Abstract
In modern scenarios, Industry 4.0 entails invention with various advanced technology, and blockchain is one among them. Blockchains are incorporated to enhance privacy, data transparency as well as security for both large and small scale enterprises. Industry 4.0 is considered as a new synthesis fabrication technique that permits the manufacturers to attain their target effectively. However, because numerous devices and machines are involved, data security and privacy are always concerns. To achieve intelligence in Industry 4.0, blockchain technologies can overcome potential cybersecurity constraints. Nowadays, the blockchain and internet of things (IoT) are gaining more attention because of their favorable outcome in several applications. Though they generate massive data that need to be effectively optimized and in this research work, deep learning-based techniques are employed for this. This paper proposes a novel mutated leader sine cosine algorithm-based deep convolutional neural network (MLSC-DCNN) in order to attain a secure and optimized IoT blockchain for Industry 4.0. Here, an MLSC is hybridized using a mutated leader and sine cosine algorithm to enhance the weight function and minimize the loss factor of DCNN. Finally, the experimentation is carried out for various simulation measures. The comparative analysis is made for Best Tip Selection Method (BTSM), Smart Block- Software Defined Networking (SDN), and the proposed approach. The evaluation results show that the proposed approach attains better performances than BTSM and SDN.