Research papers in intrusion detection for Industrial Internet of Things (IIoT) focus on developing mechanisms to identify and mitigate malicious activities in industrial networks, where security breaches can have severe operational and safety consequences. IIoT environments consist of interconnected sensors, actuators, controllers, and machines that generate massive amounts of data and operate in real-time, making them vulnerable to cyberattacks such as malware injection, Denial-of-Service (DoS), man-in-the-middle, spoofing, and insider threats. Researchers have explored various intrusion detection approaches tailored for IIoT, including signature-based, anomaly-based, specification-based, and hybrid methods. Signature-based systems detect known attack patterns but struggle with novel threats, whereas anomaly-based systems monitor deviations from normal behavior using statistical analysis, machine learning, or deep learning techniques to detect unknown attacks. Specification-based methods define correct operational behavior for devices and flag deviations, offering low false-positive rates in controlled industrial setups. Hybrid approaches combine multiple techniques to improve detection accuracy while balancing computational overhead. Edge and fog computing paradigms are increasingly integrated to perform intrusion detection closer to the data source, reducing latency, preserving bandwidth, and offloading processing from resource-constrained devices. Some studies also leverage blockchain technology for secure, distributed, and tamper-resistant logging of detected intrusions. Additionally, adaptive and context-aware intrusion detection systems are designed to handle dynamic IIoT environments with heterogeneous devices and variable network conditions. Despite significant progress, challenges remain in achieving scalable, lightweight, and real-time intrusion detection suitable for large-scale IIoT deployments while minimizing false positives and maintaining system reliability. Overall, the literature highlights that robust intrusion detection is critical for safeguarding IIoT networks, ensuring operational continuity, and maintaining trust in industrial automation systems.