Simulation-Based Performance Evaluation of a GA-Optimized Logistic Map for Lightweight Cryptographic Key Generation in IoT Systems

Abstract

The rapid advancement of the Internet of Things (IoT) has highlighted the need for cryptographic methods that can function under stringent computational limitations. Chaotic systems, such as the logistic map, have drawn considerable attention from researchers because of their complex features, extreme sensitivity to initial conditions, and unpredictable behavior. The choice of suitable control parameters significantly affects the encryption effectiveness of these chaotic systems. To create lightweight cryptographic keys, especially for Internet of Things (IoT) applications, this study presents an advanced version of the logistic map using a genetic algorithm. This technique maximizes the entropy and minimizes the autocorrelation by optimizing the map parameters. The findings show that it is fascinating to see how binary cryptographic keys are created from chaotic sequences, proving their effectiveness in guaranteeing safe communication between IoT devices. Experts assessed the methodology using NIST statistical tests for randomness, entropy autocorrelation analysis, and execution time. Notably, the GA-optimized Logistic map demonstrated increased efficiency and randomness compared to the Tent and Chebyshev maps. According to the experimental results, this framework offers a safe and effective cryptographic solution specifically designed for devices with limited resources.