## 3 Existing Surveys and Motivations
provided a comprehensive survey on layer-wise IoT security threats and AI-based countermeasures while taking into account a five-layer IoT model. Various security solutions were discussed and categorized based on the IoT layers and their respective layer-specific attacks. Most of the proposed security solutions were related to ML and DL techniques. However, the discussion on datasets was not included in this survey, although datasets are key components of ML and DL learning models. The study [44] presented a thorough examination of Explainable Artificial Intelligence (XAI) across various domains, including healthcare, industrial IoT, and smart cities, particularly focusing on transparency, trust, and decision-making in IoT contexts. Furthermore, the paper provided a detailed discussion on various XAI frameworks, such as Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Explain Like I'm 5 (ELIS). Moreover, the authors mentioned the integration of 6 G networks and edge computing technologies to support IoT environments. The paper offers valuable contributions to the field of XAI for IoT. However, it lacks discussion on the implementation of sophisticated XAI models in resource-constrained IoT environments and the interpretation of non-linear data through XAI techniques. [PERSON] et al. [45] discussed the overview of IoT security issues and presented a comprehensive examination of malware detection, covering static malware detection, dynamic malware detection, advanced malware detection, and hybrid malware detection, in this study. Moreover, the paper includes a detailed survey, which discusses emerging ML algorithms for detecting malware in IoT systems. However, the discussion on ML algorithms was insignificant. [PERSON] et al. [46] presented a survey that focuses on IoT vulnerability, effective remedies, and solutions currently available to deal with the threats that result in the exploitation of these vulnerabilities. This paper also discussed different ML and DL techniques to secure the IoT paradigm. However, a limited amount of discussion has been provided. [PERSON] et al. [47] presented a systematic investigation of AI techniques used in cyber-security to prevent intrusions and provide security in IoT-based systems. The survey covers various parts of attack domains but only includes bibliometric analysis with no in-depth discussion related to the aforementioned domains. The paper [48] provided a detailed survey of attacks and their classification concerning various aspects, such as types, attacks across different layers of the IoT, vulnerabilities, and the exploitation of protocols leading to attacks. Furthermore, the detailed discussion on IoT datasets covers technical aspects including domain, total number of features, records, number of devices used, and reproducible factors. The survey also presents the potential features commonly used in IoT datasets for attack detection. Furthermore, the paper conducts a comparative study that involves assessing the performance of IoT datasets with consideration of feature types, feature selection techniques, and ML and DL algorithms utilized in IDS model training and testing. This study provides concise insights into IoT datasets, the classification of attacks, and the methodologies involving ML/DL for establishing robust IDS. This study gives a brief insight into IoT datasets, attack classification, and ML/DL methodologies for building robust IDS. However, the paper lacks a substantial discussion on feature selection methods, which play a pivotal role in choosing features employed by ML methods to build resilient IDS. The survey paper [49] discussed the application of XAI in intrusion detection within IoT networks. It provides a comprehensive overview of AI models, encompassing both ML and DL, specifically designed for anomaly detection in IoT ecosystems. Furthermore, the paper addresses the challenges faced by XAI in the context of enhancing security in IoT applications, offering potential solutions to these issues.