PassGAN:一种关于密码破译的深度学习方法(二)

在这部分,我们做一个关于深度学习和GAN的简短概述。然后,我们回顾密码破译的最新情况。

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:

是是是.png

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。

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