## 2 Related Work  
Recent research on intrusion detection frameworks in the literature demonstrates the enhanced performance of machine learning models. The intrusion detection method uses hardware and software to identify intrusions in networks [11]. The deployment of an embedded system enables the network-level implementation of security regulations. An intrusion detection strategy is divided into network-based and host-based. The online data are used to extract features for classification-based intrusion detection models. Machine learning methods, including supervised and unsupervised models as well as deep learning models, are frequently utilized in intrusion detection systems. Unsupervised learning techniques automatically cluster a huge amount of data without the use of labels. However, little amounts of tagged data can aid in enhancing network security or cybersecurity performance. This method cannot produce high accuracy due to the unique characteristics of unknown assaults. In order to locate clusters based on similarity, an unsupervised learning approach for intrusion detection has been developed [12]. Models of supervised learning that produce positive outcomes require labels for training. Machine learning models have been widely used in conventional intrusion detection techniques. However, due to classifiers' poor generalization capabilities, these models were unable to effectively anticipate various invasion attacks. The hybrid technique developed by the researchers enhanced the intrusion detection system while enhancing the capabilities of machine learning models. A combination of SVM, particle swarm optimization (PSO), and k nearest neighbor algorithms were used by [PERSON] et al. [13]. The effectiveness of the categorization was greatly increased by combining these methods. [PERSON] [14] discovered symbolic sequences and independent attacks from standard system call sequences have significant resemblance. He demonstrated the importance of covering similarity as a measure of an anomaly in host-based detection by comparing and analyzing three similarity metrics. The intrusion detection frameworks face a significant challenge from high-dimensional data in an increasing number of incursions and attacks. An essential data feature has to be studied to reduce dimensions in order to decrease time complexity and resource use. [PERSON] et al. designed anomaly detection using SVM and artificial neural networks (ANN) for fraud detection in the second stage [15]. Similar to this, the authors decreased the dimensionality of the data by using the PCA-LDA ensemble method [16]. One of the efficient techniques for detecting intrusions is clustering. The authors applied an improved density peak algorithm for intrusion detection in [17]. Vehicles' security concerns have been the subject of multiple publications over the past few years. The public key infrastructure built on blockchain is a noteworthy trend in this regard. In order to handle the distribution and maintenance of the Certificate Revocation List (CRL) in vehicle public key infrastructure, the authors employed blockchain technology in [18]. An accountable credential management framework for vehicular communication is presented in another research as a potential solution to these issues [19]. This system takes advantage of transparency log techniques while addressing issues unique to vehicular communication. The authors proposed a framework that checks the authenticity of the IDs and keys on the blockchain, which promises a simple authentication process and lessens computational and communication load to access the network's safety messages [20]. A reputation evaluation method was proposed in [21] that used both direct past encounters, and indirect judgments about automobiles were provided in order to stop the dissemination of fake messages. To identify malicious attacks, deep learning models and hierarchical methods have been developed.