The Industrial Internet of Things (IIoT) is crucial for smart manufacturing and automation but is highly vulnerable to cyber-attacks due to inadequate security mechanisms and diverse communication protocols. Attackers can exploit weak IIoT devices to infiltrate entire networks, posing significant challenges in detecting malicious traffic within heterogeneous IIoT environments. This study introduces a hybrid model, composed of BERT and LSTM network for anomaly detection in IIoT networks. Given that the effectiveness of such models depends on the quality of the training data, this research investigates the effects of data imbalance and granularity, offering solutions to these issues. Experimental results show that the proposed method effectively identifies malicious traffic and outperforms other reference models in detection efficiency.