## 6 Conclusions  
When using multi-class classification, it is essential to prioritize classes according to the relevance of each class. Less significant classes should be classified in their current state, whereas classes that are more important should be classified with greater precision. By refusing to examine non-significant classes, we are able to focus on prioritized, highly important classes. In this research, we propose a method to improve performance regarding the important classes that have been prioritized when utilizing a multi-class dataset in the development of a suitable ML model. We propose an adaptive incremental learning approach to address multi-class data classification challenges, utilizing a completely original multi-class dataset. We propose using the score function \(scr(x)\) for the comparison of individual ML performance metrics (criteria) and the construction of the judgment matrices. An adaptive method based on an AHP judgment matrix correctly classifies the key classes prioritized in building an ECOC code matrix. The important contributions of this research include adaptive incremental learning, which allows ML models to learn new information continuously by analyzing changing data from a data stream, and the AHP, which helps users make the right decision based on model performance evaluation according to prioritized classes. Using AHP-based adaptive \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research** & **Dataset** & **Proposed Approach** & **Dataset Balance Method** & **Feature Ranking** & **Improve Performance for Prioritized Classes** \\ \hline \multirow{3}{*}{[PERSON] et al. [39]} & \multirow{3}{*}{TON\_IoT} & Transformer-based IoT intrusion detection method & Label smoothing regularization to add fuzzy noise to the training sample labels & The deep global feature extraction capability of a stacked encoder & No \\ \hline \multirow{3}{*}{[PERSON] et al. [40]} & \multirow{3}{*}{TON\_IoT} & \multirow{3}{*}{RF-based intrusion detection} & SMOTE and cluster-based undersampling & Feature elimination using multi-collinearity-based generation of complex features & No \\ \cline{1-1} \cline{3-6}  & & Merging five multi-class supervised ML models: RF, DT, ET, XgBoost and K-Nearest Neighbor & \multirow{3}{*}{SMOTE} & Mutual Information, Pearson Correlation Coefficient, and K-Best & No \\ \hline \multirow{3}{*}{Proposed} & \multirow{3}{*}{TON\_IoT} & Incremental learning, AHP & Original dataset was used & The original dataset was used & Yes \\ \cline{1-1} \cline{3-6}  & & ECOC & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results compared to state-of-the-art research. incremental learning improved the recall metric results for the most important classes in the suggested method. Recall value of the \"injection\" class increased from 59.5% to 61.8%, the recall of the \"password\" class increased from 86.7% to 88.6%, and recall of the \"ransomware\" class increased from 0 to 23.6%. Concerns have been raised about the interpretability of complicated machine learning models, which can often make it challenging for businesses to comprehend and explain the process by which security choices are made. In light of this, we will consider the possibility of developing an explainable framework for applications related to CTI and Fintech in the future. Conceptualization, A.V., J.T. and N.M.; methodology, A.V., J.T. and N.M.; software, N.M.; validation, J.T. and N.M.; formal analysis and investigation, A.V., J.T. and N.M.; data curation, N.M.; writing--original draft preparation, A.V., J.T. and N.M.; writing--review and editing, A.V., J.T. and N.M.; visualization, J.T. and N.M.; supervision, A.V. All authors have read and agreed to the published version of the manuscript. This work was conducted as part of the execution of the project \"Mission-driven Implementation of Science and Innovation Programs\" (No. 02-002-P-0001), funded by the Economic Revitalization and Resilience Enhancement Plan \"New Generation Lithuania\". Not applicable. No new data were created.