6 - Lecture #6 SS G513 Network Security Key Distribution...

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Lecture #6 SS G513 Network Security
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Key Distribution symmetric schemes require both parties to share a common secret key issue is how to securely distribute this key often secure system failure due to a break in the key distribution scheme
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Key Distribution given parties A and B have various key distribution alternatives: 1. A can select key and physically deliver to B 3. if A & B have communicated previously can use previous key to encrypt a new key 4. if A & B have secure communications with a third party C, C can relay key between A & B
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Key Distribution Scenario
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Key Distribution Issues hierarchies of KDC’s required for large networks, but must trust each other session key lifetimes should be limited for greater security use of automatic key distribution on behalf of users, but must trust system use of decentralized key distribution controlling purposes keys are used for
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Random Numbers many uses of random numbers in cryptography nonces in authentication protocols to prevent replay session keys public key generation keystream for a one-time pad in all cases its critical that these values be statistically random with uniform distribution, independent unpredictable cannot infer future sequence on previous values
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Natural Random Noise best source is natural randomness in real world find a regular but random event and monitor do generally need special h/w to do this eg. radiation counters, radio noise, audio noise, thermal noise in diodes, leaky capacitors, mercury discharge tubes etc starting to see such h/w in new CPU's problems of bias or uneven distribution in signal have to compensate for this when sample and use best to only use a few noisiest bits from each sample
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Published Sources a few published collections of random numbers Rand Co, in 1955, published 1 million numbers generated using an electronic roulette wheel has been used in some cipher designs cf Khafre earlier Tippett in 1927 published a collection issues are that: these are limited too well-known for most uses
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Pseudorandom Number Generators (PRNGs) algorithmic technique to create “random numbers” although not truly random can pass many tests of “randomness”
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Linear Congruential Generator common iterative technique using: X n +1 = ( aX n + c ) mod m given suitable values of parameters can produce a long random-like sequence suitable criteria to have are: function generates a full-period generated sequence should appear random efficient implementation with 32-bit arithmetic note that an attacker can reconstruct sequence given a small number of values
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This note was uploaded on 03/14/2010 for the course CSE SS ZG513 taught by Professor Sundarb during the Summer '10 term at Birla Institute of Technology & Science.

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6 - Lecture #6 SS G513 Network Security Key Distribution...

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