Abstract: Crypto System Identification is one of the challenging tasks in Crypt analysis. The paper discusses the possibility of employing Neural Networks for identification of Cipher Systems from cipher texts. Cascade Correlation Neural Network and Back Propagation Network have been employed for identification of Cipher Systems. Very large collection of cipher texts were generated using a Block Cipher (Enhanced RC6) and a Stream Cipher (SEAL). Promising results were obtained in terms of accuracy using both the Neural Network models but it was observed that the Cascade Correlation Neural Network Model performed better compared to Back Propagation Network.
Abstract: In this paper, RSA encryption algorithm and its hardware
implementation in Xilinx-s Virtex Field Programmable Gate
Arrays (FPGA) is analyzed. The issues of scalability, flexible performance,
and silicon efficiency for the hardware acceleration of
public key crypto systems are being explored in the present work.
Using techniques based on the interleaved math for exponentiation,
the proposed RSA calculation architecture is compared to existing
FPGA-based solutions for speed, FPGA utilization, and scalability.
The paper covers the RSA encryption algorithm, interleaved multiplication,
Miller Rabin algorithm for primality test, extended Euclidean
math, basic FPGA technology, and the implementation details of
the proposed RSA calculation architecture. Performance of several
alternative hardware architectures is discussed and compared. Finally,
conclusion is drawn, highlighting the advantages of a fully flexible
& parameterized design.