PassGAN:一种关于密码破译的深度学习方法(二)

II．背景和相关工作

在这部分，我们展示关于深度学习和GANs的简短概述。然后，我们回顾密码破译的最新情况。

A．    深度学习

在90年代中期，一些机器学习方法，比如support vector machines[64],random forests[7]，和Gaussianprocesses[60]对大多数无关联人工工程（手动编码）特征的分离和回归效果显著。在21世纪00年代中期，随着存储和数据可用性的增涨，这些方法已经被深度学习取代。对深度学习的研究表明，可以有效地从数据中学习特征，而且手工编码的特点往往不入学习的特征。这些收益与相关联的特征有关，这些收益在人工工程方面的特征可能只有低纬编码特征。

深度学习被广泛应用于解决各种问题，比如，与电脑视觉[39]，图像处理[70]，视频处理[16],[50]，语音识别[26]，自然语言识别[2],[12],[79]，或者博弈[24],[36],[45],[47]相关的问题。近年来, 在健康相关问题上使用深层学习也有了显著的改进[13],[19]。

深度学习在数据使用中已经提出一些隐私问题，从训练模型中可以学到什么，以及模型可以学习到比给定任务更多的私人信息的能力。由于这个原因，研究人员提出了隐私保护协作学习技术，这个技术依赖于差别隐私。然而，近期的工作表明，这些技术不是一开始想的那样保护隐私。特别是对方表明即使用隐私保护联合学习技术训练模型，训练模块仍然会受信息泄露，模块反演攻击的影响[31]。

除了攻击那些从训练模块中提取出来的信息，最近发现可以细微的修改样例，这样它们在人类的眼睛里就好像没有被修改。但是始终被深度学习算法错误分类[52],[52],[43],[8],[9],[35],[41],[30]。与此同时，对此类问题已经提出了一些对策[53],[77]。然而，这仍然是一个开放的研究课题。

B．    生成对抗网络

生成对抗网络（GANs）代表了深度学习领域中的一个进步。GAN是由两个神经网络组成的，一个是生成式深度神经网络G，和一个判别式深度神经网络D。给定一个数据集L={X1，X2，……,Xn}，G的目标是从潜在的概率分布Pr（x）中生成能够被D接受“伪”样例。与此同时，D的目标是尝试从真实样本I中分辨出G中的伪样本。更常见的是，输入一个简单的噪声分布Z，是被GANs解决了这个问题的优化。可以按照如下概括：

这个模块尝试最小化 θG ,同时最大化θD。学习阶段认为是完整的,当D不能够从区分出由G生产伪样本和I生产的真实样本产品。

自Goodfellow等人的初始工作以来[23]，GANs得到很多提高。Ragford等人提出了DCGAN[59]，DCGAN通过使用卷积神经网络代替多层感知器得到改进[23]。作为结果，和GANs相比，DCGAN能够产生更加可靠地图像样本[23]。

其他的基于GANs上的工作包括BEGAN[5],DiscoGAN[33],ConditionalGAN[46]，AdaGAN[73],InfoGAN[11]，Laplacian Pyramid GAN[15]和StackGAN[78]。这些技术对之前的工作做出了改进，比如新的训练和使用GAN的方法。

Arjovsky等人提出了WassersteinGAN（WGAN）[3]。WGAN通过梯度裁剪，提高了以前的GAN的学习的稳定性。这种方法的好处包括减少模式崩溃，并且通过有意义的学习曲线有助于确定最佳超参数。

上面所有的工作都重点研究写实图像的生成。为了解决篇章生成的问题，Gulrajani等人，最近提出了Improved Wasserstein GAN(IWGAN)[27]。通过IWGAN，D和G都是简单卷积神经网络（CNNs）。通过G输入一个潜在的噪声向量，通过卷积层转发进行变换，并且输出一个有32个one-hot字符的向量。一个非线性的softmax应用于G的输出，之后转发到D。每一个IWGAN的输出字符都通过计算每一个由G生成的argmax的输出向量[57]。

C．    密码破译

在密码爆破攻击中，对手会通过重复测试多个备选密码，尝试识别一个或多个用户的密码。密码破译攻击可能跟密码的诞生的历史一样古老[6]，更正式的研究可以追溯到1979年[48]。

有两个非常受欢迎的现代密码破译工具Johnthe Ripper（JTR）[71]和HashCat[28]。这两个工具实施多种类型的密码破译策略，包括：基于字典的攻击[63],[62]；基于规则的攻击[72]，[56]，其中包括从字典里的单词转换生成密码猜想。和基于Morkov模块的攻击，在这一攻击中，选中的密码的每一个字符都通过随机工程，考虑一个或多个前面的字符，以及在明文密码字典上训练的。JTR和hashCAT在密码破译方向都非常有效，尤其是，这里有一些例子，其中超过90%的来自线上服务的密码泄露已经成功恢复[57]。

最初，Narayanan等人把Markov模块用于生成密码破译[49]。他们的方法是使用手动定义的密码规则。比如生成的密码的哪一部分是由字符和数字组成。这种技术随后由Weir等人作了升级[75]。Weir声明如何通过密码分发“学习”这些规则。这些早期的工作随后由Ma[42]和Durmuth[18]等人进行扩展。基于Markov模块的技术已经用来实施实时密码强度估计，以及评估明文数据库中的密码强度（参见[14],[10]）。

Probabilisticcontext-free grammars (PCFGs)[32],[75]利用密码结构上的手工编码信息生成新的猜想。这一信息可以是隐含的（比如一个字典字后面跟着用户的生日）也可以是直言的（比如,要求密码至少含有六位字符，一个大写字符和一个数字）。之后从结果文法中随机选择合适的标记构造密码。

最近Melicher团队[44]介绍了一个基于重复性神经网络的密码破译的方法[25],[69]。通过这一技术，训练神经网络使用来自多个网站泄露的密码。在密码生成期间，神经网络一次生成一个密码字符。每个新字符（包括特殊结束符）都是基于给出的密码的可能性选取的，与基于Markov模块的想法相似（这一技术也同样应用[44]执行实时密码强度评估）。用于[44]的评估表明他们的技术在进行大批量的密码破译时优于PCTGs，Markov模块，和用于JTR和HashCat的密码构造规则（通常在10^10到10^15范围内）。

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*参考来源：PassGAN: A Deep Learning Approach for Password Guessing，丁牛网安实验室小编EVA编辑，如需转载请标明出处及引用。