## 2 Related Work  
The extensive integration of the Internet of Things (IoT) across diverse applications has appeared as a transformative force, improving the general standard of living. However, due to its vast network of interconnected devices, inherent openness, and decentralized architecture, the IoT has progressively become a significant focus for unauthorized breaches. An extensive study was conducted in [3] using feature selection, classification, and clustering on the UNSW-NB15 dataset to construct an efficient IDS. Due to improved cyber attacks, network security requires an effective IDS. This research tackled existing problems with multidimensional input data, unnecessary elements, and unimportant features when dealing with network datasets because these elements create performance problems in machine learning software. Researchers decreased the number of features through feature selection, improving model accuracy and efficiency. Clustering methods establish natural subgroup relations to extract anomalies from datasets. After that, decision tree and random forest classification methods are used to detect intrusions precisely. Experimental results have revealed better intrusion detection with fewer false alarms using the unified feature selection method, clustering, and classification. Through its integrated approach, this research shows that structured feature reduction and preprocessing strategies improve the effectiveness of machine learning IDSs. However, the exclusive use of data from the UNSW-NB15 dataset prevents generalization to current cyber attacks due to potential unseen threats. The exclusion process during feature selection might remove essential elements for recognizing new and advanced cyber attacks. Additional complexity from clustering and classification algorithms negatively impacts the system execution speed, particularly when running on systems with limited resources. Due to increasing IoT device complexity and vulnerability, a sophisticated security solution must combine anomaly and signature-based detection. A hybrid solution was proposed that uses machine learning techniques to identify malicious acts, improving heterogeneous IoT security. The proposed IDS outperformed single-detection systems in DoS, probing, and Remote-to-Local (R2L) assaults in experimental testing. The hybrid IDS proposed in [4] improves accuracy, false-positive rates, and real-time responsiveness. Since the IoT market is growing rapidly, the abovementioned report underlines how IDSs must handle device expansion and diversity. Extended research can solve significant IoT security issues with an extensible framework that defends against network-based assaults, improving cybersecurity. However, the model's functioning strictly relies on preselected IoT network datasets, restricting its use for different network types and developing attack patterns. The hybrid IDS operating method could create an additional computational burden that might reduce real-time speed when running under resource-limited IoT conditions. Intrusion detection systems (IDSs) for the Internet of Things (IoT) are essential to meet the growing security challenges. In [5], signature-based, specification-based, hybrid IDS, and anomaly-based approaches were analyzed for IoT security capabilities and vulnerabilities. This study showed how IoT systems' limited resources, heterogeneity, and widespread deployment make typical IDS solutions problematic. IDS classifications were categorized by installation type (hybrid, network-based, or host-based), and standard datasets and evaluation metrics were presented. IDS literature has several flaws, including low-weight real-time IDS frameworks and insufficient evaluation procedures. However, such analyses and the prediction of upcoming directions in IoT intrusion detection systems might struggle to anticipate quick evolutions of attack methods, impacting their enduring success.