## 1 Introduction
### Related Works  
A significant body of research has been dedicated to advancing intrusion detection within IoT ecosystems using various machine learning and optimization-based frameworks. With the exponential growth of IoT devices and the escalating complexity of cyberattacks, the focus has shifted towards building more intelligent and adaptive IDS models that can handle both known and novel intrusions. [PERSON] et al. [10] explored how classical machine learning models can be effectively adapted for intrusion detection in IoT and IIoT scenarios. By benchmarking a variety of classifiers, including decision trees, random forests, and ensemble-based methods across the TON_IoT, WUSTL-IIoT-2021, and EdgelloTset datasets, they identified lightweight models best suited for deployment in environments with constrained resources. Their evaluation addressed real-time metrics such as computational efficiency and feature selection via mutual information, while also analyzing the impact of data imbalance. Notably, their transfer learning setup revealed promising generalization across different datasets, reinforcing the importance of interoperability in IDS deployment. [PERSON] et al. [31] presented IoTForge Pro, a testbed specifically designed for generating high-quality intrusion detection datasets tailored to IIoT systems. The Forge IIOT dataset, created through this platform, includes diverse attack scenarios that mirror real-world IIoT environments. Their study demonstrated how various machine learning algorithms perform in classifying and detecting these threats, emphasizing the importance of custom-built datasets for evaluating IDS models. The paper contributes a valuable foundation for developing IDSs that are both robust and representative of industrial-scale traffic. [PERSON] et al. [32] conducted a comparative evaluation of datasets used in the cybersecurity of industrial control systems, highlighting critical limitations such as narrow attack coverage and lack of support for multi-stage attacks. Their analysis revealed that while many datasets offer partial representations of threat landscapes, X-IIoTID stands out as one of the most complete resources currently available. Their findings underscore the necessity of dataset quality and completeness in the development of IDSs tailored for operational technology networks. [PERSON] et al. [33] proposed WFE-Tab, a novel ensemble-based model built upon the TabPFN foundation to enhance intrusion detection in IIoT systems equipped with edge computing. Recognizing the limitations of TabPFN in handling complex class structures and limited training data, they introduced a weighted fusion strategy that improves generalization across diverse attack types. The WFE-Tab model achieved high performance on the Edge-IIoTset dataset, outperforming existing benchmarks with an F1-score close to 99.81%, making it a compelling candidate for deployment in IIoT-MEC environments. In a separate contribution, [PERSON] et al. [34] evaluated the application of TabPFN models in IIoT environments characterized by limited data availability. Their IDS approach leverages TabPFN's efficiency in low-sample settings to detect a range of attack types, benchmarking it against established models such as XGBoost and LightGBM. The study demonstrated notable performance improvements with minimal data, achieving strong F1 scores in scenarios with as few as 1000 samples. Their work positions TabPFN as a suitable choice for edge-deployable IDSs in data-sparse IIoT systems. [PERSON] et al. [35] focused on building a streamlined end-to-end IDS capable of addressing the specific challenges found in industrial IoT networks. Using the Edge-IIoTsetdataset, their CNN1D-based model was trained to detect a comprehensive set of 15 attack types while maintaining low complexity. Their system demonstrated impressive classification performance, with near-perfect accuracy and low loss across cross-validation folds.