Search results “Cryptographically secure random generator”
Applied Cryptography: Random Numbers (1/2)
Previous video: https://youtu.be/6ro3z2pTiqI Next video: https://youtu.be/KuthrX4G1ss
Views: 4067 Leandro Junes
Pseudorandom number generators | Computer Science | Khan Academy
Random vs. Pseudorandom Number Generators Watch the next lesson: https://www.khanacademy.org/computing/computer-science/cryptography/modern-crypt/v/the-fundamental-theorem-of-arithmetic-1?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/cryptography/crypt/v/perfect-secrecy?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information). About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 157406 Khan Academy Labs
Pseudo Random Number Generator - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 8309 Udacity
Cryptographically secure pseudorandom number generator Top # 7 Facts
Cryptographically secure pseudorandom number generator Top # 7 Facts
Views: 82 Duryodhan Trivedi
Cryptographically secure pseudorandom number generator
Cryptographically secure pseudorandom number generator A cryptographically secure pseudo-random number generator (CSPRNG) or cryptographic pseudo-random number generator (CPRNG) is a pseudo-random number generator (PRNG) with properties that make it suitable for use in cryptography.Many aspects of cryptography require random numbers, for example: key generation. -Video is targeted to blind users Attribution: Article text available under CC-BY-SA image source in video https://www.youtube.com/watch?v=NL-EL2KcU-Q
Views: 797 WikiAudio
Applied Cryptography: Random Numbers (2/2)
Previous video: https://youtu.be/g3iH74XFaT0 Next video:
Views: 1350 Leandro Junes
PRNG Implementation Solution - Applied Cryptography
Views: 1355 Udacity
Applied Cryptography: Random Numbers in Java (1/5)
Previous video: https://youtu.be/KuthrX4G1ss Next video: https://youtu.be/FhrsUCICh-Y
Views: 984 Leandro Junes
Lesson 8  - Java Secure Random Number Generator and Statistics
In statistics, if we roll a six-sided die X amount of times, the number of times each face will appear becomes more equal as X approaches infinity. In this lesson, we use the Secure Random Number Class to generator a random number between one and three and prove the statistic to be true using a for loop.
Views: 93 TDG Industries
Applied Cryptography: Random Numbers in Java (5/5)
Previous video: https://youtu.be/KnHp1uSm6k0 Next video: https://youtu.be/8VlG5lq4xLs
Views: 379 Leandro Junes
Applied Cryptography: Random Numbers in Java (2/5)
Previous video: https://youtu.be/_IcG4N7PQfA Next video: https://youtu.be/uTlZHRa-ZkM
Views: 576 Leandro Junes
How secure is 256 bit security?
Supplement to the cryptocurrency video: How hard is it to find a 256-bit hash just by guessing and checking? What kind of computer would that take? Cryptocurrency video: https://youtu.be/bBC-nXj3Ng4 Thread for Q&A questions: http://3b1b.co/questions Several people have commented about how 2^256 would be the maximum number of attempts, not the average. This depends on the thing being attempted. If it's guessing a private key, you are correct, but for something like guessing which input to a hash function gives a desired output (as in bitcoin mining, for example), which is the kind of thing I had in mind here, 2^256 would indeed be the average number of attempts needed, at least for a true cryptographic hash function. Think of rolling a die until you get a 6, how many rolls do you need to make, on average? Music by Vince Rubinetti: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Views: 947576 3Blue1Brown
A Quantum Random Number Generator for cryptographic applications
This project presents a quantum random number generator for a multitude of cryptographic applications based on the alpha decay of a household radioactive source.
Views: 640 BTYoungScientists
Bsides LV 2014 - Untwisting The Mersenne Twister: How I killed the PRNG - 05Aug2014
05 Aug 2014 - Bsides Las Vegas 2014 Joe "moloch" - Bishop Fox Dan "AltF4" Petro - Bishop Fox http://www.bishopfox.com http://www.bishopfox.com/blog/2014/08/untwisting-mersenne-twister-killed-prng/ http://www.irongeek.com/i.php?page=videos/bsideslasvegas2014/bg04-untwisting-the-mersenne-twister-how-i-killed-the-prng-moloch Untwisting The Mersenne Twister: How I killed the PRNG Applications rely on generating random numbers to provide security, and fail catastrophically when these numbers turn out to be not so “random.” For penetration testers, however, the ability to exploit these systems has always been just out of reach. To solve this problem, we’ve created “untwister:” an attack tool for breaking insecure random number generators and recovering the initial seed. We did all the hard math, so you don't have to! Random numbers are often used in security contexts for generating unique IDs, new passwords for resets, or cryptographic nonces. However, the built-in random number generators for most languages and frameworks are insecure, leaving applications open to a series of previously theoretical attacks. Lots of papers have been written on PRNG security, but there's still almost nothing practical you can use as a pentester to actually break live systems in the wild. This talk focuses on weaponizing what used to be theoretical into our tool: untwister. Let's finally put rand() to rest. DISCLAIMER: This video is intended for pentesting training purposes only.
Views: 4008 Bishop Fox
COSIC Seminar - Entropy Sources For Cryptographic Random Number Generation (John Kelsey)
Random number generation underlies all of cryptography—if you can’t generate good random numbers, you probably can’t do any useful crypto. In this tutorial, I will go over how cryptographic random number generation works, and then zoom in on entropy sources—the ultimate source of unpredictability in any cryptographic RNG. I’ll discuss the problems of designing and analyzing an entropy source, and the approach we’ve used in SP 800-90B for specifying how they should work and how labs should try to validate them. I’ll also talk about the related problem of extractors, the functions that process entropy-bearing inputs and yield some kind of seed for a deterministic RNG.
Cryptographically secure pseudorandom number generator
This video is part of an online course, Applied Cryptography. Check out the course here: Random vs. Pseudorandom Number Generators Watch the next lesson: Cryptographically secure pseudorandom number generator A cryptographically secure pseudo-random number generator (CSPRNG) or cryptographic pseudo-random number generator (CPRNG) is a pseudo-rando.
Views: 17 Shira Hohn
Applied Cryptography: Random Numbers in Java (3/5)
Previous video: https://youtu.be/FhrsUCICh-Y Next video: https://youtu.be/KnHp1uSm6k0
Views: 475 Leandro Junes
DEF CON 22 - Dan Kaminsky - Secure Random by Default
Secure Random By Default Dan Kaminsky Chief Scientist, White Ops As a general rule in security, we have learned that the best way to achieve security is to enable it by default. However, across operating systems and languages, random number generation is always exposed via two separate and most assuredly unequal APIs -- insecure and default, and secure but obscure. Why not fix this? Why not make JavaScript and PHP and Java and Python and even libc rand() return strong entropy? What are the issues stopping us? Should we just shell back to /dev/urandom, or is there merit to userspace entropy gathering? How does fork() and virtualization impact the question? What of performance, and memory consumption, and headless machines? Turns out the above questions are not actually rhetorical. Just because a change might be a good idea doesn't mean it's a simple one. This will be a deep dive, but one that I believe will actually yield a fix for the repeated *real world* failures of random number generation systems. Dan Kaminsky has been a noted security researcher for over a decade, and has spent his career advising Fortune 500 companies such as Cisco, Avaya, and Microsoft.Dan spent three years working with Microsoft on their Vista, Server 2008, and Windows 7 releases. Dan is best known for his work finding a critical flaw in the Internet’s Domain Name System (DNS), and for leading what became the largest synchronized fix to the Internet’s infrastructure of all time. Of the seven Recovery Key Shareholders who possess the ability to restore the DNS root keys, Dan is the American representative. Dan is presently developing systems to reduce the cost and complexity of securing critical infrastructure.
Views: 42245 DEFCONConference
Pseudorandom Generators I
Raghu Meka, UCLA https://simons.berkeley.edu/talks/pseudorandom-generators-1 Pseudorandomness Boot Camp
Views: 935 Simons Institute
What is PSEUDORANDOM NUMBER GENERATOR? What does PSEUDORANDOM NUMBER GENERATOR mean? PSEUDORANDOM NUMBER GENERATOR meaning - PSEUDORANDOM NUMBER GENERATOR definition - PSEUDORANDOM NUMBER GENERATOR explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by a relatively small set of initial values, called the PRNG's seed (which may include truly random values). Although sequences that are closer to truly random can be generated using hardware random number generators, pseudorandom number generators are important in practice for their speed in number generation and their reproducibility. PRNGs are central in applications such as simulations (e.g. for the Monte Carlo method), electronic games (e.g. for procedural generation), and cryptography. Cryptographic applications require the output not to be predictable from earlier outputs, and more elaborate algorithms, which do not inherit the linearity of simpler PRNGs, are needed. Good statistical properties are a central requirement for the output of a PRNG. In general, careful mathematical analysis is required to have any confidence that a PRNG generates numbers that are sufficiently close to random to suit the intended use. John von Neumann cautioned about the misinterpretation of a PRNG as a truly random generator, and joked that "Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin." A PRNG can be started from an arbitrary initial state using a seed state. It will always produce the same sequence when initialized with that state. The period of a PRNG is defined thus: the maximum, over all starting states, of the length of the repetition-free prefix of the sequence. The period is bounded by the number of the states, usually measured in bits. However, since the length of the period potentially doubles with each bit of "state" added, it is easy to build PRNGs with periods long enough for many practical applications. If a PRNG's internal state contains n bits, its period can be no longer than 2n results, and may be much shorter. For some PRNGs, the period length can be calculated without walking through the whole period. Linear Feedback Shift Registers (LFSRs) are usually chosen to have periods of exactly 2n-1. Linear congruential generators have periods that can be calculated by factoring. Although PRNGs will repeat their results after they reach the end of their period, a repeated result does not imply that the end of the period has been reached, since its internal state may be larger than its output; this is particularly obvious with PRNGs with a one-bit output. Most PRNG algorithms produce sequences which are uniformly distributed by any of several tests. It is an open question, and one central to the theory and practice of cryptography, whether there is any way to distinguish the output of a high-quality PRNG from a truly random sequence, knowing the algorithms used, but not the state with which it was initialized. The security of most cryptographic algorithms and protocols using PRNGs is based on the assumption that it is infeasible to distinguish use of a suitable PRNG from use of a truly random sequence. The simplest examples of this dependency are stream ciphers, which (most often) work by exclusive or-ing the plaintext of a message with the output of a PRNG, producing ciphertext. The design of cryptographically adequate PRNGs is extremely difficult, because they must meet additional criteria (see below). The size of its period is an important factor in the cryptographic suitability of a PRNG, but not the only one. A PRNG suitable for cryptographic applications is called a cryptographically secure PRNG (CSPRNG). A requirement for a CSPRNG is that an adversary not knowing the seed has only negligible advantage in distinguishing the generator's output sequence from a random sequence. In other words, while a PRNG is only required to pass certain statistical tests, a CSPRNG must pass all statistical tests that are restricted to polynomial time in the size of the seed. Though a proof of this property is beyond the current state of the art of computational complexity theory, strong evidence may be provided by reducing the CSPRNG to a problem that is assumed to be hard, such as integer factorization. In general, years of review may be required before an algorithm can be certified as a CSPRNG.
Views: 2960 The Audiopedia
Quantum Optics – Quantum random numbers generator QRNG
Views: 261 intrigano
DEFCON 17: Design and Implementation of a Quantum True Random Number Generator
Speaker: Sean Boyce Security Researcher The problem of generating "reasonable" approximations to random numbers has been solved quite some time ago... but this talk is not for reasonable people. Generating true random numbers with a deterministic system is impossible; and so we must drink deeply from the raw, godless chaos of quantum physics. This talk will cover the various pitfalls of quantum true random number generator construction, including bias, statistical relatedness between bits, and unpleasant supply voltages. A working reference design that overcomes these hurdles will be described, and barring major disaster, demonstrated. Notably, this design contains a custom, fully solid-state particle detector that may be constructed for around USD 20$. To benefit the most from this lecture, a very basic knowledge of statistics, particle physics, and/or analog electronics is ideal; however enough background will be provided that this will not be strictly necessary. If in doubt, the Wikipedia articles on quantum tunneling, alpha particle, normal distribution, operational amplifier, and hardware random number generator should provide more than sufficient background. Demo For more information visit: http://bit.ly/defcon17_information To download the video visit: http://bit.ly/defcon17_videos
Views: 5306 Christiaan008
cryptography - Pseudorandom Generators
Cryptography To get certificate subscribe: https://www.coursera.org/learn/cryptography ======================== Playlist URL: https://www.youtube.com/playlist?list=PL2jykFOD1AWb07OLBdFI2QIHvPo3aTTeu ============================ Youtube channel: https://www.youtube.com/user/intrigano ============================ https://scsa.ge/en/online-courses/ https://www.facebook.com/cyberassociation/
Views: 1935 intrigano
[wr0ng 2017] Random Number Generator Done Wrong - Nadia Heninger
Randomness is essential to cryptography: cryptographic security depends on private keys that are unpredictable to an attacker. But how good are the random number generators that are actually used in practice? In this talk, I will discuss several large-scale surveys of cryptographic deployments, including TLS, SSH, Bitcoin, and smart cards, and show that random number generation flaws are surprisingly widespread. We will see how many of the most commonly used public key encryption and signature schemes, including RSA, DSA, and ECDSA are brittle if used with faulty random number generators and can fail catastrophically to an external attacker. We trace many of the the random number generation flaws we encountered to specific implementations and vulnerable implementation patterns. I will also discuss followup work showing that, distressingly, many hosts with random number generation flaws remain unpatched years after public disclosure. This talk surveys several joint projects with a very large number of collaborators.
Views: 357 ECRYPT
April 2014 NYCBUG Meeting: Secure Random Number Generators, by Yevgeniy Dodis
This is a recording of the April 1st NYCBUG Meeting on Random Number Generators. We discussed how to design (and not design) secure Random Number Generators. In particular, we will show attacks on Linux /dev/random, present first theoretical analysis on the Windows 8 RNG Fortuna, and talk about the importance of provable security. We will follow these papers: http://eprint.iacr.org/2013/338 http://eprint.iacr.org/2014/167 Recent and relevant blog posts: https://www.schneier.com/blog/archives/2014/03/the_security_of_7.html https://www.schneier.com/blog/archives/2013/10/insecurities_in.html http://it.slashdot.org/story/13/10/14/2318211/linux-rng-may-be-insecure-after-all Speaker Bio Yevgeniy Dodis is a Professor of computer science at New York University. Dr. Dodis received his summa cum laude Bachelors degree in Mathematics and Computer Science from New York University in 1996, and his PhD degree in Computer Science from MIT in 2000. Dr. Dodis was a post-doc at IBM T.J.Watson Research center in 2000, and joined New York University as an Assistant Professor in 2001. He was promoted to Associate Professor in 2007 and Full Professor in 2012. Dr. Dodis' research is primarily in cryptography and network security. In particular, he worked in a variety of areas including leakage-resilient cryptography, cryptography under weak randomness, cryptography with biometrics and other noisy data, hash function and block cipher design, protocol composition and information-theoretic cryptography. Dr. Dodis has more than 100 scientific publications at various conferences, journals and other venues, was the Program co-Chair for the 2015 Theory of Cryptography Conference, has been on program committees of many international conferences (including FOCS, STOC, CRYPTO and Eurocrypt), and gave numerous invited lectures and courses at various venues. Dr. Dodis is the recipient of National Science Foundation CAREER Award, Faculty Awards from IBM, Google and VMware, and Best Paper Award at 2005 Public Key Cryptography Conference. As an undergraduate student, he was also a winner of the US-Canada Putnam Mathematical Competition in 1995.
Views: 1031 BSDTV
How to Generate Pseudorandom Numbers | Infinite Series
Viewers like you help make PBS (Thank you 😃) . Support your local PBS Member Station here: https://to.pbs.org/donateinfi What is a the difference between a random and a pseudorandom number? And what can pseudo random numbers allow us to do that random numbers can't? Tweet at us! @pbsinfinite Facebook: facebook.com/pbsinfinite series Email us! pbsinfiniteseries [at] gmail [dot] com Previous Episode How many Cops to catch a Robber? | Infinite Series https://www.youtube.com/watch?v=fXvN-pF76-E Computers need to have access to random numbers. They’re used to encrypt information, deal cards in your game of virtual solitaire, simulate unknown variables -- like in weather prediction and airplane scheduling, and so much more. But How can a computer possibly produce a random number? Written and Hosted by Kelsey Houston-Edwards Produced by Rusty Ward Graphics by Ray Lux Assistant Editing and Sound Design by Mike Petrow Made by Kornhaber Brown (www.kornhaberbrown.com) Special Thanks to Alex Townsend Big thanks to Matthew O'Connor and Yana Chernobilsky who are supporting us on Patreon at the Identity level! And thanks to Nicholas Rose and Mauricio Pacheco who are supporting us at the Lemma level!
Views: 103426 PBS Infinite Series
Cryptography stream ciphers and pseudo random generators
Cryptography Stream ciphers and pseudo random generators To get certificate subscribe: https://www.coursera.org/learn/crypto Playlist URL: https://www.youtube.com/playlist?list=PL2jykFOD1AWYosqucluZghEVjUkopdD1e About this course: Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key.
Views: 454 intrigano
True Random Number Generators - FST-01 - Well Tempered Hacker
Randomness forms the basis of cryptography but computers are deterministic and therefore terrible for generating true randomness. In this episode we'll look at the FST-01, a $35 USB based true random number generator (TRNG) which harvests randomness from the environment. We'll flash the NeuG random number generator software onto the device using a serial programmer and a few wires. Then we'll plug it in, start it up and look at the random data it generates. Hardware: http://www.seeedstudio.com/wiki/FST-01 http://www.seeedstudio.com/depot/s/fst-01.html Software: http://www.gniibe.org/memo/development/gnuk/rng/neug.html #crypto #cryptography #random #randomnumber #geigercounter #cryptography #mouse #pgp #privatekey #flyingstonetiny #FST-01 #randomnumbergenerator #environment #computing #communication #messaging #mail #email﻿
Views: 12927 Anders Brownworth
Random bytes and  random int functions
Two new functions have been added to generate cryptographically secure integers and strings in a cross platform way: random_bytes() and random_int().
Views: 959 Avelx
Random numbers on the blockchain
Random numbers on the blockchain: How to guarantee randomness between multiple parties not trusting each other I will discuss the different techniques used to get random number on the blockchain. The talk will cover the security of the methods from technical and game-theoretical point of views. The first 4 techniques will be literature review. While the “Sequential proof of work” will also cover my own research. Clément Lesaege CTO of Kleros, a crowdsourced dispute resolution Dapp. Clément holds a Master of Science in Computer Science from Georgia Tech. He started playing with blockchain technology in 2013. He has worked as blockchain freelancer and focused on finding vulnerabilities in smart contracts.
Views: 660 Ethereum Foundation
[wr0ng 2017] Security of Pseudo-Random Number Generators With Input - Damien Vergnaud
A pseudo-random number generator (PRNG) is a deterministic algorithm that produces numbers whose distribution is indistinguishable from uniform. A formal security model for PRNG with input was proposed in 2005 by Barak and Halevi. This model involves an internal state that is refreshed with a (potentially biased) external random source, and a cryptographic function that outputs random numbers from the internal state. In this talk, we will discuss the Barak-Halevi model and its extension proposed in 2013 by Dodis, Pointcheval, Ruhault, Wichs and Vergnaud to include a new security property capturing how a PRNG should accumulate the entropy of the input data into the internal state. We will present analysis of the security of real-life PRNGs in this model and present efficient constructions that achieve provable security.
Views: 154 ECRYPT
Proofs in Cryptography: Lecture 5 Pseudo Random Generators
Proofs in Cryptography Lecture 5 Pseudo Random Generators ALPTEKİN KÜPÇÜ Assistant Professor of Computer Science and Engineering Koç University http://crypto.ku.edu.tr
Views: 2667 KOLT KU
Applied Cryptography: Random Numbers in Java (4/5)
Previous video: https://youtu.be/uTlZHRa-ZkM Next video: https://youtu.be/KCcJE8l__H0
Views: 470 Leandro Junes
IOTA tutorial 3: IOTA Seed
If you like this video and want to support me, go this page for my donation crypto addresses: https://www.youtube.com/c/mobilefish/about This is part 3 of the IOTA tutorial. In this video series different topics will be explained which will help you to understand IOTA. It is recommended to watch each video sequentially as I may refer to certain IOTA topics explained earlier. An IOTA seed is 81 characters long and only consists of the latin alphabet characters and the number 9: ABCDEFGHIJKLMNOPQRSTUVWXYZ9 The characters A-Z are all upper case. With the seed the IOTA wallet can generate corresponding addresses. Each specific seed generate addresses belonging to the seed. An IOTA seed looks like: C9RQFODNSAEOZVZKEYNVZDHYUJSA9QQRCUJVBJD9KHAKPTAKZSNNKLJHEFFVK9AWVDAUJRYYKHGWQIAWT According to the official IOTA knowledge base: https://kb.helloiota.com/KnowledgebaseArticle50005.aspx you can use the following methods to generate IOTA seeds: - Linux Operating System: Open a terminal and enter the following command: cat /dev/urandom |tr -dc A-Z9|head -c${1:-81} - Mac Operating System: Open a terminal and enter the following command: cat /dev/urandom |LC_ALL=C tr -dc 'A-Z9' | fold -w 81 | head -n 1 The function /dev/urandom creates cryptographically random numbers by gathering random data for example environmental noise (entropy) from device drivers, network packet timings and other sources into an entropy pool. The data from the entropy pool is used as input for the Cryptographically Secure PseudoRandom Number Generator (CSPRNG) This generator will generate the random numbers. urandom means unlimited random On the Mac there is no difference between /dev/random and /dev/urandom, both behave identically. On Linux systems there are differences between /dev/random and /dev/urandom. In this presentation these differences will not be discussed. Another solution the IOTA knowledge base recommends to generate an IOTA seed is using this web application: https://ipfs.io/ipfs/QmdqTgEdyKVQAVnfT5iV4ULzTbkV4hhkDkMqGBuot8egfA The source code for this seed generator can be found at: https://github.com/knarz/seedgen The knarz/seedgen uses the Stanford Javascript Crypto Library. This library can be found at: https://github.com/bitwiseshiftleft/sjcl More information about this library can be found at: http://bitwiseshiftleft.github.io/sjcl/ http://bitwiseshiftleft.github.io/sjcl/doc The Stanford Javascript Crypto Library (SJCL) is a project by the Stanford Computer Security Lab to build a secure, powerful, fast, small, easy-to-use, cross-browser library for cryptography in Javascript. The SJCL library is used in many web applications. If you want to use the web application to generate an IOTA seed do the following: - Goto https://ipfs.io/ipfs/QmdqTgEdyKVQAVnfT5iV4ULzTbkV4hhkDkMqGBuot8egfA and save the webpage locally on your computer. - Disconnect your computer from the Internet (disable WiFi, or remove your Ethernet cable) - Open the webpage and move your mouse until its reaches 100% - Store your IOTA seed in a secure location. You should NEVER create an IOTA seed by entering 81 characters (A-Z9) yourself on a keyboard. You should NEVER create an IOTA seed using an web application while you are online. You should NEVER use unknown IOTA seed generators. Use the seed generators recommended by the official IOTA knowledge base: https://kb.helloiota.com/KnowledgebaseArticle50005.aspx There are several online IOTA seed generators which do not generate Cryptographically Secure Random Numbers which means there is big chance someone else can generate the same seed as you have. Check out all my other IOTA tutorial videos https://goo.gl/aNHf1y Subscribe to my YouTube channel: https://goo.gl/61NFzK The presentation used in this video tutorial can be found at: https://www.mobilefish.com/developer/iota/iota_quickguide_tutorial.html #mobilefish #howto #iota
Views: 10862 Mobilefish.com
EYL - Micro Quantum Random Number Generator
EVERYWHERE IN YOUR LIFE, EYL Lately, as the frequency of threats to data and personal information has been increasing, the security of encryption keys has become crucially important for the perfect security in all areas of information and communication industry. Encryption keys are composed of random numbers that should be impossible to decipher nor predict. Existing Pseudo-random number imitates perfect random number with its generated values from an algorithm that is predictable and vulnerable to hacking. However, EYL will provide perfect random numbers with the world's first encryption technology that utilizes Quantum-random number generator. Since Quantum-random number generator has a mechanism of producing random numbers from detecting the particles emitted randomly and naturally from the radioactive isotopes. EYL provides the perfect encryption keys that even the best hacker cannot even break. As the number of IoT devices is growing exponentially with threatening security risks in reality EYL will provide the perfect security through the encryption technology utilizing quantum-random numbers. In the future, EYL's QRNG, smaller in size with stronger security, will protect your daily lives. QUANTUM SECURITY WILL BE RIGHT IN YOUR POCKET … … … EYL If you have a question, please email to [email protected]
Pseudo Random Number Generator Solution - Applied Cryptography
Views: 2647 Udacity
The Lava Lamps That Help Keep The Internet Secure
At the headquarters of Cloudflare, in San Francisco, there's a wall of lava lamps: the Entropy Wall. They're used to generate random numbers and keep a good bit of the internet secure: here's how. Thanks to the team at Cloudflare - this is not a sponsored video, they just had interesting lava lamps! There's a technical rundown of the system on their blog here: https://blog.cloudflare.com/lavarand-in-production-the-nitty-gritty-technical-details Edited by Michelle Martin, @mrsmmartin I'm at http://tomscott.com on Twitter at http://twitter.com/tomscott on Facebook at http://facebook.com/tomscott and on Snapchat and Instagram as tomscottgo
Views: 1255760 Tom Scott
Pseudo Random Number Generators - Peter Faiman
Peter Faiman White Hat VP, talks about pseudo-random number generators (PRNGs), random number quality, and the importance of unpredictable random numbers to cryptography.
Views: 2992 White Hat Cal Poly
PRNG Part 1
Part 1 of a 3 part lesson on Pseudo Random Number Generators (PRNGs)
Generate Strong Passwords with PWGen
PWGen is a free professional strong password generator and, by using this application it is possible to generate cryptographically-secure passwords.
Views: 4178 TechTipsHub
Radioactive Random Number Generator☢️
Ever want to generate random numbers? Radioactivity is the way to go! This Counter is compatible with the Arduino so you can make one yourself! https://amzn.to/2zKDbLS Donate to TT: https://www.paypal.me/TannerTech
Views: 1056 Tanner Tech
CSPRNG Meaning
Video shows what CSPRNG means. cryptographically secure pseudo-random number generator. CSPRNG Meaning. How to pronounce, definition audio dictionary. How to say CSPRNG. Powered by MaryTTS, Wiktionary
Views: 171 ADictionary
Random Numbers
How to get random numbers and animate dice as part of a series of modules to teach key stage 1 & 2 children real computer skills.
SHA: Secure Hashing Algorithm - Computerphile
Secure Hashing Algorithm (SHA1) explained. Dr Mike Pound explains how files are used to generate seemingly random hash strings. EXTRA BITS: https://youtu.be/f8ZP_1K2Y-U Tom Scott on Hash Algorithms: https://youtu.be/b4b8ktEV4Bg http://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: http://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com
Views: 441536 Computerphile
#5 computer security techniques, continued + cryptography primitives
- surveillance - choke point - need to know - don't do crypto yourself Cryptographic primitives - hash functions and their basic properties - pseudo-random number generators - determinism - period - entropy - /dev/random vs /dev/urandom
Views: 269 ralienpp
Because "use urandom" isn't everything: a deep dive into CSPRNGs in Operating Systems & Programming
Implementation, hazards and updates on use of RNGs in programming languages and the Linux Kernel (among others) Over the past year multiple people have been engaging language maintainers and designers to change their use of CSPRNGs (mainly relying on user-land RNGs like the one from OpenSSL, and sometimes suggesting "adding entropy" by various means from user-land daemons like haveged). In this short presentation we'll survey the struggle of cryptographers, developers and security engineers to change the path various high-profile languages have taken to provide randomness to their userbase. Affected languages include but are not limited to: Ruby, node.js and Erlang. We outline better approaches for language maintainers and implementers as well as coming changes within the Linux kernel crypto subsystem (i.e. /dev/random and /dev/urandom) w.r.t. security and performance. Recently these changes were merged into mainline Linux (4), problems with languages implementations however remain. We'll also discuss operating system provided randomness testing, attacks/mitigation in embedded and virtualized environments. #Software #Security Aaron Zauner (azet)
Views: 217 SHA2017
Startup: High-quality Random Number Generator
In this video Luka Matic explains why and how he designed and built a super duper random number generator that passes official regulatory (DIN, NIST, etc.) tests for randomness. Based on noise Zener diodes the circuit fills an SD card with files of up to 4 GB of really random data. On http://www.elektormagazine.com Luka writes: This Random Number Generator uses Zener diodes to generate avalanche noise signal, then differential amplifier (and a few analog filters) to eliminate deterministic effects. The noise signal is captured by ATTiny2313 and sent to an FAT32 SD card as a sequence of meaningless hex numbers. I tested random number sequences in MATLAB to check for randomness. I designed and produced a prototype PCB (that you can see on the elektormagazine website (https://www.elektormagazine.com/labs/random-number-generator-150116). This RNG is designed with cheap and ubiquitous components and still creates random sequences of good randomness. I tried all the methods that I know to analyze the random sequences. If you know more about mathematical methods for checking the randomness of the sequence, you could be interested to check yourself. I have the circuit schematics drawn on paper.
Views: 4252 www.elektor.tv
Random Number Generator
Random Number Generator :))
Views: 593 Chainerlt

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