Cross-Layer Framework for IoT Data Protection Using Blockchain Based Federated Learning Model (BCFL)

Abstract

In recent years, concerns about the security of data exchanged between the Internet of Things (IoT) devices have increased with respect to information security. Data security and the prevention of critical information leaks are both facilitated by a security model. Using Blockchain technology, the issues with data security in IoT devices may be resolved. Decentralized ledger technology known as blockchain enables many useful applications, including distributed storage, consensus, encryption, and Machine-to-Machine (M2M) data transmission. This paper develops a cross-layer framework for protecting the sensitive data of IoT by applying Blockchain based Federated Learning model (BCFL). The data records are organized into blocks in the BCFL paradigm, and then encode and decode methods are used to send the data to the servers. Using the Federated learning method, the block size and coding redundancy factor are calculated in a block-by-block transmission manner. To protect the integrity of data, a hash function and a digital signature are computed for each block of data. By experimental results, it was shown that the proposed BCFL model attains higher detection accuracy and correctness of data with reduced encoding/decoding time and reconstruction time.