## 5 Discussion
### Comparison of the Research Findings
The CNN-LSTM model that was used in the most recent study (This Study) looks to be a potential choice for intrusion detection on the CICIDS2017 dataset due to the excellent accuracy and precision it achieved, as well as the small false positive rate it produced. To make a judgment that is more thorough and well-informed, however, additional study should take into account other criteria outside only computing efficiency, such as the model's interpretability and its ability to generalize to a variety of datasets and network contexts. To begin, when taking into consideration the \"CICIDS2017\" dataset, the CNN-LSTM model that was utilized in \"This Study\" attained an accuracy of 97.93%, which is only a little bit lower than the highest-performing CNN-LSTM model (99.7% from Study [50]). The CNN-LSTM model used in \"This Study\" has a precision of 97.46%, which is comparable to the precision of other CNN-LSTM models (which can range anywhere from 97.6% to 99.6%). The CNN-LSTM model used in \"This Study\" has an FPR of zero, which indicates that it did not produce any false positive predictions. As a result, it is an extremely trustworthy tool for intrusion detection. The CNN-LSTM model that was used in \"This Study\" achieved an accuracy of 99.63% on the \"Store-and-forward\" dataset. This is quite close to the top-performing CNN-LSTM model (99.7% from Study [50]), which was attained using the model that was used in \"This Study\". The CNN-LSTM model used in \"This Study\" achieved a precision of 99.92% on the \"Store-and-forward\" dataset, suggesting a high level of confidence in the model's ability to make accurate positive predictions. The CNN-LSTM model used in \"This Study\" on the \"Store-and-forward\" dataset has an FPR of zero, which indicates that there are no false positive predictions being made by the model. The hybrid detection technique can be applied to identify variations in IP packet headers in a real-world network environment. This can be detected via examining the implementation of a \"Firewall\" and \"Router\" that facilitate connectivity between multiple subnets. One instance of a tangible occurrence can be witnessed in the application of the \"Ping Tunnel\" technique within the realm of computer networks. Adversaries with the power to modify the packet headers of the Internet Control Message Protocol (ICMP) are able to transit hidden data payloads between two compromised systems. Adversaries possess the capability to covertly transmit data through the manipulation of ICMP echo request and echo reply packet headers, thereby circumventing standard security measures and evading discovery. In the provided scenario, it is necessary for the detection system to analyze ICMP packets in order to discover anomalous patterns inside the packet headers, such as unexpected modifications in fields like TTL or checksums. A detection system that has undergone precise calibration possesses the ability to identify modified packets as evidence of a covert communication channel. During the store-and-forward operation, the detection process is mapped onto the \"Firewall\" and \"Router\" components. It is crucial to recognize that security techniques and threats are subject to continuous modification. Hence, in store-and-forward situation, it is imperative to stay updated with the values arrived during \"store\" and the values the is transmitted during \"forward\" in order to ensure the effectiveness of a detection system.