## 3 Methodology
### Rlfoula Framework Description
#### 3.2.3 Employing Functional Agents in RL
In comparison, AESMOTE primarily employs SMOTE as its re sampling method and does not explicitly incorporate multiple machine learning algorithms or undersampling techniques. Furthermore, RLFOUA evaluates performance using various metrics, including accuracy, precision, recall, and F1 score, on both CSE-CIC-ID2018 and NSL-KDD datasets, while AESMOTE only focuses on the NSL-KDD dataset. Additionally, RLFOUA utilizes a new metric, IM, for comparing algorithms. In contrast, AESMOTE does not specify a new metric used for performance evaluation. Table 5 provides a comprehensive comparative analysis of the performance metrics between RLFOUA and AESMOTE. In the case of the NSL-KDD dataset, RLFOUA's testing set comprises 12,598 independent instances, carefully extracted from the original file. In contrast, AESMOTE is divided into a training set with 125,973 samples and a testing set with 22,544 samples [3]. The table showcases key metrics, including F1 score, accuracy, precision, and recall, for both methods. As illustrated in Table 5, RLFOUA consistently outperforms AESMOTE across all metrics, underscoring its superior performance. This distinction is particularly evident in the significantly higher F1 score, accuracy, and recall values achieved by RLFOUA. In conclusion, this paper introduces RLFOUA, an innovative approach that incorporates the novel TFRSMOTE resampling algorithm. We rigorously validated and tested RLFOUA on multiple datasets, employing a diverse range of machine learning algorithms and undersampling techniques. The performance metrics presented here serve as a testament to the effectiveness of our approach. The substantial differences in performance between AESMOTE and RLFOUA underscore the unique contributions of our work and its potential to advance the field of anomaly detection in intrusion detection systems. Beyond the realm of intrusion detection, the RLFOUA framework, coupled with the innovative TFRSMOTE resampling algorithm, holds significant promise for applications in other domains characterized by imbalanced datasets and the need for robust anomaly detection. Industries ranging from finance to healthcare and beyond stand to benefit from the heightened accuracy and precision demonstrated by RLFOUA. By effectively addressing the challenges posed by class imbalances in data, our approach opens avenues for the development of more reliable and efficient anomaly detection systems across diverse domains. Moreover, the adaptability of RLFOUA, demonstrated through its performance across multiple datasets, suggests its potential for customization to suit specific application scenarios, further expanding its applicability in real-world settings. ## 6 Conclusions

IDSs are essential for identifying and mitigating malicious activities within networks. The application of machine learning algorithms to imbalanced IDS datasets can significantly enhance the performance of existing IDS methods. However, the challenge lies in the imbalance of such datasets, where malicious activities can easily conceal themselves among a large volume of normal events. Consequently, machine learning algorithms may prioritize recognizing normal events, potentially missing critical attacks. Therefore, there is a pressing need for algorithms that can effectively balance IDS datasets, leading to better-trained machine learning models. In this paper, we present RLFOUA, an RL framework specifically designed to address the imbalanced nature of IDS datasets. RLFOUA is enhanced through the integration of a novel resampling algorithm called TFRSMOTE. TFRSMOTE facilitates dataset balance \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **F1 Score** & **Accuracy** & **Precision** & **Recall** \\ \hline RLFOUA & 0.9061 & 0.9981 & 0.9055 & 0.9118 \\ AESMOTE & 0.8243 & 0.8209 & 0.8411 & 0.8209 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative performance metrics of RLFOUA and AESMOTE.