Big data information security encryption scheme based on complex chaotic systems in the Internet of Things

With the increasing demand for image information security in the Internet of Things, traditional encryption methods still have shortcomings in key space, scrambling diffusion coordination, and anti-attack capabilities. A multi-level image encryption model is proposed, which integrates a two-dimensional lagged complex Logistic chaotic system, a complex Chen-Lorenz framework, deoxyribonucleotide bidirectional operation, and wavelet domain decomposition. The model enhances key sensitivity through multi-source chaotic sequences and perturbations from the dynamic perturbation function, achieves nonlinear diffusion in the deoxyribonucleotide domain, and combines wavelet decomposition to improve randomness and robustness. The experimental results demonstrate that the proposed model achieves average encryption and decryption times of approximately 510 ms and 500 ms, respectively. Ablation experiments validate the effectiveness of each submodule from the perspectives of internal state stability and modular synergy. With the integration of composite chaos and dual-domain processing, the root mean square error of intermediate states decreases from 0.12 to 0.06, indicating enhanced numerical stability after multi-module integration. In security testing, the memory consumption for encryption and decryption remains between 250 kb and 500 kb, with the highest value not exceeding 650 kb. Under six typical IoT tampering attacks, the bit error rate of the model is consistently maintained below 0.2, demonstrating superior performance compared to most competing methods. This highlights the ciphertext's excellent statistical indistinguishability. The overall results have verified the comprehensive improvement of the research method in terms of encryption and decryption efficiency, security, and robustness, and have good application and promotion value in Internet of Things scenarios.