The Industrial Internet of Things (IIoT) enhances the connectivity and efficiency of living lifestyles. However, it also comes with significant security vulnerabilities. Traditional authentication methods are often inadequate, leading to IIoT devices opened to security threats. This paper proposes a comprehensive security framework integrating blockchain, cryptographic techniques, smart contracts, and deep learning-based Intrusion Detection Systems (IDS) to tackle the mentioned issue. Blockchain ensures data integrity and prevents tampering through a decentralized ledger. A decentralized device identity management system enhances user verification, while secure communication protocols using Hash-based Message Authentication Codes (HMAC) safeguard data integrity. Smart contracts automate transactions, providing transparent, secure record-keeping without a central authority. The deep learning-based IDS, utilizing Contractive Sparse Autoencoder (CSAE) and Attention-Based Bidirectional Long Short-Term Memory (ABiLSTM) networks, effectively detects cyber threats. Evaluation metrics, including precision, recall, F1-score, and False Acceptance Rate (FAR), demonstrate high accuracy and low false alarm rates across datasets. This framework addresses the need for secure, efficient, and scalable authentication in IIoT, combining blockchain's security features with advanced cryptographic and anomaly detection techniques, offering robust defence against cyber threats.