The rapid expansion of the Industrial Internet of Things (IIoT) necessitates the digitization of industrial processes in order to increase network efficiency. The integration of Digital Twin (DT) with IIoT digitizes physical objects into virtual representations to improve data analytics performance. Nevertheless, DT empowered IIoT generates a massive amount of data that is mostly sent to the cloud or edge servers for real-time analysis. However, unreliable public communication channels and lack of trust among participating entities causes various types of threats and attacks on the ongoing communication. Motivated from the aforementioned discussion, we present a blockchain and Deep Learning (DL) integrated framework for delivering decentralized data processing and learning in IIoT network. The framework first present a new DT model that facilitates construction of a virtual environment to simulate and replicate security-critical processes of IIoT. Second, we propose a blockchain-based data transmission scheme that uses smart contracts to ensure integrity and authenticity of data. Finally, the DL scheme is designed to apply the Intrusion Detection System (IDS) against valid data retrieved from blockchain. In DL scheme, a Long Short Term Memory-Sparse AutoEncoder (LSTMSAE) technique is proposed to learn the spatial-temporal representation. The extracted characteristics are further used by the proposed Multi-Head Self-Attention (MHSA)-based Bidirectional Gated Recurrent Unit (BiGRU) algorithm to learn long-distance features and accurately detect attacks. The practical implementation of our proposed framework proves considerable enhancement of communication security and data privacy in DT empowered IIoT network. IEEE