tailieunhanh - Lecture Network security: Chapter 19 - Dr. Munam Ali Shah

The topic discussed in this chapter are: Attacks on pseudorandom generators, tests for pseudorandom functions, true random generators. After studying this chapter you will be able to present an understanding of the random numbers and pseudorandom numbers; understand the use and implementation of TRNG, PRNG and PRF. | Network Security Lecture 19 Presented by: Dr. Munam Ali Shah Summary of the Previous Lecture We have discussed public/ asymmetric key cryptography in detail and RSA was discussed as an example. RSA Algorithm We have explored the TRNG and PRNG Introduction to Pseudorandom Numbers Some Pseudorandom Number Generators Summary of the Previous Lecture by Rivest, Shamir & Adleman of MIT in 1977 best known & widely used public-key scheme Block cipher scheme: plaintext and ciphertext are integer between 0 to n-1 for some n Use large integers . n = 1024 bits Summary of the Previous Lecture sample RSA encryption/decryption is: given message M = 88 (nb. 88<187) encryption: C = 887 mod 187 = 11 decryption: M = 1123 mod 187 = 88 Outlines of today’s lecture Attacks on Pseudorandom generators Tests for pseudorandom functions True Random generators Objectives You would be able to present an understanding of the random numbers and pseudorandom numbers . You would be able understand the use and . | Network Security Lecture 19 Presented by: Dr. Munam Ali Shah Summary of the Previous Lecture We have discussed public/ asymmetric key cryptography in detail and RSA was discussed as an example. RSA Algorithm We have explored the TRNG and PRNG Introduction to Pseudorandom Numbers Some Pseudorandom Number Generators Summary of the Previous Lecture by Rivest, Shamir & Adleman of MIT in 1977 best known & widely used public-key scheme Block cipher scheme: plaintext and ciphertext are integer between 0 to n-1 for some n Use large integers . n = 1024 bits Summary of the Previous Lecture sample RSA encryption/decryption is: given message M = 88 (nb. 88<187) encryption: C = 887 mod 187 = 11 decryption: M = 1123 mod 187 = 88 Outlines of today’s lecture Attacks on Pseudorandom generators Tests for pseudorandom functions True Random generators Objectives You would be able to present an understanding of the random numbers and pseudorandom numbers . You would be able understand the use and implementation of TRNG, PRNG and PRF A random number generator (RNG) is a computational or physical device designed to generate a sequence of numbers or symbols that lack any pattern, . appear random. The many applications of randomness have led to the development of several different methods for generating random data True Random number generator (TRNG) Introduction Usage Almost all network security protocols rely on the randomness of certain parameters Nonce - used to avoid replay session key Unique parameters in digital signatures Monte Carlo Simulations - is a mathematical technique for numerically solving differential equations. Randomly generates scenarios for collecting statistics. Introduction (Desirable) Properties of Pseudorandom Numbers Uncorrelated Sequences - The sequences of random numbers should be serially uncorrelated Long Period - The generator should be of long period (ideally, the generator should not repeat; practically, the repetition should occur only after the .