## 3 Existing Surveys and Motivations
However, the paper falls short in addressing potential limitations associated with the implementation of XAI in IDS for IoT. Notably, the trade-off between model complexity and interpretability is not thoroughly discussed. Additionally, the impact of employing XAI techniques on the overall detection performance of the system is still in its early stages of exploration. While there are some notable exceptions, such as [44] and the survey work in [50], which have begun to address this issue, the evaluation of XAI techniques based on resource consumption requires further investigation, specifically tailored for IoT-based systems. Another study [50] presented a comprehensive survey of the applications of XAI in industrial environments. It highlights the growing importance of XAI in manufacturing and Industrial Cyber-Physical Systems, particularly in areas such as predictive maintenance, fault detection, process control, and cyber-security. The paper systematically categorizes XAI methods, discusses their benefits, and emphasizes the need for transparent AI models in critical infrastructure, where decision-making must be interpretable by human operators. It further explores the challenges and opportunities of implementing XAI in various industrial applications to enhance trust and accountability in AI systems. However, it does not discuss any studies focused on real-time XAI solutions that can provide immediate insights for dynamic decision-making in industrial environments. The survey paper [51] presents comprehensive studies conducted in the field of ML-enabled IDS specifically designed for IoT-based systems. The authors introduce a taxonomy that classifies IDS based on validation strategy, security threat, IDS placement strategy, and detection method. The challenges associated with implementing IDS in IoT are discussed, acknowledging specific features like limited resources, heterogeneity, and diverse network protocols inherent in IoT environments. The paper provides a valuable resource for researchers and practitioners in the field of IoT security and intrusion detection. However, the paper lacks an in-depth analysis of the strengths and weaknesses inherent in the various IDS approaches discussed. A comparative study of relevant literature discussed above is presented in Table 1 along with the proposed study in this work, which highlights the need to conduct this study. As shown in Table 1, various studies have been conducted in the domain of ML-enabled IDS for IoT. However, no study has focused specifically on lightweight IDS based on ML for IoT. In this paper, we have concentrated exclusively on studies that address the design of lightweight IDS for resource-constrained IoT systems. Moreover, this survey covers other aspects, including state-of-the-art datasets, popular FS methods used in designing lightweight IDS, and a detailed discussion of lightweight IDS solutions constituted by different approaches. Additionally, FS-enabled lightweight IDS with a focused study towards the emerging EFS techniques as the core approach to design lightweight classification models for cyber-attack detection. This survey work comprehensively covers all important aspects of FS/EFS-based techniques used for designing lightweight IDS in the IoT ecosystem by providing a review with sufficient depth and breadth. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Survey** & **Threats at** & **Discussion** & **ML-** & **Review on** & **Discussion** & **Discussion on** & **Selection** & **Ensemble** & **Explainable** & **Parformance** \\
**Papers** & **IoT Layers** & **on ML Algorithms** & **IDS** & **Datasets** & **Topes** & **Lightweight IDS** & **Tech-** & **Feature** & **Feature** & **Fearative** \\ \hline This & & & & & & & & & & & & \\ Paper & & & & & & & & & & & \\ \hline
[35] & ✔