## 2 Related Work
Utilizing the WUSTL-IIoT-2021 dataset, their research demonstrated the model's effectiveness in analyzing sequential network flows using a streamlined multi-head attention mechanism. [PERSON] et al. (2024) [43] introduced an ensemble framework incorporating an enhanced harmony search algorithm (HBO) for feature selection in IDSs. Their approach, tested on datasets such as NSL-KDD, WUSTL-IIoT-2021, and HAI, improved intrusion detection accuracy by nearly 15% on large-scale datasets while significantly reducing the number of original features. Lastly, [PERSON] and [PERSON] (2023) [44] conducted a comprehensive review of existing IIoT network datasets and advanced persistent threat (APT) attack characteristics, proposing a standardized evaluation framework for benchmarking IDS models. Their assessment, applied to datasets including WUSTL-IIoT-2021, underscored the dataset's significance in advancing IIoT security research and developing effective countermeasures against sophisticated cyberthreats. In summary, studies utilizing the WUSTL-IIoT-2021 dataset frequently adapt their methodologies and consistently report high accuracy in intrusion detection. Many of the proposed models evaluated on this dataset achieve accuracy nearing 99%, reflecting a prevailing trend in developing robust and effective intrusion detection systems for IIoT environments. Several studies have demonstrated superior performance compared to the existing literature, highlighting the reliability and effectiveness of these approaches. The strong emphasis on high accuracy underscores the critical need for advanced, trustworthy security solutions to protect IIoT networks. As a widely adopted benchmark, the WUSTL-IIoT-2021 dataset plays a key role in these evaluations. As shown in Table 1, the WUSTL-IIoT-2021 dataset has been analyzed in various studies. However, as it is relatively new compared to other datasets in the field, research directly focused on it remains limited. This study is expected to contribute to its growing adoption. Furthermore, this study stands out by examining three different modeling approaches--machine learning, deep learning, and hybrid models--making it one of the few in this domain to do so. The analysis also incorporates 12 different models, a rare approach in existing research. While hybrid methods have generally outperformed standalone deep learning techniques in previous studies, the specific models employed in this research demonstrated superior performance on the WUSTL-IIoT-2021 dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Authors** & **Year** & **Type** & **Dataset** & **Accuracy (\%)** & **F1 Score (\%)** \\ \hline
[30] & [PERSON] et al. & 2022 & MPL & WUSTL-IIoT-2021 & 99.97 & 99.97 \\
[31] & [PERSON] et al. & 2023 & IDS & WUSTL-IIoT-2021 & 94.29 & 93.96 \\  & & & & Combined dataset & & \\
[32] & [PERSON] & & & (Edge-IIoTset, & & \\  & and [PERSON] & 2024 & DL model & & WUSTL-IIoT-2021, & 94.83 & 94.65 \\  & & & & and XIIoTID & & \\
[33] & [PERSON] et al. & 2023 & ML-based IDS model & WUSTL-IIoT-2021 & 99.97 & \\
[9] & [PERSON] & 2023 & System & WUSTL-IIoT-2021 & 99.98 & 99.98 \\
[34] & [PERSON] et al. & 2023 & FDL model & X-IIoTID, Edge-IIoTset, & 99.39 & 99.90 \\  & [PERSON] & 2023 & CNN-GRU model & WUSTL-IIoT-2021 & 99.98 & \\
[35] & [PERSON] et al. & 2023 & DL model & WUSTL-IIoT-2021 & 98.95 (FNN) & 68.48 (FNN) \\
[36] & [PERSON] et al. & 2023 & ML algorithms & WUSTL-IIoT-2018 \& & 99 \\
[40] & [PERSON] et al. & 2023 & Semi-supervised & WUSTL-IIoT-2021 & 99.93 & 100 \\
[41] & [PERSON] et al. & 2023 & Machine learning (ML) & WUSTL-IIoT-2021 & 99.99 & 99.96 \\
[33] & [PERSON] et al. & 2024 & SMOTE-based multi-class & WUSTL-IIoT-2021 & 99.99 & 95.4–99.98 \\
[42] & [PERSON] et al. & 2023 & NIDS framework & WUSTL-IIoT-2021 & 96.37 & 92.51 \\
[43] & [PERSON] et al. & 2023 & Informory search & NSL-KDD, & 98.72 (precision) & \\  & & & & & 96.36 (recall) & 97.27 \\  & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Comparison of other work on intrusion detection.