Optimized hybrid cryptographic model with deep learning for secure IoT data

The rapid growth of smart devices has increased the demand for robust and scalable encryption mechanisms while expanding the attack surface to cyber threats, especially in Internet of Things (IoT) environments. Recent advances in Deep Learning (DL) have created new opportunities to improve encryption methods and enable secure data storage and transmission against emerging cyber threats. This paper presents an optimized security framework that integrates DL with an optimized cryptographic scheme to provide secure and effective data protection. A Hybrid Convolutional Neural Network and Long Short-Term Memory (HCL) model is used to generate candidate cryptographic keys, then Snake Optimizer (SO) is applied to select an optimal key that maximizes entropy and minimizes encryption and decryption time. To enhance data confidentiality, Advanced Encryption Standard and Blowfish (ABF) algorithms are used. In key selection experiments, the results showed that HCL-SO achieved the lowest average key selection time (211 ms ), with improvements of 17.51%, 9.14%, and 16.55%, compared to the HCL integrated with Grey Wolf Optimization (GWO), Honey Badger Algorithm (HBA), and Harris Hawks Optimization (HHO), respectively. For data confidentiality, the proposed key generation mechanism with the ABF algorithm achieved an average of 64.2% lower encryption latency and 25.3% higher throughput compared to the standard AES-Blowfish encryption model. Furthermore, the security of the proposed method was validated using NIST statistical tests and key sensitivity analysis. The results demonstrated high randomness and strong sensitivity to small changes, indicating that the proposed framework provides an effective and secure approach for protecting IoT data applications.