## 5 Discussion
### Interpretation of the Findings Associated with the Store-and-Forward Dataset  
The objective of this study was to create a detection system based on modifications using a combined CNN + LSTM model. This system was developed and tested on a store-and-forward dataset obtained from a network that was specifically constructed for experimental purposes. The primary aim of this task was to effectively detect and classify alterations in network traffic, including intrusions and anomalies. The focus was on assessing the model's performance in accurately identifying these modifications within the context of binary classification. After conducting the experiments and analyzing the obtained results, it was observed that the CNN + LSTM model exhibited a notably high modification detection accuracy. Specifically, the model achieved an accuracy rate of 95%. This finding suggests that the model effectively detects instances of network traffic modifications with a high degree of accuracy. In order to obtain additional insights, this research conducted an analysis of the confusion matrix. The findings indicated an equitable distribution of accurate positive and negative classifications, indicating the model's efficacy in accurately categorizing both unaltered and altered network traffic. Additionally, a minimal occurrence of both false positives and false negatives was observed, suggesting that the model exhibits a strong capacity to differentiate between the two classes. In order to conduct a more comprehensive evaluation of the model's performance, this research generated a receiver operating characteristic (ROC) curve and computed the area under the curve (AUC-ROC) score. The plotted curve exhibited a gradual and consistent rise in the rate of correctly identified positive instances, accompanied by a relatively low rate of incorrectly identified positive instances. This indicates that the model possesses exceptional discriminatory abilities. Moreover, the AUC-ROC score of 0.92 provides additional evidence that the model possesses the capability to effectively differentiate between regular and altered network traffic. Additionally, a precision-recall analysis was conducted in response to the presence of class imbalance within this research dataset. The precision-recall curve exhibited a consistent rise in precision as the recall rate declined, a characteristic frequently observed in imbalanced datasets. Notwithstanding this challenge, this research model has attained a substantial precision value, suggesting that the identified modifications are highly probable to be genuine rather than erroneous alerts. After conducting a thorough analysis of the obtained results, this research assert with confidence that this research modification detection system, which utilizes a combination of a convolutional neural network (CNN) and long short-term memory (LSTM), has exhibited encouraging performance. The high level of accuracy, well-balanced confusion matrix, and robust AUC-ROC score of the model indicate its potential for practical implementation in the field of network intrusion detection. Nevertheless, it is important to acknowledge the presence of certain limitations within this study. The dataset was obtained from a network specifically designed for experimental objectives, potentially lacking a comprehensive representation of the intricacies and difficulties encountered in real-world networks. Hence, it is advisable to conduct additional assessment of the model's performance on varied and extensive datasets in order to verify its capacity for generalization. Furthermore, it is suggested that future investigations could delve into the refinement of the model in order to effectively handle various forms of alterations. Additionally, it is recommended that the incorporation of explainability techniques be considered to enhance the comprehension of the decision-making process employed by the model. Finally, the results of this study suggest that the CNN + LSTM model exhibits potential for the detection of modifications.