With the expansion of the Industrial Internet of Things in critical infrastructures, securing these systems has become a strategic necessity. In this context, deep learning-based approaches have emerged as effective solutions for enhancing the accuracy of cyberattack detection. However, the dispersion of studies, the diversity of proposed architectures, and variations in datasets and evaluation metrics have made comprehensive comparisons among these methods challenging. This study adopts a holistic approach to examine convolutional, recurrent, autoencoder, generative adversarial, and deep reinforcement learning-based methods. These approaches are analyzed in terms of architecture, deployment scenarios, and comparative performance. The input data for this study include selected publications in deep learning-based intrusion detection, commonly used network traffic datasets, and reported performance metrics. The novelty and contribution of this research lie in providing a structured analytical framework for multi-criteria comparison of existing methods, integrating scattered numerical results, identifying key research gaps related to scalability, latency, and real-time deployability, and outlining future research directions aimed at developing efficient and reliable intrusion detection systems in real-world IIoT environments. Analysis of the reported numerical results indicates that accuracy, false alarm rate, and F1-score in deep learning-based systems range between 94-97%, 1.5-2%, and 0.94-0.97, respectively.