改进的MD4有意义碰撞攻击

在FSE 1996上,Hans Dobbertin给出了一个基于ASCII编码且前20个字符是随机字符的有意义的MD4碰撞。贾珂婷和王小云教授于2009年给出了一个基于Latin-1 字符集的有意义的MD4碰撞。以王小云教授的模差分方法为基础,采用于红波等在CANS 2005上给出的碰撞路线,给出了两个有意义的MD4碰撞实例,其中一个是基于GBK编码的汉语的有意义碰撞,另一个是基于UTF-8编码的英语的有意义的碰撞。同时给出了一个python脚本被篡改的实例。     

一个新的MD4消息差分

在对Hash函数MD4的已知碰撞攻击方法研究的基础上,提出了一个新的分析思路——在差分路径的第3轮中不再构造局部碰撞,并给出了一条全新的差分路径。结果表明：新的差分路径在第3轮中不存在充分条件需要满足,以此路径构造的MD4碰撞攻击效率与以往攻击结果相比最优,计算复杂度不超过1次MD4运算。     

带认证邮局协议的密钥恢复攻击

作者提出了一种新的针对带认证邮局协议的密钥恢复攻击,能够更快地恢复出密钥并能够恢复更多的密钥字符.基于通道技术和高级消息修改技术,提出了一种“群满足方案”来确定性地满足分而治之策略下最后一个通道首三步的所有充分条件,籍此提高MD5 (Message Digest Algorithm 5)碰撞对搜索的效率.并提出了一些新的通道来控制MD5碰撞对消息的更多比特的取值,比如可以构造出352比特值确定的MD5碰撞对.通过这些技术改进了多位信息确定的MD5碰撞对搜索效率,应用到APOP的密钥恢复攻击中不仅能够快速恢复长达31个字符的密钥,而且能够在实际时间内恢复长达43个字符的密钥.     

MD5碰撞攻击的差分路径构建

著名的杂凑算法MD5是MD4的增强版本,由Ronald L.Rivest在1991年设计.MD5广泛应用于口令变换、数据完整性、数字证书等领域.近年最具影响的MD5安全性的分析结果是王小云首次发现的MD5碰撞.之后,MD5碰撞攻击的改进主要集中在提高碰撞对搜索的性能.基于新的不同于王小云的明文差分构成的MD5碰撞,介绍差分路径的构建方法,并给出一条差分路径以及碰撞对数据.     

获取外部数据源的一种实用方法

介绍了一种简单实用的利用汉字及ASCII字符的编码特征将外部电子表格导入数据库中的方法,并给出了其在PowerBuilder环境下的实现过程。该方法主要用于将文本文件、Word文档及Excel电子表格中的数据直接导入到数据库中,极大地方便了数据的处理。     

Hash差分攻击算法研究

Hash函数广泛应用于商业.军事等领域,因此对Hash算法的攻击在理论上和实际应用上都有重要的意义.自王小云教授提出差分攻击算法并攻破SHA-1,MD5,RIPEMD,MD4以来,对该算法的研究日益受到关注.然而王教授没有给出如何寻找差分和差分路径的方法.国内外专家都猜测她是靠非凡的直觉手工完成的,如何寻找差分和差分路径的方法成为关注的热点.构造差分路径涉及到如何处理差分循环移位和选择高概率的充分条件.业已证明,一般情况下,差分位移后有4种情况,并给出了4种情况的概率,最后比较了4种情况的概率.     

WPS密码设置分析与解密

本文对ＷＰＳ文字处理系统的密码设置作了较透彻的分析，指出了ＷＰＳ设置密码不单是将密码字符作了逻辑变换写入存盘文件，而且还将文本文件每一个字符的ＡＳＣＩＩ码都与相应的经过变换了的密码值作异或运算后再存盘。并给出了根除密码的方法及程序。     

用VC++编程实现转换文本文件的字符编码

介绍了使用VC++实现转换文本文件字符编码的方法,可使文本文件的字符编码在Unicode、Big5、UTF8、GBK等编码之间实现自由转换.     

书写器文件的存储格式及其自动识别

在编写应用软件时常常涉及到读写文本文件的操作,对于一般的纯文本文件可以很容易地实现,但对于用WPS及WINDOWS的书写器编辑的文件就比较棘手。读写WPS文件的方法已有不少介绍,我在这里介绍一种读写书写器文件的方法。 笔者通过对书写器文件的比较分析,基本弄清了它的存储格式,并编写了一个函数用来识别书写器文件。如果是普通的纯文本文件,则返回其文件名;如果是书写器格式的文件,则根据此文件生成一个纯文本格式的临时文件,并把这个临时文件的文件名作为函数的返回值返回。这样,程序运行     

旋转的Wang Tiles纹理合成算法

针对Wang Tiles存在的样图利用不完全、切割路径非最优、中心和拐角区域不匹配等问题,提出一种旋转的Wang Tiles纹理合成算法.使用4个正方形的子图块构造一个旋转的Wang Tile初始框架,把纹理重叠区域分为两种类型并引入基于边结构的最短路径求解方法快速确定切割路径,生成旋转的Wang Tiles集合后,采用随机的正菱形填充方法合成纹理.实验结果表明,对于多种类型的样图纹理,该算法都能够实时地获得质量较高的合成纹理.     

Improved Collision Attack on Hash Function MD5

In this paper, we present a fast attack algorithm to find two-block collision of hash function MD5. The algorithm is based on the two-block collision differential path of MD5 that was presented by Wang et al. in the Conference EUROCRYPT 2005. We found that the derived conditions for the desired collision differential path were not sufficient to guarantee the path to hold and that some conditions could be modified to enlarge the collision set. By using technique of small range searching and omitting the computing steps to check the characteristics in the attack algorithm, we can speed up the attack of MD5 efficiently. Compared with the Advanced Message Modification technique presented by Wang et al., the small range searching technique can correct 4 more conditions for the first iteration differential and 3 more conditions for the second iteration differential, thus improving the probability and the complexity to find collisions. The whole attack on the MD5 can be accomplished within 5 hours using a PC with Pentium4 1.70GHz CPU.     

RESULTS OF AN AUTOMATED ATTACK ON THE RUNNING KEY CIPHER

ABSTRACT

The running key cipher uses meaningful text as the key. Since the message also consists of meaningful text, the result is obtained by combining valid words. Automated attacks can find all such combinations that yield a given ciphertext. The results of these attacks are presented in this paper.     

MD4杂凑函数的近似碰撞

在现代密码学中,Hash函数扮演着重要的角色。而在Hash函数发展过程中,MD4算法又起着基石的作用。通过对MD4算法和王小云逐比特差分分析的介绍,利用相关差分分析的理论知识,对MD4算法产生了一对近似碰撞。找出了该碰撞的差分路径,并确定出满足其差分路径的充分条件。     

MD5报文摘要算法的各圈函数碰撞分析

本文通过分析ＭＤ５报文摘在要算法的四个非线性函数的特点，讨论了ＭＤ５的每个圈函数的许多碰撞及这些碰撞发生的概率，本文的分析结果有助于了解ＭＤ５各圈函数的 特点及ＭＤ５方案的安全性。     

Review and Implementation of Topic Modeling in Hindi

ABSTRACT

Due to the widespread usage of electronic devices and the growing popularity of social media, a lot of text data is being generated at the rate never seen before. It is not possible for humans to read all data generated and find what is being discussed in his field of interest. Topic modeling is a technique to identify the topics present in a large set of text documents. In this paper, we have discussed the widely used techniques and tools for topic modeling. There has been a lot of research on topic modeling in English, but there is not much progress in the resource-scarce languages like Hindi despite Hindi being spoken by millions of people across the world. In this paper, we have discussed the challenges faced in developing topic models for Hindi. We have applied Latent Semantic Indexing (LSI), Non-negative Matrix Factorization (NMF), and Latent Dirichlet Allocation (LDA) algorithms for topic modeling in Hindi. The outcomes of the topic model algorithms are usually difficult to interpret for the common user. We have used various visualization techniques to represent the outcomes of topic modeling in a meaningful way. Then we have used the metrics like perplexity and coherence to evaluate the topic models. The results of Topic modeling in Hindi seem to be promising and comparable to some results reported in the literature on English datasets.     

Mention detection in coreference resolution: survey

Coreference Resolution is an essential task for Natural Language Processing (NLP) application, which has a paramount impact on the performance of text summarization, machine translation, text classification, and recognizing textual entailment. Mention Detection (MD) is the core component of the coreference resolution task and is additionally a process of extraction of all possible mentions from the text. Mention is referred to as a textual representation of entities in the text, such as Name, Nominal, and Pronominal mentions. The mentions appear in the text using different representations but indicating the same entity. The performance of an MD module positively affects the performance of NLP tasks such as Coreference resolution, Relation Extraction, Information retrieval, Information extraction, etc. Incorrect identification of mentions in the text severely affects the efficiency of the coreference resolution task. This paper aims to provide a comprehensive overview for the state of the art of mention detection approaches, which is utilized in the coreference resolution task and explains the importance of MD in Coreference resolution. The subsisting approaches are classified based on the underlying techniques adopted by each approach in three categories: Rule-based mention detection, Statistics-based mention detection, and Deep learning-based mention detection. The performance of deep learning is improving as more data and more powerful computing resources become available. This study endeavors to provide a comparative analysis of various mention detection approaches and help the researchers to assimilate knowledge about the mention detection approaches from sundry aspects.

     

How to Improve Text Summarization and Classification by Mutual Cooperation on an Integrated Framework

Text summarization and classification are core techniques to analyze a huge amount of text data in the big data environment. Moreover, as the need to read texts on smart phones, tablets and television as well as personal computers continues to grow, text summarization and classification techniques become more important and both of them do essential processes for text analysis in many applications.Traditional text summarization and classification techniques have individually been considered as different research fields in this literature. However, we find out that they can help each other as text summarization makes use of category information from text classification and text classification does summary information from text summarization. Therefore, we propose an effective integrated learning framework using both of summary and category information in this paper. In this framework, the feature-weighting method for text summarization utilizes a language model to combine feature distributions in each category and text, and one for text classification does the sentence importance scores estimated from the text summarization.In the experiments, the performances of the integrated framework are better than ones of individual text summarization and classification. In addition, the framework has some advantages of easy implementation and language independence because it is based on only simple statistical approaches and POS tagger.     

关于MD5强度分析的研究

本文试图通过对MD5算法强度的分析,结合现有的碰撞分析的结论,为Hash算法的改进提出相应的思路。作为一个广为使用的Hash算法的MD5,一次循环包含了四轮64步及一次累加运算。本文通过对它由步到轮,再由轮到全局循环的分析,给出了现有碰撞分析的突破点及怎样弥补这些已被突破的缺陷。在现有研究的基础上,本文主要改进了文献[1]中给出的各步分析的结论;利用改进的结论将文献[1]中给出的概率分析非概率化;在全局碰撞的分析中给出了单轮的最大k-原像攻击的可能性;在文章的最后给出了Hash函数的四个个可能的改进方向及相应的意见。     

Visualizing Expanded Query Results

When performing queries in web search engines, users often face difficulties choosing appropriate query terms. Search engines therefore usually suggest a list of expanded versions of the user query to disambiguate it or to resolve potential term mismatches. However, it has been shown that users find it difficult to choose an expanded query from such a list. In this paper, we describe the adoption of set‐based text visualization techniques to visualize how query expansions enrich the result space of a given user query and how the result sets relate to each other. Our system uses a linguistic approach to expand queries and topic modeling to extract the most informative terms from the results of these queries. In a user study, we compare a common text list of query expansion suggestions to three set‐based text visualization techniques adopted for visualizing expanded query results – namely, Compact Euler Diagrams, Parallel Tag Clouds, and a List View – to resolve ambiguous queries using interactive query expansion. Our results show that text visualization techniques do not increase retrieval efficiency, precision, or recall. Overall, users rate Parallel Tag Clouds visualizing key terms of the expanded query space lowest. Based on the results, we derive recommendations for visualizations of query expansion results, text visualization techniques in general, and discuss alternative use cases of set‐based text visualization techniques in the context of web search.     

基于词或词组长度和频数的短中文文本关键词提取算法

中文文本的关键词提取是自然语言处理研究中的难点。国内外大部分关键词提取的研究都是基于英文文本的, 但其并不适用于中文文本的关键词提取。已有的针对中文文本的关键词提取算法大多适用于长文本,如何从一段短中文文本中准确地提取出具有实际意义且与此段中文文本的主题密切相关的词或词组是研究的重点。 提出了面向中文文本的基于词或词组长度和频数的关键词提取算法,此算法首先提取文本中出现频数较高的词或词组,再根据这些词或词组的长度以及在文本中出现的频数计算权重,从而筛选出关键词或词组。该算法可以准确地从中文文本中提取出相对重要的词或词组,从而快速、准确地提取此段中文文本的主题。实验结果表明,基于词或词组长度和频数的中文文本关键词提取算法与已有的其他算法相比,可用于处理中文文本,且具有更高的准确性。