## 5 Discussion
### Interpretation of the Findings Associated with the CICIDS2017 Dataset  
This research involved the implementation of a thorough investigation into the detection of modifications using a convolutional neural network (CNN) combined with a long short-term memory (LSTM) model. This model was trained on the CICIDS2017 dataset, which is widely recognized as a benchmark dataset for network modification detection. The primary objective of the research is to evaluate the efficacy of the model in accurately detecting alterations and irregularities within the \"Flow duration\" and \"Forward packet\" in network traffic. Following the training results, the model utilizing a CNN combined with LSTM has demonstrated an exceptional detection accuracy of 98% when applied to the CICIDS2017 dataset. The model's ability to accurately distinguish between normal and modified network traffic instances demonstrates its successful learning process and high precision. In order to obtain a more comprehensive understanding of the model's performance, a more detailed examination of the confusion matrix was conducted. The findings illustrate an equitable distribution of accurate positive and negative classifications, suggesting that the research proposed model is proficient in differentiating between genuine network activity and instances of alterations or unauthorized access. In addition, the model's capacity to generate precise predictions is reinforced by the minimal occurrence of false positives and false negatives. In order to assess the model's ability to distinguish between different classification thresholds, this research generated a graphical representation known as the receiver operating characteristic (ROC) curve. Additionally, this research computed the area under the ROC Curve (AUC-ROC) score as a quantitative measure of the model's discriminative performance. The receiver operating characteristic (ROC) curve exhibited a continuous and ascending pattern, characterized by a notable true positive rate (sensitivity) and a comparatively low false positive rate (1-specificity). This implies that the model this research has developed has the capability to maintain a significantly high true positive rate while simultaneously minimizing the false positive rate. As a result, it can be considered highly reliable for the purpose of modification-based detection. The AUC-ROC score of 0.96 highlights the model's outstanding performance, as values approaching 1 signify a higher level of discriminative ability. In addition, a precision-recall analysis was performed, considering the class imbalance observed in the CICIDS2017 dataset. The precision-recall curve demonstrates that the research's proposed model exhibited a notable level of precision despite a decline in the recall rate. The aforementioned attribute holds significant importance within the realm of modification detection, given that erroneous positive outcomes may necessitate expensive investigations, whereas erroneous negative outcomes could potentially allow network intrusions to go unnoticed. The high precision value underscores the model's ability to accurately detect alterations while maintaining a low rate of false positives. Furthermore, the study provides evidence that the CNN + LSTM model, which was trained using the CICIDS2017 dataset, exhibits a high level of efficacy in the detection of modifications. The model exhibits exceptional accuracy, a well-balanced confusion matrix, a high AUC-ROC score, and robust precision-recall performance, suggesting its viability for practical implementation in network security applications within real-world settings. However, it is important to acknowledge certain limitations of this study. Although the CICIDS2017 dataset serves as a valuable benchmark, it is important to acknowledge that real-world networks may introduce additional complexities and challenges that could potentially influence the performance of the model.