## 2 Related Work
This was achieved through conducting experiments on a commonly utilized intrusion detection system (IDS) dataset. Additionally, the authors demonstrated the possible influence of hyperparameters on the final outcome of the classification. The influence of a little adjustment in hyperparameter setting on the accuracy of a particular neural network architecture is illustrated by theauthors through the use of two distinct intrusion detection systems (IDSs) as case studies. The best design attains 99.909% accuracy in the task of multi-class categorization. The reason for this is that unsupervised approaches are specifically designed to detect anomalies within the conventional CAN traffic pattern [27]. The process of creating authentic assault datasets from real autos in the context of in-vehicle networks presents difficulties because to the significant costs associated with it and the necessity to prioritize safety concerns [28]. However, the only requirement for unsupervised learning methods is the collection of data from a vehicle during its normal functioning. The aforementioned data can be easily acquired from a vehicle that is functioning in a standard manner. A considerable proportion of the endeavors focused on detecting intrusions in controller area networks involves the application of deep learning methodologies [29]. Some studies focus on certain components of AIDs, such as temporal factors or payload size, while other study combines these characteristics in a detection model to enhance the identification of a wider range of attacks. The review of prior research papers on algorithms for detecting IP packet header change demonstrates a wide array of approaches. The efficacy of rule-based systems in detecting established patterns of header modifications linked to store-and-forward operations has been demonstrated. The utilization of machine learning techniques, such as anomaly detection, has been extensively investigated in order to identify minor alterations that are not encompassed by pre-established rules. Moreover, the integration of rule-based and machine learning models in hybrid techniques shows potential in attaining a well-rounded detection capability. It is crucial to note that the aforementioned studies [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29] continue to prioritize the study of intrusion detection systems (IDSs). However, there has been limited discussion regarding the practical implementation of at-a-point detection in various applications. Therefore, building upon prior effective implementations of intrusion detection systems (IDSs) [4; 5; 6; 7; 8] in the field of network security and inspired by the research efforts of [7; 19; 22], this study introduces methodologies for investigating the application of detection in store-and-forward operations, specifically in the context of transmission sessions. The research of previously published scholarly works on techniques for detecting IP packet header changes demonstrates a wide array of approaches. The usefulness of rule-based systems in identifying known patterns of header alterations associated with store-and-forward operations has been established. The utilization of machine learning techniques, such as anomaly detection, has been extensively investigated in order to identify tiny alterations that are not accounted for using pre-established criteria. Furthermore, the integration of rule-based and machine learning models in hybrid techniques shows potential in attaining a well-rounded detection capability. The research gap established dwells on the fact that while there have already been significant breakthroughs in the detection algorithms for IP packet header modification within IDS, there is still a discernible research gap in adequately addressing the dynamic and developing characteristics of header alterations associated with store-and-forward processes. Current methodologies frequently encounter difficulties in accommodating novel attack strategies that leverage non-traditional modifications.