知,这是第一次使用内部和经典差分将DFA挂载到基于随机数的身份验证密码上的错误攻击,否则被认为对DFA免疫。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Differential Fault Analysis of NORX PRINCE under Differential Fault Attack: Now in 3D Building a Modern TRNG: An Entropy Source Interface for RISC-V Formidable Challenges in Hardware Implementations of Fully Homomorphic Encryption Functions for Applications in Machine Learning SoK
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