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The Industrial Internet of Things (IIoT) is a key component of Industry 4.0, the fourth industrial revolution that leverages digital technologies to optimize industrial processes and operations. IIoT refers to the network of interconnected sensors, instruments, and devices that collect, exchange, and analyze data from various industrial domains, such as manufacturing, energy, transportation, and health care [1, 2]. IIoT enables improved productivity, efficiency, quality, and sustainability in industrial systems, as well as new business models and opportunities [3, 4]. However, the widespread adoption of IIoT also poses significant security challenges, as IIoT infrastructure becomes a prime target for cyberattacks that can compromise the confidentiality, integrity, and availability of critical data and services [5, 6]. Moreover, the heterogeneous, dynamic, and complex nature of IIoT environments makes it difficult to apply traditional security solutions that rely on predefined rules or signatures to detect and prevent attacks [6, 7]. Therefore, there is a pressing need to develop advanced security measures that can cope with the evolving and sophisticated cyber threats facing IIoT systems. One promising direction is to integrate intrusion detection systems (IDSs) with machine learning (ML) algorithms to enhance the security of IIoT environments [8–14]. IDSs are tools that monitor network traffic and devices for any malicious or anomalous activities and alert the security administrators to take appropriate actions [15, 16]. ML is a branch of artificial intelligence that enables systems to learn from data and make predictions or decisions without explicit programming [17, 18]. By combining the strengths of IDSs and ML, it is possible to develop a robust security framework that can automatically learn the normal behavior patterns of IIoT devices and swiftly detect any deviations indicative of potential security breaches [19, 20]. Furthermore, ML can help to identify the type and source of the attacks, as well as to adapt to the changing patterns and dynamics of the IIoT landscape [21–24]. In this chapter, we investigate the integration of IDS with ML algorithms to fortify the security of IIoT environments. We delve into the intricacies of developing a robust IDS framework that harnesses the capabilities of ML for anomaly detection and threat identification. We propose a novel approach that employs ML algorithms to analyze real-time data streams generated by IIoT devices and discern normal behavior patterns and swiftly detect deviations indicative of potential security breaches. We evaluate the effectiveness of various ML techniques in enhancing the accuracy and responsiveness of IDS in the dynamic and complex IIoT landscape. We also compare our approach with existing IDS and ML solutions and demonstrate its superior performance and scalability. The findings underscore the significance of leveraging ML-driven IDS to proactively mitigate cyber threats, safeguarding the integrity and reliability of IIoT ecosystems. The rest of the chapter is organized as follows. Section 1.2 provides a background on IIoT, IDS, and ML. Section 1.3 presents the proposed approach for integrating IDS with ML algorithms for IIoT security. Section 1.4 describes the experimental setup and results. Section 1.5 concludes the chapter and suggests future directions.