Deep intelligent blockchain technology for securing IoT-based healthcare multimedia data

Nowadays, Internet of Things (IoT) based applications are widely used in different sectors because of their high mobility, low cost, and efficiency. However, the wide usage of these applications leads to various security issues. Several security applications exist for protecting multimedia data, but the appropriate confidential range is not met due to the multi-variant features. Hence, the novel hybrid Elman Neural-based Blowfish Blockchain Model has been developed in this article to secure IoT healthcare multimedia data. Here, the Elman network features provided continuous monitoring for predicting malicious events in the trained multimedia data. In addition, the crypto analysis was performed to enhance the confidentiality rate by hiding the raw data from third parties. The presented model was verified using python software. Furthermore, the robustness of the developed model is validated with a crypt analysis by launching attacks. Finally, the outcomes were estimated and compared with the existing techniques in terms of Encryption time, decryption time, execution time, error rate and confidential rate. Here, the evaluation database is the multimedia data, which is high in data size. Henceforth, the performance of the security model for securing multimedia data depends on time. Considering this, the time evaluation is measured in three classes: encryption, decryption and execution. The comparative analysis proves that the developed model achieved better results than others.