## 2 Related Work
This approach maximizes accuracy and conserves processing power on limited IoT infrastructure. The researchers trained and evaluated logistic regression, decision tree, random forest, and K-nearest neighbor (KNN) classifiers on valid and malicious IoT traffic from 21 classes using IOT2023. Performance improvement began with label encoding and data cleansing. The random forest and decision tree classifiers achieved the highest accuracy of 99% for IoT traffic classification, notably for benign and common harmful behavior. The research shows that choosing the best features enhances model interpretability and performance quality. The 'uid, 'proto', and 'duration' features had the greatest impact among all other variables. Performance tests using metrics like ROC curve, the F1 score, precision, accuracy, and recall indicated good detection capabilities. The above mentioned study shows that ensemble learning and feature engineering are essential for IDS solutions in modern IoT contexts. The proposed methodology allows for deep learning and real-time deployment enhancements. Hybrid machine learning models improve IoT ecosystem safety in the face of rising security threats and provide operational direction for smart-domain IDS developments. Supplying IoT environments with hybrid machine learning systems is associated with challenges such as computational demands that can cause problems in terms of scalability and real-time response capabilities. The Internet of Things (IoT) requires continuous intrusion detection, since network traffic generates large amounts of complex data. According to [18], the PCM-RF hybrid technique selects characteristics using PCM and a random forest classifier to improve IoT intrusion detection system (IDS) performance. Traditional feature selection methods cannot manage high-dimensional, redundant, and irrelevant features that reduce IDS machine learning model performance. PCM removes unnecessary characteristics before entering the RF component to improve computational performance in the PCM-RF architecture. After feature ranking, RF evaluates the remaining inputs based on significance scores to select the best inputs for classification models. The system described above performed well compared to conventional methods on the CICIoT2023 dataset, which includes 34 network attacks. XGBoost detected 86% of targets and achieved 99.39% accuracy using selected features. This research shows how well the chosen features perform. The article referenced aboveshows PCM-RF can outperform standard detection methods for MitM, DDoS attacks, and overall attack identification. In restricted IoT networks, PCM filtering and RF ensembling yield high performance, system adaptability, and real-time intrusion detection. Previous research yielded a flexible hybrid feature selection strategy that improves IoT security categorization and the operational functionality of defense technology. Through effective adaptation, the proposed model advances intrusion detection. However, the PCM-RF hybrid feature selection mechanism shows limited adaptability to constantly shifting attack vectors, resulting in the deterioration of its effectiveness when deployed in the long term. The system's performance suffers from resource constraints in IoT devices, affecting its ability to detect intrusions in real time. Hackers use the expanding number of Internet of Things devices to increase their opportunities to launch large cyber assaults that endanger network protection and breach private information. A deep learning-based hybrid IDS was proposed in [19] to detect and combat botnet intrusions in IoT networks. Due to the complexities in IoT traffic patterns, novel detection approaches are necessary, prompting the development of an IDS framework using CNN and LSTM components. The proposed model leverages the strengths of both CNN and LSTM architectures, using a CNN to extract spatial patterns in traffic data and LSTM to track temporal dependencies, allowing for detecting attack sequences across time periods.