## 4 Findings and Discussion
### Overview of Datasets  
This section gives an overview of the datasets used by the researchers to test their presented techniques' performance.  
1. The VeReMi dataset: This dataset [120] is an ITS-specific dataset that captures malicious messages intended to trigger falsification, and hence serves as a model for evaluating falsification detection models in an IoV network. The dataset consists of vehicle onboard message logs, including a ground truth labeled, generated from the simulation environment of the Luxembourg City Vehicle Network. The Luxembourg City Network is a smart city that greatly represents an ecosystem enabled by interaction among technologies such as IoT, AI, open data platforms, autonomous cars, smart lights, and wearable devices. Using LuST and VEINS, the simulated network generated GPS data on local vehicles, as well as sets of BSMs, received from other vehicles through DSRC. The classification process was carried out using _Maat_ to execute multiple parallel detectors. The initial dataset contains a number of simple attacks with 614,940 observations and 17 features. A detailed description of the dataset can be found in [120].
2. The BurST-ADMA: The BurST-ADMA dataset [121] was released in March 2022. The BurST-ADMA is an Australian motorway dataset made up of onboard vehicle message logs generated on a Burwood road map that connects Melbourne's city to the suburbs. Vehicle trajectory information was extracted from the Simulation of Urban Mobility (SUMO) road traffic simulation framework and false data were injected into the retrieved trajectory information for a total duration of 1000 s. The BurST-ADMA dataset contains 207,315 observations with 179,126 normal BSM data and 28,189 false data. Seven (7) different falsifications are labeled on the BSMs and one normal vehicle data type. Each of these data points contains the time-step, the vehicle's ID, its X and Y location coordinates, and differential information such as heading, speed, acceleration, and labels [121].
3. V2X falsification dataset: The scenario created here mimics a malicious program or a malfunctioning sensor by injecting false data into the vehicle's positional and speed readings. To model the transmission of CAM in the vehicle network, two of the most well-known simulators SUMO and Network Simulator-3 (NS-3) were used on a VSimRTI platform, a Java-based platform that can seemingly couple the operations of both simulators. The falsification attack is divided into attacks with several parameters: speed, acceleration, and heading. Hence, four (4) different scenarios were explored where vehicles broadcast messages using different strategies and service platforms capturing all simulated false data injection scenarios [126].
4. Car-hacking dataset: The car-hacking dataset [127] was used in conjunction with traditional IDS datasets by the authors in [118] to evaluate the performance of using rule extraction methods from deep learning neural networks to implement a two-stage IDS for ITS. On the other hand, the authors in [117] used the same dataset to validate their proposed enhanced multi-stage deep learning framework for detecting malicious activities from autonomous vehicles. The dataset comprised DoS, fuzzy, and RPM/GEAR attacks, respectively, as detailed in [127].