## 2 Literature Review
Finally, the proposed model obtains an accuracy of around 99%, which demonstrates its effectiveness, shows an accuracy of 92% without the use of Federated Learning (FL), and outperforms other models in real-time identification of forged Basic Safety Messages (BSM).An IDS based on DBN and RF was presented and the results on datasets showed the effectiveness of the proposed solution. The IDS was tested using the CICIDS2017 and NSL-KDD datasets, for which RF with 30 and 24 features was chosen, respectively. Inketing incorporates over-the-air updates of attack signatures and attains high detection ratios for internal and external communications of vehicles without the use of Federated Learning (FL) [34; 35]. Exploitation of several instances of machine learning models with FL for protection of the IIoT environments has been explained. The study uses the geolocation X-IIoTID dataset, which includes 68 features; the model is reduced to the use of 41 of them. In this way, the FL approach proves how effective FL is in boosting the security of data belonging to the IIoT devices as the learning process is distributed across various nodes, and privacy is still upheld [36]. This paper describes a framework that will be used in identifying jamming attacks using the Gradient boosting model coupled with federated learning. By using the custom-collected dataset in the study, the features with the top k importance scores can be chosen. With the help of FL, privacy is maintained in addition to the collaborative analysis of the traffic in the nodes, which leads to the enhanced and accurate detection of jamming attacks in traffic [37]. A two-phase Deep Learning Model for Anomaly Detection in IoT Network Traffic using the IoT-23 dataset was presented. The model solely concentrates on the construction of an efficient AD with no FL and includes the inclusion of crucial features in the algorithm to improve the recognition of IoT cyberattacks [38]. A present approach adopted CNN and LSTM networks as the basis for the hybrid IoT traffic anomalousness model. The model uses the IoT-23 dataset and incorporates spatial and temporal characteristics to detect various types of attacks, and in certain scenarios, it applies FL and increases the detection rates compared to a model without FL [39]. Interfering with vehicular networks can be very disruptive, as it may disrupt the reception of safety messages or may consume the available amount of bandwidth, destroying people's lives. The absence of proper security facilities to protect these vehicles might lead to serious shocks in the cities, and, for example, stopping a part of cars in heavy traffic may lead to total confusion. The espionage, injection, bus-off, and DoS attacks all target the failure of the Engine Control Unit ECU. These threats have been studied by various researchers, and solutions for security have been proposed to address these threats [40; 41]. In this case, intrusion detection systems (IDS) come in handy as they help in monitoring and hiding malevolent acts within vehicular networks. There are three types of IDS-subsumed network-based (NIDS), host-based (HIDS), and combined. NIDS continues tracking the traffic of the telecommunication network about the equipment as HIDS monitors the equipment for any deviations; and last, hybrid is the junction of the first two. Likewise, parametric machine learning systems, as well as deep networks, are utilized in enhancing the IDS via learning features from the datasets or imitating the real strings of the network against which the data are fitting [42; 43]. Some researchers have incorporated the usage of ML and DL methods to protect vehicular networks. For example, an IDS for connected vehicles in smart cities employs DBN- DT to distinguish and categorize anomalies, as shown in [44]. Datasets for vehicular networks have been created to implement DoS and fabrication attacks and hierarchical IDS based on ML algorithms to classify malicious behaviors [45]. The utilization of 5G technology with vehicular networks is still at the stage of maturity.