One-Time Pad Encryption Steganography System
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Sensor-Seeded Cryptographically Secure Random Number Generation
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019 Sensor-Seeded Cryptographically Secure Random Number Generation K.Sathya, J.Premalatha, Vani Rajasekar Abstract: Random numbers are essential to generate secret keys, AES can be executed in various modes like Cipher initialization vector, one-time pads, sequence number for packets Block Chaining (CBC), Cipher Feedback (CFB), Output in network and many other applications. Though there are many Feedback (OFB), and Counter (CTR). Pseudo Random Number Generators available they are not suitable for highly secure applications that require high quality randomness. This paper proposes a cryptographically secure II. PRNG IMPLEMENTATIONS pseudorandom number generator with its entropy source from PRNG requires the seed value to be fed to a sensor housed on mobile devices. The sensor data are processed deterministic algorithm. Many deterministic algorithms are in 3-step approach to generate random sequence which in turn proposed, some of widely used algorithms are fed to Advanced Encryption Standard algorithm as random key to generate cryptographically secure random numbers. Blum-Blum-Shub algorithm uses where X0 is seed value and M =pq, p and q are prime. Keywords: Sensor-based random number, cryptographically Linear congruential Generator uses secure, AES, 3-step approach, random key where is the seed value. Mersenne Prime Twister uses range of , I. INTRODUCTION where that outputs sequence of 32-bit random Information security algorithms aim to ensure the numbers. confidentiality, integrity and availability of data that is transmitted along the path from sender to receiver. All the III. SENSOR-SEEDED PRNG security mechanisms like encryption, hashing, signature rely Some of PRNGs uses clock pulse of computer system on random numbers in generating secret keys, one time as seeds. -
A Secure Authentication System- Using Enhanced One Time Pad Technique
IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.2, February 2011 11 A Secure Authentication System- Using Enhanced One Time Pad Technique Raman Kumar1, Roma Jindal 2, Abhinav Gupta3, Sagar Bhalla4 and Harshit Arora 5 1,2,3,4,5 Department of Computer Science and Engineering, 1,2,3,4,5 D A V Institute of Engineering and Technology, Jalandhar, Punjab, India. Summary the various weaknesses associated with a password have With the upcoming technologies available for hacking, there is a come to surface. It is always possible for people other than need to provide users with a secure environment that protect their the authenticated user to posses its knowledge at the same resources against unauthorized access by enforcing control time. Password thefts can and do happen on a regular basis, mechanisms. To counteract the increasing threat, enhanced one so there is a need to protect them. Rather than using some time pad technique has been introduced. It generally random set of alphabets and special characters as the encapsulates the enhanced one time pad based protocol and provides client a completely unique and secured authentication passwords we need something new and something tool to work on. This paper however proposes a hypothesis unconventional to ensure safety. At the same time we need regarding the use of enhanced one time pad based protocol and is to make sure that it is easy to be remembered by you as a comprehensive study on the subject of using enhanced one time well as difficult enough to be hacked by someone else. -
A Note on Random Number Generation
A note on random number generation Christophe Dutang and Diethelm Wuertz September 2009 1 1 INTRODUCTION 2 \Nothing in Nature is random. number generation. By \random numbers", we a thing appears random only through mean random variates of the uniform U(0; 1) the incompleteness of our knowledge." distribution. More complex distributions can Spinoza, Ethics I1. be generated with uniform variates and rejection or inversion methods. Pseudo random number generation aims to seem random whereas quasi random number generation aims to be determin- istic but well equidistributed. 1 Introduction Those familiars with algorithms such as linear congruential generation, Mersenne-Twister type algorithms, and low discrepancy sequences should Random simulation has long been a very popular go directly to the next section. and well studied field of mathematics. There exists a wide range of applications in biology, finance, insurance, physics and many others. So 2.1 Pseudo random generation simulations of random numbers are crucial. In this note, we describe the most random number algorithms At the beginning of the nineties, there was no state-of-the-art algorithms to generate pseudo Let us recall the only things, that are truly ran- random numbers. And the article of Park & dom, are the measurement of physical phenomena Miller (1988) entitled Random generators: good such as thermal noises of semiconductor chips or ones are hard to find is a clear proof. radioactive sources2. Despite this fact, most users thought the rand The only way to simulate some randomness function they used was good, because of a short on computers are carried out by deterministic period and a term to term dependence. -
9 Purple 18/2
THE CONCORD REVIEW 223 A VERY PURPLE-XING CODE Michael Cohen Groups cannot work together without communication between them. In wartime, it is critical that correspondence between the groups, or nations in the case of World War II, be concealed from the eyes of the enemy. This necessity leads nations to develop codes to hide their messages’ meanings from unwanted recipients. Among the many codes used in World War II, none has achieved a higher level of fame than Japan’s Purple code, or rather the code that Japan’s Purple machine produced. The breaking of this code helped the Allied forces to defeat their enemies in World War II in the Pacific by providing them with critical information. The code was more intricate than any other coding system invented before modern computers. Using codebreaking strategy from previous war codes, the U.S. was able to crack the Purple code. Unfortunately, the U.S. could not use its newfound knowl- edge to prevent the attack at Pearl Harbor. It took a Herculean feat of American intellect to break Purple. It was dramatically intro- duced to Congress in the Congressional hearing into the Pearl Harbor disaster.1 In the ensuing years, it was discovered that the deciphering of the Purple Code affected the course of the Pacific war in more ways than one. For instance, it turned out that before the Americans had dropped nuclear bombs on Japan, Purple Michael Cohen is a Senior at the Commonwealth School in Boston, Massachusetts, where he wrote this paper for Tom Harsanyi’s United States History course in the 2006/2007 academic year. -
Cryptanalysis of the Random Number Generator of the Windows Operating System
Cryptanalysis of the Random Number Generator of the Windows Operating System Leo Dorrendorf School of Engineering and Computer Science The Hebrew University of Jerusalem 91904 Jerusalem, Israel [email protected] Zvi Gutterman Benny Pinkas¤ School of Engineering and Computer Science Department of Computer Science The Hebrew University of Jerusalem University of Haifa 91904 Jerusalem, Israel 31905 Haifa, Israel [email protected] [email protected] November 4, 2007 Abstract The pseudo-random number generator (PRNG) used by the Windows operating system is the most commonly used PRNG. The pseudo-randomness of the output of this generator is crucial for the security of almost any application running in Windows. Nevertheless, its exact algorithm was never published. We examined the binary code of a distribution of Windows 2000, which is still the second most popular operating system after Windows XP. (This investigation was done without any help from Microsoft.) We reconstructed, for the ¯rst time, the algorithm used by the pseudo- random number generator (namely, the function CryptGenRandom). We analyzed the security of the algorithm and found a non-trivial attack: given the internal state of the generator, the previous state can be computed in O(223) work (this is an attack on the forward-security of the generator, an O(1) attack on backward security is trivial). The attack on forward-security demonstrates that the design of the generator is flawed, since it is well known how to prevent such attacks. We also analyzed the way in which the generator is run by the operating system, and found that it ampli¯es the e®ect of the attacks: The generator is run in user mode rather than in kernel mode, and therefore it is easy to access its state even without administrator privileges. -
Secure Communications One Time Pad Cipher
Cipher Machines & Cryptology © 2010 – D. Rijmenants http://users.telenet.be/d.rijmenants THE COMPLETE GUIDE TO SECURE COMMUNICATIONS WITH THE ONE TIME PAD CIPHER DIRK RIJMENANTS Abstract : This paper provides standard instructions on how to protect short text messages with one-time pad encryption. The encryption is performed with nothing more than a pencil and paper, but provides absolute message security. If properly applied, it is mathematically impossible for any eavesdropper to decrypt or break the message without the proper key. Keywords : cryptography, one-time pad, encryption, message security, conversion table, steganography, codebook, message verification code, covert communications, Jargon code, Morse cut numbers. version 012-2011 1 Contents Section Page I. Introduction 2 II. The One-time Pad 3 III. Message Preparation 4 IV. Encryption 5 V. Decryption 6 VI. The Optional Codebook 7 VII. Security Rules and Advice 8 VIII. Appendices 17 I. Introduction One-time pad encryption is a basic yet solid method to protect short text messages. This paper explains how to use one-time pads, how to set up secure one-time pad communications and how to deal with its various security issues. It is easy to learn to work with one-time pads, the system is transparent, and you do not need special equipment or any knowledge about cryptographic techniques or math. If properly used, the system provides truly unbreakable encryption and it will be impossible for any eavesdropper to decrypt or break one-time pad encrypted message by any type of cryptanalytic attack without the proper key, even with infinite computational power (see section VII.b) However, to ensure the security of the message, it is of paramount importance to carefully read and strictly follow the security rules and advice (see section VII). -
Random Number Generation Using MSP430™ Mcus (Rev. A)
Application Report SLAA338A–October 2006–Revised May 2018 Random Number Generation Using MSP430™ MCUs ......................................................................................................................... MSP430Applications ABSTRACT Many applications require the generation of random numbers. These random numbers are useful for applications such as communication protocols, cryptography, and device individualization. Generating random numbers often requires the use of expensive dedicated hardware. Using the two independent clocks available on the MSP430F2xx family of devices, software can generate random numbers without such hardware. Source files for the software described in this application report can be downloaded from http://www.ti.com/lit/zip/slaa338. Contents 1 Introduction ................................................................................................................... 1 2 Setup .......................................................................................................................... 1 3 Adding Randomness ........................................................................................................ 2 4 Usage ......................................................................................................................... 2 5 Overview ...................................................................................................................... 2 6 Testing for Randomness................................................................................................... -
The Enigma History and Mathematics
The Enigma History and Mathematics by Stephanie Faint A thesis presented to the University of Waterloo in fulfilment of the thesis requirement for the degree of Master of Mathematics m Pure Mathematics Waterloo, Ontario, Canada, 1999 @Stephanie Faint 1999 I hereby declare that I am the sole author of this thesis. I authorize the University of Waterloo to lend this thesis to other institutions or individuals for the purpose of scholarly research. I further authorize the University of Waterloo to reproduce this thesis by pho tocopying or by other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research. 11 The University of Waterloo requires the signatures of all persons using or pho tocopying this thesis. Please sign below, and give address and date. ill Abstract In this thesis we look at 'the solution to the German code machine, the Enigma machine. This solution was originally found by Polish cryptologists. We look at the solution from a historical perspective, but most importantly, from a mathematical point of view. Although there are no complete records of the Polish solution, we try to reconstruct what was done, sometimes filling in blanks, and sometimes finding a more mathematical way than was originally found. We also look at whether the solution would have been possible without the help of information obtained from a German spy. IV Acknowledgements I would like to thank all of the people who helped me write this thesis, and who encouraged me to keep going with it. In particular, I would like to thank my friends and fellow grad students for their support, especially Nico Spronk and Philippe Larocque for their help with latex. -
(Pseudo) Random Number Generation for Cryptography
Thèse de Doctorat présentée à L’ÉCOLE POLYTECHNIQUE pour obtenir le titre de DOCTEUR EN SCIENCES Spécialité Informatique soutenue le 18 mai 2009 par Andrea Röck Quantifying Studies of (Pseudo) Random Number Generation for Cryptography. Jury Rapporteurs Thierry Berger Université de Limoges, France Andrew Klapper University of Kentucky, USA Directeur de Thèse Nicolas Sendrier INRIA Paris-Rocquencourt, équipe-projet SECRET, France Examinateurs Philippe Flajolet INRIA Paris-Rocquencourt, équipe-projet ALGORITHMS, France Peter Hellekalek Universität Salzburg, Austria Philippe Jacquet École Polytechnique, France Kaisa Nyberg Helsinki University of Technology, Finland Acknowledgement It would not have been possible to finish this PhD thesis without the help of many people. First, I would like to thank Prof. Peter Hellekalek for introducing me to the very rich topic of cryptography. Next, I express my gratitude towards Nicolas Sendrier for being my supervisor during my PhD at Inria Paris-Rocquencourt. He gave me an interesting topic to start, encouraged me to extend my research to the subject of stream ciphers and supported me in all my decisions. A special thanks also to Cédric Lauradoux who pushed me when it was necessary. I’m very grateful to Anne Canteaut, which had always an open ear for my questions about stream ciphers or any other topic. The valuable discussions with Philippe Flajolet about the analysis of random functions are greatly appreciated. Especially, I want to express my gratitude towards my two reviewers, Thierry Berger and Andrew Klapper, which gave me precious comments and remarks. I’m grateful to Kaisa Nyberg for joining my jury and for receiving me next year in Helsinki. -
Vigen`Ere Cipher – Friedman's Attack
Statistics 116 - Fall 2002 Breaking the Code Lecture # 5 – Vigen`ere Cipher – Friedman’s Attack Jonathan Taylor 1 Index of Coincidence In this lecture, we will discuss a more “automated” approach to guessing the keyword length in the Vigen`erecipher. The procedure is based on what we will call the index of coincidence of two strings of text S1 and S2 of a given length n. The index of coincidence is just the proportion of positions in the strings where the corresponding characters of the two strings match, and is defined for the strings S1 = (s11, s12, . , s1n) and S2 = (s21, s22, . , s2n) n 1 X I(S ,S ) = δ(s , s ) 1 2 n 1i 2i i=1 where the function ( 1 if x = y δ(x, y) = 0 otherwise. Now, in our situation we have to imagine that the string we observe is “random”. Suppose then, that S1 and S2 are two random strings generated by choosing letters at random with the frequencies (p0, . , p25) and (q0, . , q25), respectively. If the strings are random we can talk about the “average” index of coincidence of the two strings S1 and S2. In statistics, if we have a random quantity X we write E(X) for the “expectation” or “average” of X. It is not difficult to show that if S1 is a string of random letters with frequencies (p0, . , p25) and (q0, . , q25) then the average index of coincidence of these two strings is given by 25 X piqi. (1) i=0 1 This is because, for any of the letters in the string, the probability that the two characters match is the probability that they are both A’s, both B’s, . -
Random-Number Functions
Title stata.com Random-number functions Contents Functions Remarks and examples Methods and formulas Acknowledgments References Also see Contents rbeta(a,b) beta(a,b) random variates, where a and b are the beta distribution shape parameters rbinomial(n,p) binomial(n,p) random variates, where n is the number of trials and p is the success probability rcauchy(a,b) Cauchy(a,b) random variates, where a is the location parameter and b is the scale parameter rchi2(df) χ2, with df degrees of freedom, random variates rexponential(b) exponential random variates with scale b rgamma(a,b) gamma(a,b) random variates, where a is the gamma shape parameter and b is the scale parameter rhypergeometric(N,K,n) hypergeometric random variates rigaussian(m,a) inverse Gaussian random variates with mean m and shape param- eter a rlaplace(m,b) Laplace(m,b) random variates with mean m and scale parameter b p rlogistic() logistic variates with mean 0 and standard deviation π= 3 p rlogistic(s) logistic variates with mean 0, scale s, and standard deviation sπ= 3 rlogistic(m,s) logisticp variates with mean m, scale s, and standard deviation sπ= 3 rnbinomial(n,p) negative binomial random variates rnormal() standard normal (Gaussian) random variates, that is, variates from a normal distribution with a mean of 0 and a standard deviation of 1 rnormal(m) normal(m,1) (Gaussian) random variates, where m is the mean and the standard deviation is 1 rnormal(m,s) normal(m,s) (Gaussian) random variates, where m is the mean and s is the standard deviation rpoisson(m) Poisson(m) -
A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications
Special Publication 800-22 Revision 1a A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications AndrewRukhin,JuanSoto,JamesNechvatal,Miles Smid,ElaineBarker,Stefan Leigh,MarkLevenson,Mark Vangel,DavidBanks,AlanHeckert,JamesDray,SanVo Revised:April2010 LawrenceE BasshamIII A Statistical Test Suite for Random and Pseudorandom Number Generators for NIST Special Publication 800-22 Revision 1a Cryptographic Applications 1 2 Andrew Rukhin , Juan Soto , James 2 2 Nechvatal , Miles Smid , Elaine 2 1 Barker , Stefan Leigh , Mark 1 1 Levenson , Mark Vangel , David 1 1 2 Banks , Alan Heckert , James Dray , 2 San Vo Revised: April 2010 2 Lawrence E Bassham III C O M P U T E R S E C U R I T Y 1 Statistical Engineering Division 2 Computer Security Division Information Technology Laboratory National Institute of Standards and Technology Gaithersburg, MD 20899-8930 Revised: April 2010 U.S. Department of Commerce Gary Locke, Secretary National Institute of Standards and Technology Patrick Gallagher, Director A STATISTICAL TEST SUITE FOR RANDOM AND PSEUDORANDOM NUMBER GENERATORS FOR CRYPTOGRAPHIC APPLICATIONS Reports on Computer Systems Technology The Information Technology Laboratory (ITL) at the National Institute of Standards and Technology (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the nation’s measurement and standards infrastructure. ITL develops tests, test methods, reference data, proof of concept implementations, and technical analysis to advance the development and productive use of information technology. ITL’s responsibilities include the development of technical, physical, administrative, and management standards and guidelines for the cost-effective security and privacy of sensitive unclassified information in Federal computer systems.