The integration of IIoT devices into Industry 4.0 marks a major shift towards smarter and more interconnected industrial processes. However, this progress also introduces intricate security vulnerabilities, specifically stemming from the emergence of anomalies that have the potential to undermine the dependability and efficiency of these advanced systems. Within the realm of Industry 4.0, this research undertakes a comprehensive examination of suitable anomaly detection techniques for IIoT devices. The study systematically analyzes the efficacy, scalability, and flexibility of various detection techniques, such as machine learning algorithms, hybrid approaches, and statistical models, in identifying and mitigating possible risks to IIoT environments. The investigation uncovers valuable insights into the performance of these techniques across various operational scenarios, shedding light on their advantages and constraints. This research examines the practical consequences of implementing these methods in real-life situations, emphasizing the crucial significance of anomaly detection in upholding the durability and dependability of Industry 4.0 systems. Through an extensive comparative examination, this research seeks to offer guidance to researchers, professionals, and policymakers in choosing and executing efficient anomaly detection approaches, thus promoting the progress and safeguarding of IIoT ecosystems.

Anomaly Detection Industrial Internet of Things (IIoT) Cybersecurity Statistical Techniques Machine Learning Industry 4.0 Hybrid Detection Systems Scalability Operational Dynamics Application Scenarios