# A Comparative Analysis of the TDCGAN Model for Data Balancing and Intrusion Detection
## 5 Conclusions  
To demonstrate the impact of data balancing on enhancing classification accuracy and improving machine learning performance, this research conducted a comparative analysis of four machine learning models: Decision Tree, Random Forest, Multi-Layer Perceptron (MLP), and Naive Bayes, across various datasets. The primary objective of the study was to assess the efficacy of integrating the TDCGAN model for data balancing task. Utilizing diverse benchmark IDS datasets, the study implemented the TDCGAN model to address class imbalance. Subsequently, the machine learning models were utilized to classify the data both before and after applying data balancing techniques, aiming to assess the resulting effects on classification performance. Before data balancing, the performance of various machine learning models, including decision tree, random forest, multilayer perceptron (MLP), and Naive Bayes, exhibited inconsistencies across datasets. Notably, metrics such as recall and F1-score displayed discrepancies, indicating challenges in accurately identifying positive class instances. However, after employing the TDCGAN model for data balancing, substantial improvements were observed across all models and datasets. The augmentation of the dataset with synthetic instances generated by TDCGAN led to notable enhancements in accuracy, precision, recall, and F1-score. For instance, in the CIC-IDS2017 dataset, recall surged from 0.86 to 0.90 for the decision tree model, showcasing a significant improvement in positive class identification. Similar enhancements were observed across other models, highlighting the effectiveness of TDCGAN in mitigating class imbalance and enhancing overall classification performance. The consistent improvements seen in the performance of machine learning models post-data balancing strongly indicate the efficacy of the TDCGAN approach. By generating synthetic instances of the minority class while preserving the underlying data distribution, TDCGAN effectively addressed class imbalance issues present in the datasets. This facilitated a more balanced representation of both classes, enabling the models to learn more effectively and make fairer and more accurate predictions. The substantial enhancements across metrics such as precision, recall, and F1-score underscore the significant role played by TDCGAN in refining the classification capabilities of the machine learning models. Thus, the study highlights the importance of employing advanced data balancing techniques like TDCGAN to improve the performance of machine learning models in scenarios with imbalanced datasets. In summary, while the integration of TDCGAN has demonstrated significant improvements in addressing class imbalance and enhancing classification performance, it also introduces certain challenges. The addition of TDCGAN increases the complexity of the model development process, requiring careful tuning of additional hyperparameters, which demands extensive experimentation and expertise. Furthermore, the implementation of TDCGAN adds computational overhead, increasing the time and resources needed for generating synthetic instances and integrating them into the training data. These factors highlight the need for a balanced approach, where the benefits of using TDCGAN are carefully weighed against the added complexity and resource requirements  
Conceptualization, O.S., M.A. and M.J.; methodology, O.S., M.A. and M.J.; software, M.J.; validation, O.S., M.A., M.J. and A.M.M. ; formal analysis, O.S., M.A., M.J. and A.M.M. ; investigation, O.S. ; resources, M.J.; data curation, M.J.; writing--original draft preparation, O.S. and M.A. ; writing--review and editing, O.S. and M.A. ; visualization, O.S., M.A. and M.J.; supervision, A.M.M. ; project administration, O.S., M.A. and M.J.; funding acquisition, M.J. and A.M.M. All authors have read and agreed to the published version of the manuscript. This research was funded by the Deanship of Scientific Research at AlQuds University.