## 2 Related Works
The model maintained a lightweight nature, with faster training times and smaller model sizes than traditional techniques like SVM or Random Forest. Limitations include reliance on a single dataset and the increased model count, which may slightly affect resource overhead in constrained IoT environments. GAOR, a Genetic Algorithm-based optimization technique to enhance the robustness of machine learning models against adversarial attacks in communication networks, was proposed in [28]. The conceptual framework integrates adversarial sample generation using genetic algorithms with tree-based models like Random Forest (RF) and XGBost (v1.7.5, Python). The CIC-IDS2019 and 5G-NIDD datasets were employed, covering both legacy and 5G-specific traffic scenarios. GA was used for hyperparameter optimization and adversarial sample generation, aiming to improve model resilience. Results showed that GAOR models maintained high accuracy (up to 99.94%) and improved robustness, though performance varied against advanced adversarial attacks like ZooAttack with LightGBM (v3.3.5, Python). Limitations include computational overhead from GA opera tions and sensitivity to noisy or imbalanced data. Challenges involve balancing accuracy and robustness, and adapting to increasingly sophisticated attack vectors in evolving communication environments. A Binary Chimp Optimization Algorithm with Machine Learning-based Intrusion Detection (BCOA-MLID) for securing IoT-assisted Wireless Sensor Networks (WSNs) was proposed in [29]. The conceptual framework combines four stages: data normalization, feature selection using BCOA, intrusion detection using a Class-specific Cost Regulation Extreme Learning Machine (CCR-ELM), and parameter optimization via a Sine Cosine Algorithm (SCA). The WSN-DS dataset was utilized, comprising 374,661 samples across five attack categories. Results showed that BCOA-MLID achieved superior accuracy (99.63%), sensitivity (97.91%), specificity (99.67%), and faster computation times compared to models like XGBoost and KNN-AOA. Limitations include dependency on supervised learning and limited attack categories in the dataset. Challenges involve handling dynamic traffic patterns, addressing data imbalance, and extending detection coverage to diverse IoT-based cyberattacks beyond the tested scenarios. The reviewed studies collectively demonstrate significant progress in the field of intrusion detection using a variety of machine learning and optimization techniques. These approaches have shown promising results across diverse datasets and network environments, particularly in addressing challenges like class imbalance, real-time detection, and high-dimensional data. However, limitations such as dependency on labeled datasets, lack of adaptability to novel threats, and scalability constraints in dynamic environments persist. Table 1 summarizes recent ELM-based intrusion detection techniques from existing studies, highlighting these advances and their respective limitations. These gaps underscore the need for more robust, flexible, and intelligent IDS frameworks. Motivated by these findings, the present study proposes a two-stage anomaly detection mechanism combining dimensionality reduction and classification techniques, aiming to enhance detection performance while addressing the aforementioned limitations.