## 2 Related Works
Limitations involve challenges related to generalization, scalability, and class imbalance. A network IDS that utilizes Stochastic Gradient Descent (SGD) and Gaussian Naive Bayes (GNB), incorporating PCA and SVD for dimensionality reduction, was introduced in [22]. The WSN-DS dataset, which focuses on DoS attacks, reached an accuracy of 96% and an F1 score of 97%, while the WUSTL EHMS 2020 IoMT dataset recorded 87% accuracy and 93% F1 score. SGD outperformed GNB, enhancing real-time efficiency and minimizing false positives. However, the approach has limitations, including high computational costs and reliance on data quality and feature selection. A hierarchical IDS, utilizing ELM as the main classifier, with Grey Wolf Optimization (GWO), Archimedes Optimization Algorithm (AOA), and Honey Badger Algorithm (HBA) employed for hyperparameter tuning and feature selection, was introduced in [23]. The system achieved an accuracy of 98.93% with a false alarm rate of 0.51% on the UNSW-NB15 dataset, and 99.63% accuracy with a false alarm rate of 0.01% on the CICIDS2017 dataset. Although ELM outperformed traditional classifiers, the use of meta-heuristic optimization introduces additional computational overhead and complexity, particularly in real-time applications. A Smart Attacks Learning Machine Advisor (SALMA) system to protect smart cities from cyber threats was proposed in [24]. The system uses a five-level classification model based on Extreme Learning Machine (ELM) algorithms, classifying anomalies in Software-Defined Networking (SDN) environments. The conceptual framework involves a multilayer architecture for SDN-based smart cities, where flow statistics from SDN controllers feed into ELM classifiers. The NSL-KDD and KDDCUP99 benchmark datasets are employed for training and testing. Experimental results achieved high detection accuracies of 95% and 99.2%, respectively, outperforming several existing IDS models. Limitations include reliance on static datasets, which may not reflect evolving real-time smart city traffic, and the focus on known attack types, which might limit zero-day attack detection. Challenges include scaling the model for large, dynamic smart city infrastructures and integrating it with diverse IoT ecosystems. Recent advancements in ELM-based models have addressed challenges in fault diagnosis under imbalanced data conditions. For example, the research in [25] proposed a novel chiller fault diagnosis framework that integrates an improved GAN with an enhanced Deep ELM, effectively mitigating the effects of data imbalance and improving diagnostic accuracy. Similarly, in another study, the authors introduced a new resampling technique combined with an improved ensemble ELM to enhance fault detection in imbalanced chiller data scenarios [26]. These studies demonstrate the adaptability and robustness of ELM in handling real-world, skewed datasets. Although these studies effectively handle imbalanced data in fault diagnosis scenarios using advanced ELM-based architecture, the present work leverages a Hybrid Autoencoder-ELM framework for intrusion detection, emphasizing efficient feature reduction and classification without requiring extensive data resampling or synthetic generation, thus maintaining the integrity of the original traffic data. An efficient intrusion detection mechanism tailored for IoT environments using paired autoencoders combined with ELM was introduced in [27]. The proposed conceptual framework involves a two-layer detection system: first, data are partitioned into regions (quasi-normal, quasi-attack, divergence, and undetermined) based on predictions from multiple autoencoders trained separately on normal and various attack data. In the second layer, optimal autoencoders are selected for precise detection within each region. The NSL-KDD dataset was employed for experimentation. Results demonstrated improved overall accuracy (94.89%) and F1-Score (95.23%), outperforming baseline models. Notably, accuracy in the complex divergence region improved by 20.68%.