## 6 Discussion and Conclusions  
RBS attacks pose a significant threat to cellular communication networks and subscribers which can have devastating effects in V2X scenarios. This paper describes the simulation of a platoon of autonomous vehicles moving through an area of 5G radio coverage, and periodically calculates the RSS. As can be seen from various attack scenarios, there is potential for a compromised BS to be masqueraded by the RBS, which will assume control of the UE. When such an event occurs, an UE can be captured for tens of seconds. This is sufficient time to steal information or cause a car accident. The simulation model can generate a realistic dataset of radio information and RSS measurements in a reconfigurable scenario with multiple legitimate base stations and a numerous rogues. The dataset is created using radio propagation models and aims to generate measurement reports that exhibit characteristics that would be normally evident in a real drive test. Our experiments show that a dataset that would represent a 20-min drive test can be obtained in only 15 s of simulation. Consequently, this method enables researchers to quickly develop various geographical and radio scenarios and generate MR data in the absence of abnormal radio propagation situations. As the broadcast channels of gNodeB elements in 5G and previous standards must contain unciphered information so that UEs can identify each BS and decide whether to attach, RBS attacks are always possible. However, by exploiting the data provided in the MRs, the network may look for aberrant behaviour to indicate that an RBS is trying to capture the UE. The work to date described in this paper presents the specifications of a tool for generating realistic streams of RSS data. A software platform was developed to simulate signals from legitimate BS units and variable positioned RBSs. The dataset yielded from this stage was approximately 7 h of drive test results passing 30 legitimate BSs. It seems likely that an ML-based solution can yield a robust and adaptive RBS identification mechanism that can be trained on a large set of signal strength measurements covering many different scenarios. The proposed novel flexible data generator creates received signal-level reports based on vehicles platooning scenarios. Users can generate a highway scenario that is hundreds of kilometres long with randomised locations of base station and variable speed of the vehicles. The simulation runs 80 times faster than real time so that we can generate large time series datasets of a UE's measurement reports, which can then be used to design new techniques for detecting RBS attacks. The next step of this work will involve using this dataset to develop high-performance models for the detection of RBS so that they can be rejected before being considered for handover. This will exploit the output from the simulator to design ML methods to identify RBS attacks and protect against them. Conceptualization, all authors; methodology, M.S., A.M.; software, M.S. ; validation, A.M., P.P. ; formal analysis, all authors; investigation and resources, M.S. ; data curation, M.S., A.M.; writing--original draft preparation, M.S. ; writing--review and editing, A.M., P.P. ; visualization, M.S. ; supervision, A.M., P.P. ; project administration, A.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability StatementData generated during this investigation are accessible from the corresponding author upon reasonable request; however, due to the sensitive nature of the data, access is limited to researchers with appropriate institutional affiliations.