## 5 Discussion
### Limitations and Future Directions  
The limitations and the challenges that this study encountered lie with datasets that are used for the analysis: CICIDS2017 and a specialized \"Store-and-forward\" dataset. Because of the restricted diversity of the datasets, it is possible that the findings cannot be generalized to the same extent as they could have been otherwise. This could have an effect on the implications of the findings. Furthermore, there is lack of support from other research regarding the size of the individualized \"Store-and-forward\" dataset. The performance of learning models can vary depending on the size of the dataset, as well as prior utilization. The comparison analysis does not take into account baseline models or traditional intrusion detection algorithms, which would have allowed for a more comprehensive assessment. When trying to understand how effective the proposed CNN-LSTM model is, including baseline comparisons can provide a framework for doing so better. Some of the future directions based on this study's limitations might lie with exploring larger and more diverse datasets. It is recommended that future studies take into consideration the possibility of evaluating the CNN-LSTM model on a larger number of datasets that come from a variety of network environments and attack scenarios. The resilience of the model and its ability to generalize to real-world settings can be evaluated with the assistance of larger datasets. Furthermore, it is critical to comprehend the decision-making process of the CNN-LSTM model; hence, conducting research on model interpretability methodologies is an absolute must. This will provide insights into the features that influence model predictions as well as increase the reliability of the intrusion detection system. Finally, future research can consider exploring ensemble methods and hybrid approaches that integrate a variety of intrusion detection algorithms in an attempt to increase detection accuracy and robustness. The benefits of multiple methods can be consolidated into a single model through the utilization of ensemble modeling. The issues faced by intrusion detection systems (IDSs) when scanning packet headers occur inside dynamic scenarios, particularly in the context of store-and-forward operations. In the present scenario, there exists a constraint pertaining to intrusion detection systems (IDSs), specifically in the context of contemporary network settings. These environments involve the traversal of packets across diverse network devices and components, each playing a role in the overall communication process. The aforementioned components encompass routers, switches, firewalls, and various other network appliances [56]. During the process of transmission, a packet has the potential to undergo multiple intermediary operations, one of which is known as the store-and-forward operation. Another primary difficulty encountered by intrusion detection systems (IDSs) arises when these systems are tasked with scanning packet headers to identify indications of intrusion or malicious behavior. In such scenarios, IDSs are designed to efficiently and consistently process packets within a given timeframe [57]. In the context of a dynamic store-and-forward operation, several issues emerge, such as the occurrence of \"Packet Fragmentation\". This phenomenon occurs when packets are divided into smaller parts during the store-and-forward process, primarily due to constraints imposed by buffers or network congestion. The fragmentation of packets poses a challenge to the intrusion detection system (IDS) in effectively analyzing the packet header as a cohesive unit, which may result in inadequate or erroneous analysis. The timing of intrusion detection can be influenced by the store-and-forward delay in a similar manner.