## 2 Related Work  
Extensive study has been conducted in the past on the topics of packet inspection and network attacks. The CICIDS2017 dataset has been widely utilized in the bulk of prior research endeavors. The investigations yielded experimental results that showcased high rates of detection and low rates of false positives. Additionally, the performance of the system was shown to be superior in terms of accuracy, detection rate, false alarm rate, and time overhead, as indicated by previous research [16]. The intrusion detection system (IDS) is the primary area of research for detecting attacks. Machine learning approaches are predominantly utilized for achieving successful detection [17]. The utilization of the CICIDS2017 dataset has been prevalent in the bulk of past research studies. The tests yielded experimental results that showcased high rates of detection and low rates of false positives. Additionally, the performance of the tested methods was shown to be superior in terms of accuracy, detection rate, false alarm rate, and time overhead, as indicated by previous research [18]. The implementation of an intrusion detection system for the controller area network (CAN) bus system in automotive applications has been discussed in a previous study [19]. In the domain of identifying distinct attacks, unsupervised techniques exhibit a higher level of effectiveness when compared to supervised methods [20]. [PERSON] et al. [21] introduced an approach for intrusion detection system (IDS) that uses a deep learning-based model to effectively detect and classify malicious network traffic aimed at compromising on-board units (OBUs). This methodology obviates the need for human feature extraction and possesses the additional advantage of being capable of processing unprocessed traffic data. The effectiveness of this approach is assessed via a comparison analysis with alternative intrusion detection system (IDS) methods using both a publically accessible dataset and a generated dataset of a vehicular ad hoc network (VANET). The results of the study indicate that the implemented scheme exhibited exceptional performance while necessitating a reduced allocation of resources. [PERSON] et al. [22] presented a detailed analysis of the procedures employed in the development of an intrusion detection system (IDS) using a deep learning architecture. the primary aim of the proposed system is to ascertain the legality of login attempts conducted on a computer network, effectively differentiating between unauthorized hacking endeavors and allowed actions. Moreover, the authors suggest that additional research should be undertaken, employing a more flexible arrangement of the deep learning framework. Various supervised learning techniques, including support vector machine (SVM), k-nearest neighbor (kNN), random forest, artificial neural networks (ANNs), deep convolutional neural networks (CNNs), and long short-term memory, have been intensively explored in academic research [23]. Given the importance of machine learning in intrusion detection systems (IDSs), it has been observed that the use of supervised learning techniques for training requires a significant amount of labeled data. However, these methods have shown the ability to outperform unsupervised learning techniques in detecting known instances of attacks [24]. This information should contain a diverse range of assault manifestations. [PERSON] et al. [25] conducted a study in which they employed a convolutional neural network (CNN) classifier for the purpose of an intrusion detection system (IDS). A level of accuracy of 99.78% was achieved. In addition, it demonstrates the capacity to discern and detect attacks, a challenge frequently encountered by traditional intrusion detection systems (IDSs). The study conducted by [PERSON] [26] demonstrated the methodology of incorporating several hyperparameters and topology configurations in order to attain the highest level of performance for an artificial neural network (ANN) classifier.