A Secure Authentication System- Using Enhanced One Time Pad Technique
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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 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. -
Cryptography in Modern World
Cryptography in Modern World Julius O. Olwenyi, Aby Tino Thomas, Ayad Barsoum* St. Mary’s University, San Antonio, TX (USA) Emails: [email protected], [email protected], [email protected] Abstract — Cryptography and Encryption have been where a letter in plaintext is simply shifted 3 places down used for secure communication. In the modern world, the alphabet [4,5]. cryptography is a very important tool for protecting information in computer systems. With the invention ABCDEFGHIJKLMNOPQRSTUVWXYZ of the World Wide Web or Internet, computer systems are highly interconnected and accessible from DEFGHIJKLMNOPQRSTUVWXYZABC any part of the world. As more systems get interconnected, more threat actors try to gain access The ciphertext of the plaintext “CRYPTOGRAPHY” will to critical information stored on the network. It is the be “FUBSWRJUASLB” in a Caesar cipher. responsibility of data owners or organizations to keep More recent derivative of Caesar cipher is Rot13 this data securely and encryption is the main tool used which shifts 13 places down the alphabet instead of 3. to secure information. In this paper, we will focus on Rot13 was not all about data protection but it was used on different techniques and its modern application of online forums where members could share inappropriate cryptography. language or nasty jokes without necessarily being Keywords: Cryptography, Encryption, Decryption, Data offensive as it will take those interested in those “jokes’ security, Hybrid Encryption to shift characters 13 spaces to read the message and if not interested you do not need to go through the hassle of converting the cipher. I. INTRODUCTION In the 16th century, the French cryptographer Back in the days, cryptography was not all about Blaise de Vigenere [4,5], developed the first hiding messages or secret communication, but in ancient polyalphabetic substitution basically based on Caesar Egypt, where it began; it was carved into the walls of cipher, but more difficult to crack the cipher text. -
A Practical Implementation of a One-Time Pad Cryptosystem
Jeff Connelly CPE 456 June 11, 2008 A Practical Implementation of a One-time Pad Cryptosystem 0.1 Abstract How to securely transmit messages between two people has been a problem for centuries. The first ciphers of antiquity used laughably short keys and insecure algorithms easily broken with today’s computational power. This pattern has repeated throughout history, until the invention of the one-time pad in 1917, the world’s first provably unbreakable cryptosystem. However, the public generally does not use the one-time pad for encrypting their communication, despite the assurance of confidentiality, because of practical reasons. This paper presents an implementation of a practical one-time pad cryptosystem for use between two trusted individuals, that have met previously but wish to securely communicate over email after their departure. The system includes the generation of a one-time pad using a custom-built hardware TRNG as well as software to easily send and receive encrypted messages over email. This implementation combines guaranteed confidentiality with practicality. All of the work discussed here is available at http://imotp.sourceforge.net/. 1 Contents 0.1 Abstract.......................................... 1 1 Introduction 3 2 Implementation 3 2.1 RelatedWork....................................... 3 2.2 Description ........................................ 3 3 Generating Randomness 4 3.1 Inadequacy of Pseudo-random Number Generation . 4 3.2 TrulyRandomData .................................... 5 4 Software 6 4.1 Acquiring Audio . 6 4.1.1 Interference..................................... 6 4.2 MeasuringEntropy................................... 6 4.3 EntropyExtraction................................ ..... 7 4.3.1 De-skewing ..................................... 7 4.3.2 Mixing........................................ 7 5 Exchanging Pads 8 5.1 Merkle Channels . 8 5.2 Local Pad Security . -
Historical Ciphers • A
ECE 646 - Lecture 6 Required Reading • W. Stallings, Cryptography and Network Security, Chapter 2, Classical Encryption Techniques Historical Ciphers • A. Menezes et al., Handbook of Applied Cryptography, Chapter 7.3 Classical ciphers and historical development Why (not) to study historical ciphers? Secret Writing AGAINST FOR Steganography Cryptography (hidden messages) (encrypted messages) Not similar to Basic components became modern ciphers a part of modern ciphers Under special circumstances modern ciphers can be Substitution Transposition Long abandoned Ciphers reduced to historical ciphers Transformations (change the order Influence on world events of letters) Codes Substitution The only ciphers you Ciphers can break! (replace words) (replace letters) Selected world events affected by cryptology Mary, Queen of Scots 1586 - trial of Mary Queen of Scots - substitution cipher • Scottish Queen, a cousin of Elisabeth I of England • Forced to flee Scotland by uprising against 1917 - Zimmermann telegram, America enters World War I her and her husband • Treated as a candidate to the throne of England by many British Catholics unhappy about 1939-1945 Battle of England, Battle of Atlantic, D-day - a reign of Elisabeth I, a Protestant ENIGMA machine cipher • Imprisoned by Elisabeth for 19 years • Involved in several plots to assassinate Elisabeth 1944 – world’s first computer, Colossus - • Put on trial for treason by a court of about German Lorenz machine cipher 40 noblemen, including Catholics, after being implicated in the Babington Plot by her own 1950s – operation Venona – breaking ciphers of soviet spies letters sent from prison to her co-conspirators stealing secrets of the U.S. atomic bomb in the encrypted form – one-time pad 1 Mary, Queen of Scots – cont. -
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. -
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................................................................................................... -
(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. -
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. -
Secure Communications One Time Pad Cipher
Cipher Machines & Cryptology Ed. 7.4 – Jan 22, 2016 © 2009 - 2016 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, covert communications, Morse cut numbers. 1 Contents 1. Introduction………………………………. 2 2. The One-time Pad………………………. 3 3. Message Preparation…………………… 4 4. Encryption………………………………... 5 5. Decryption………………………………... 6 6. The Optional Codebook………………… 7 7. Security Rules and Advice……………… 8 8. Is One-time Pad Really Unbreakable…. 16 9. Legal Issues and Personal Security…... 18 10. Appendices………………………………. 19 1. 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. Working with one-time pads is easy to learn. The system is transparent and you do not need a computer, special equipment or any knowledge about cryptographic techniques or mathematics. One-time pad encryption is an equation with two unknowns, which is mathematically unsolvable. The system therefore provides truly unbreakable encryption when properly used. It will never be possible to decipher one-time pad encrypted data without having the proper key, regardless any existing or future cryptanalytic attack or technology, infinite computational power or infinite time. -
Random.Org: Introduction to Randomness and Random Numbers
random.org: Introduction to Randomness and Random Numbers Mads Haahr June 1999* Background Oh, many a shaft at random sent Finds mark the archer little meant! And many a word at random spoken May soothe, or wound, a heart that's broken!" | Sir Walter Scott Randomness and random numbers have traditionally been used for a variety of purposes, for ex- ample games such as dice games. With the advent of computers, people recognized the need for a means of introducing randomness into a computer program. Surprising as it may seem, however, it is difficult to get a computer to do something by chance. A computer running a program follows its instructions blindly and is therefore completely predictable. Computer engineers chose to introduce randomness into computers in the form of pseudo- random number generators. As the name suggests, pseudo-random numbers are not truly random. Rather, they are computed from a mathematical formula or simply taken from a precalculated list. A lot of research has gone into pseudo-random number theory and modern algorithms for generating them are so good that the numbers look exactly like they were really random. Pseudo- random numbers have the characteristic that they are predictable, meaning they can be predicted if you know where in the sequence the first number is taken from. For some purposes, predictability is a good characteristic, for others it is not. Random numbers are used for computer games but they are also used on a more serious scale for the generation of cryptographic keys and for some classes of scientific experiments.