## 2 Past Research and the Innovations in This Work  
### Past Research  
Numerous studies have effectively utilized neural networks to identify vulnerabilities in network traffic. However, a lot of these studies employ old datasets that do not account for the complexities of present-day networks, including large-scale designs and emerging avenues of attack [22]. A recent study, for instance, uses artificial neural networks (ANNs) to evaluate the combination of hybrid oracle-explainer method for intrusion detection systems (IDSs) using the CICIDS2017 dataset. While this approach offers human-understandable explanations [28], its applicability is limited due to the constraints of the dataset. Similarly, another study [29] implements an explainable AI (XAI) framework using techniques for intrusion detection on the NSL-KDD (2009) dataset, such as SHAP, LIME, CEM, ProtoDash, and BRCG, whereas [30] used the same dataset and SHAP to solve a multiclass classification issue. The study in [31] also used CIC-IDS 2017 for intrusion detection and employed LIME and SHAP for model explainability. In [32], using XAI and a decision tree algorithm,the authors utilized the KDD dataset to improve trust management in IDSs. The NSL-KDD dataset is also used in [33], which employed linear and multilayer perceptron classifiers and offered explanations through clear visuals. Despite the fact that these studies provide insightful conceptual information, their applicability is constrained by their dependence on antiquated datasets that do not take into consideration the dynamic and increasingly complicated network environments of today. Even though some researchers have used more recent datasets, they frequently overlook how crucial it is to balance subcategories and use explainable AI (XAI) methodologies to make sense of the choices their AI models make. In [34], for instance, the author assessed how well different deep learning models detect cybersecurity threats in IoT networks, while in [35], machine learning techniques are used. to identify botnet activities in IoT environments. However, neither of these studies integrates XAI techniques or addresses dataset balancing, which can introduce bias and reduce predictive accuracy. Similarly, other researchers have suggested light weight deep learning techniques for detecting intrusion and DDoS attacks in IoT settings [36; 37]. Additionally, the research in [38; 39] explores tree-based algorithms for binary classification of malicious IoT traffic. However, their approaches also overlook data balancing and/or XAI techniques, which limits transparency and interpretability. The overall efficacy and dependability of the security solutions may be compromised by these flaws, which may cause AI models to exhibit inconsistent performance across several subcategories. To improve the relevance and applicability of the findings in this domain, it is imperative that future studies prioritize the use of current datasets, incorporate XAI techniques, and address dataset balancing. By focusing on these critical factors, researchers can improve model transparency, reduce potential biases, and align their findings more closely with the complexities of modern network scenarios. This all-encompassing strategy will greatly increase the dependability and efficiency of AI-driven cybersecurity solutions. While some studies interpret their findings using explainable AI (XAI), they frequently fail to consider the usage of recursive feature elimination (RFE) to iteratively improve accuracy and overall performance [25]. In the work in [40], for instance, machine learning models' categorization judgments on NetFlow feature sets, such as BoT-IoT and ToN-IoT, are interpreted using SHAP. While this approach enhances detection accuracy, it does not incorporate feature elimination for further optimization, which could enhance model performance.