## 4 Limitations  
It is important to recognize and critically examine several shortcomings, even if our study on a hybrid intrusion model employing cluster-based classification for novel attack detection in the IoT is thorough and encouraging. The dataset used in this is the IoTCIC2023 dataset, and its characteristics and biases might have affected the study results. Due to differences in the network topology and attack strategy, the results may not apply to many IoT settings. However, for all datasets and for any attack situation, feature selection via SelectKBest with a fixed value of k = 10 may not be the best option. In some cases, model performance may vary due based on sensitivity to the considered features. One limitation of k-means and other clustering algorithms is that they work best within a limited set of situations involving data within a reasonable dimensionality and clusters with fairly uniform density. The types of assault in the dataset and their distribution have effects on clustering. In the case of the classification of eight classes, this study considered eight predetermined types of assault. While this does not lead to new kinds of attacks in the IoT threat environment, new forms of attacks may appear in the future due to the ever-changing nature of this threat environment. The proposed technique performs well with eight classes mapped to two clusters. However, one potential disadvantage of the model is how well it adjusts to new and potential assault patterns. Frequently used assessment measures like F1 score, precision, recall, and accuracy may not sufficiently match the model's performance in real-world circumstances. It should include other elements, like false positives/negatives and the resistance of the model to adversarial assaults. Due to differences in datasets, parameter setup, and algorithmic choice, the suggested CBC technique presents some difficulties when compared to current state-of-the-art techniques. Thus, generalizations regarding the superiority of the suggested method are impossible. Performance drops compared to the two-class scenario, since the eight-class classification job has more complexity and areas of possible compatibility in attack categories. It may be possible to improve the model's performance if a better understanding of and support for multi-class categorization in an Internet of Things environment can be achieved. The consequences of the suggested model concerning security and deployment in the actual world should be thoroughly investigated. It is important to test the model regarding its response to malicious assaults and its ability to accurately detect attacks in changing IoT environments. The model's scalability should be tested, especially for large-scale Internet of Things (IoT) installations. However, the applicability of such a model to Internet of Things \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Benchmark Scheme** & **Dataset** & **Accuracy** & **Precision** & **Recall Detection Rate** & **F1 Score** \\ \hline Multiple Kernel Clustering [1] & NSL-KDD & 93.80\% & 81.65\% & 89\% & 85.1\% \\  & AWID & 95.60\% & 88.24\% & 90\% & 89.11\% \\  & CICIIoT2023 & 89\% & 86\% & 89\% & 87\% \\ \hline Deep Clustering-based CNN [2] & NSL-KDD & 98.71\% & - & - & - \\  & CICIIoT2023 & 98.5\% & 97.9\% & 97.9\% & 98.0\% \\ \hline Clustering and Classification [3] & UNSW-NB15 & 97.59\% & 97.6\% & 97.6\% & 97.6\% \\  & CICIIoT2023 & 89.5\% & 87.5\% & 88.5\% & 88\% \\ \hline
8-Class Classification Mapped to 2 Clusters & CICIIoT2023 & 88.24\% & 56.30\% & 41.26\% & 44.27\% \\ Proposed approach based on KNN & \multirow{2}{*}{CICIIoT2023} & \multirow{2}{*}{95.11\%} & \multirow{2}{*}{76.33\%} & \multirow{2}{*}{87.76\%} & \multirow{2}{*}{78.78\%} \\ \cline{1-1} \cline{5-5}
2-Class Classification Mapped to 8 Clusters & & & & \\ Proposed approach based on RF & \multirow{2}{*}{UNSW-NB15} & \multirow{2}{*}{93.57\%} & \multirow{2}{*}{90.19\%} & \multirow{2}{*}{92\%} & \multirow{2}{*}{91.09\%} \\  & NSL-KDD & & & & \\ \hline \hline \end{tabular}
\end{table}