Implementation of AES Encryption Algorithm on MATLAB Platform with Code Analysis

Implementing the AES encryption algorithm on the MATLAB platform requires understanding its core processes, including key expansion, byte substitution, row shifting, column mixing, and round key addition. The following analysis covers key implementation approaches:

S-Box Generation The AES S-Box is a nonlinear substitution table constructed through finite field operations. In MATLAB, it can be generated using polynomial operations and modular inverse calculations, ensuring each byte maps to a unique substitute value. Implementation typically involves creating lookup tables using MATLAB's array initialization capabilities.

Key Expansion The initial key is expanded into multiple round subkeys using the Rijndael key scheduling algorithm. MATLAB's matrix operations and loop structures are well-suited for implementing key grouping, cyclic left shifts, and round constant XOR operations. This can be efficiently coded using cell arrays or matrices to store round keys.

Round Function Processing Byte Substitution: Direct lookup from the S-Box table to replace each byte in the state matrix, implemented using MATLAB's indexing operations. Row Shifting: Cyclic shifting of each row in the state matrix, efficiently handled through MATLAB's array indexing and circshift functions. Column Mixing: Matrix multiplication through finite field multiplication, requiring special attention to GF(2^8) arithmetic rules. Implementation involves creating custom functions for finite field multiplication. Round Key Addition: Simple XOR operation, easily implemented using MATLAB's element-wise bitxor function.

Complete Process Integration Based on AES-128/192/256 round requirements, the round functions are called iteratively, with the column mixing step omitted in the final round. MATLAB script or function encapsulation can enhance code reusability through modular programming approaches.

Extended Considerations While MATLAB's numerical computation advantages make it suitable for algorithm verification, practical encryption applications require performance optimization, such as precomputing S-Box and column mixing matrices. Additionally, comparing with C/C++ implementations can analyze platform differences in encryption efficiency, with MATLAB offering better prototyping capabilities while compiled languages provide faster execution.