# Federated Learning for V2X Misbehavior Detection System in 5G Edge Networks

Hadi Yakan, Ilhem Fajjari, Nadjib Aitsaadi, Cédric Adjih

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Hadi Yakan, Ilhem Fajjari, Nadjib Aitsaadi, Cédric Adjih. Federated Learning for V2X Misbehavior Detection System in 5G Edge Networks. MSWiM 2023 - Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems, Oct 2023, Montreal Quebec, Canada. pp.155-163, 10.1145/3616388.3617533 . hal-04836305

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# Federated Learning for V2X Misbehavior Detection System in 5G Edge Networks

Hadi Yakan

Paris-Saclay University, UVSQ, DAVID

Versailles, France

hadi.yakan@uvsq.fr

Nadjib Aitsaadi

Paris-Saclay University, UVSQ, DAVID

Versailles, France

nadjib.aitsaadi@uvsq.fr

Ilhem Fajjari

Orange Innovation

Chatillon, France

ilhem.fajjari@orange.fr

Cedric Adjih

Inria Saclay

Palaiseau, France

cedric.adjih@inria.fr

## Abstract

The emergence of 5G Cellular Vehicle-to-Everything (C-V2X) has made it the predominant technology for enabling Vehicle-to-Everything (V2X) communications. As a result, this has created an opportunity for telecommunications service providers to leverage their pre-existing 5G network infrastructure, enabling them to provide Vehicle-to-Network (V2N) services. In this paper, we propose a new approach enhancing the security of 5G V2N services through the implementation of a Federated Learning V2X misbehavior detection system within 5G core network. The proposed system aims to protect V2X application servers (V2X ASs) that are located in 5G edge networks against potential V2X attacks, while leveraging the privacy and scalability advantages of Federated Learning. Our proposed model is compared, using extensive emulations, to other centralized and distributed approaches, achieving excellent results, making it feasible for deployment. Our proposal achieved a notable accuracy of 98.4%, while scoring an impressive 99.3% precision and 96.9% detection rate.

**Keywords:** 5G, V2X, C-ITS, Security, Misbehavior Detection, Deep Learning, Federated Learning, Edge Network

## ACM Reference Format:

Hadi Yakan, Ilhem Fajjari, Nadjib Aitsaadi, and Cedric Adjih. 2023. Federated Learning for V2X Misbehavior Detection System in 5G Edge Networks. In *Proceedings of The 26th International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWIM '23)*. ACM, New York, NY, USA, 9 pages. https://doi.org/XXXXXXX.XXXXXXX

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MSWIM '23, October 30-November 03, 2023, Montreal, Canada

© 2023 Association for Computing Machinery.

ACM ISBN 978-1-4503-XXXX-X/18/06...$15.00

https://doi.org/XXXXXXX.XXXXXXX

## 1 Introduction

The constant evolution of the automotive industry, fueled by the rise of electric/autonomous/connected vehicles, necessitates advances in road safety and communication technologies. As the market adopts these advanced vehicles, ensuring their safe and dependable operation becomes vital. Vehicle-to-Everything (V2X) communication plays a crucial role in enabling Cooperative Intelligent Transport Systems (C-ITS) applications that improve road safety, traffic management, and the driving experience overall.

Maintaining the integrity and security of V2X systems requires detecting and preventing malicious activities that might target V2X-enabled devices, including position falsification, Denial of Service (DoS), sybil and replay attacks. Misbehavior detection is a critical component to protect V2X communications.

Initial proposals to address misbehavior detection in V2X rely on creating simple plausibility checks to detect abnormal behaviors. Later, researchers utilized these plausibility checks along with additional vehicles' movement data to train traditional machine learning models to improve detection results. With the advancement of machine learning and deep learning, new solutions are proposed based on Recurrent Neural Networks (RNNs) due to their ability to retain trajectory information.

With the emergence of Artificial Intelligence (AI) and 5G, the potential for utilizing advanced techniques such as Federated Learning to effectively resolve the scalability challenge has emerged as a promising solution.

In this paper, we propose a distributed misbehavior detection system based on Long Short-Term Memory (LSTM) and Federated Learning, where the nodes of the system are installed in the 5G edge network across the coverage zone, to protect C-ITS application servers hosted on the edge/cloud/internet against V2X attacks.

The scalability of Federated Learning makes it the best solution for detecting V2X misbehavior in large-scale deployments. Due to the anticipated exponential growth in the number of connected vehicles and low-latency requirements
<figure><img src="image_2.png" /><figcaption>**Figure 1.** VeReMi Extension Dataset Classes</figcaption></figure>

of V2X applications, traditional centralized approaches may be incapable of meeting the massive data volume and computational demands. Federated Learning distributes the learning process, enabling edge nodes to contribute their local knowledge and participate in training without overwhelming central servers. This distributed computing paradigm enables efficient and scalable V2X misbehavior detection systems, capable of accommodating the expected growth in vehicle connectivity.

Federated Learning provides a convenient solution for detecting V2X misbehavior in 5G networks. It enables V2X systems to detect and prevent position falsification attacks and other malicious behaviors while protecting the privacy of individual vehicles by leveraging the power of collaborative and privacy-preserving learning. Federated Learning paves the way for robust and effective V2X misbehavior detection systems in the era of 5G-enabled connected vehicles via its adaptability, scalability, and ability to integrate real-time data from diverse sources.

The paper is organized as follows. Section 2 presents the problem statement. In Section 3, we review related work addressing V2X security and misbehavior detection. Section 4 details the architecture of our proposed solution. Then, in Section 5, we describe the proposed Federated Learning model. Section 6 provides the results of offline and online performance evaluations. Finally, in Section 7, we conclude the paper.

## 2 Problem statement

The current V2X security solutions rely heavily on cryptography, the majority of these solutions implement a Public Key

Infrastructure (PKI) system to distribute and validate vehicular certificates. Vehicular certificates are typically smaller in size than regular certificates and are used to authenticate vehicles. While Public Key Infrastructure (PKI) systems are crucial for protecting against external threats, additional measures are necessary to mitigate attacks that could be launched by malicious insiders who have already authenticated and are part of the network. To address this issue, a Misbehavior Detection System (MDS) can be implemented, it acts like an Intrusion Detection/Prevention System (IDS/IPS) in traditional IT networks.

V2V misbehavior detection is a crucial security mechanism for C-ITS. However, most existing techniques focus on verifying the position and speed of vehicles based on V2V messages, and neglect the possibility of malicious behavior in V2N communications. This can pose a serious threat to the safety and efficiency of network-hosted C-ITS services, as misbehaving vehicles may target edge/cloud servers while behaving normally on V2V.

We take into account all the nineteen V2X misbehavior types included in VeReMi-extension dataset [5], which are depicted in Fig. 1. In the figure, the blue dots represent the authentic values that the vehicle would have transmitted if it was benign, whereas the red and other colored dots are the values that were actually transmitted by the attacker, and the gray dots are an aggregation of normal values transmitted by non-malicious vehicles.

The attack types are the following. Attack type 1 (Constant Position): The misbehaving vehicle reports the same fixed
position despite its movement. Attack type 2 (Constant Position Offset): The vehicle adds a predetermined fixed value to its current position, resulting in the generation of a path that is parallel to the true path. Attack type 3 (Random Position): The misbehaving vehicle sends a random position instead of its actual one. Attack type 4 (Random Position Offset): The vehicle adds a random number to its true position, creating a fuzzy path. The next four attacks, namely Attack type 5 (Constant Speed), Attack type 6 (Constant Speed Offset), Attack type 7 (Random Speed), and Attack type 8 (Random Speed Offset), are respectively similar to the first four, the only difference is that the misbehaving vehicle alters its speed value instead of its location. This is also reflected in the four corresponding graphs in Fig. 1 which depict speed values. Attack type 9 (Eventual Stop): The vehicle sends its precise location and speed information at first, but after a certain amount of time, it starts reporting a fake fixed position and zero speed. Attack type 10 (Disruptive): The vehicle replays information previously received from random neighbors to overwhelm the network. Attack type 11 (Data Replay): The attacking vehicle replays the information received from a specific target neighbor as its own. Attack type 12 (Delayed Messages): The attacking vehicle sends its old accurate movement information after a pre-defined period of time. Attack type 13 (DoS): The attack consists of sending accurate information at an increased frequency to flood the network. Attack type 14 (DoS Random): The attack is similar to DoS in terms of message frequency, however, all the values included in the messages are random and not accurate. Attack type 15 (DoS Disruptive): This attack combines the increased sending frequency with the flooding of previously received random neighbors' messages. Attack type 16 (Grid Sybil): The intention of the attacker is to create a fake congestion by creating ghost vehicles, where vehicle pseudo IDs are created for nonexistent vehicles in a specific target position, and the attacker maintains a realistic communication profile of the ghost vehicles. Each pseudo ID is represented with a different color in the corresponding graph in Fig. 1. Attack type 17 (Data Replay Sybil): This attack is a more sophisticated form of Attack type 11. The attacker replays the data of a targeted neighbor using multiple pseudo IDs to masquerade the real attacker's identity. Attack type 18 (DoS Random Sybil): It combines three forms of attacks into one: increased frequency of sent messages, randomness of all values in the messages, and multiple fake pseudo IDs. Attack type 19 (DoS Disruptive Sybil): The attacker replays previously received messages from random neighbors with high frequency using many pseudo IDs.

As 5G enables low-latency C-ITS applications in the Edge networks, it is important to protect them against V2X attacks launched by misbehaving vehicles, which can have significant consequences and might disrupt the operations of critical C-ITS services. Therefore, in this paper we propose

a novel approach to detect and mitigate V2N misbehavior by leveraging LSTM and Federated Learning.

## 3 Related work

Based on the LuST scenario [3], van der Heijden et al. created VeReMi [15], a publicly accessible dataset of V2X attacks. The dataset was generated using the vehicular network simulators SUMO [7] and Veins [13]. The dataset contains the tracks of both normal and misbehaving vehicles, the latter engage in five distinct position falsification attacks in V2V. The dataset was utilized by the authors to compare various plausibility checks. All the papers in this section utilize this dataset or a variation of it.

In [12] So et al. proposed using a machine learning-based method for detecting V2V misbehavior. They developed, using MATLAB, a machine learning version of the VeReMi dataset before proposing a model that makes use of six input features, including two plausibility checks and four numerical measurements of the vehicle's movement. The authors compared the performance of standalone plausibility checks, k-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms. The authors found that accuracy improved by 6 to 7 percent, precision improved by around 20 percent, while maintaining recall within 5 percent margin. Sharma et al. evaluated more machine learning algorithms in [11], using Scikit-learn [10], including Naive Bayes (NB), Random Forest (RF), and Ensemble Boosting and Voting, and found that the RF and Ensemble models outperformed the rest.

In [6] Kim et al., compared KNN, SVM, RF, Extreme Gradient Boosting (XGB), and Multi-Layer Perceptron (MLP) on the VeReMi-ML dataset. They further introduce a new extended set of differential features that allow the checking of mobility constraints and inconsistencies, which, as per the reported results, proved to improve the machine learning models' performance. When the extended features are used, MLP performs best, followed by XGB and RF. The authors also proposed Zero-Day Attack Detection based on Autoencoder architecture. The autoencoder is a Deep Neural Network (DNN) composed of an encoder and a decoder with internal hidden layers. The encoder projects the input data to a lower dimensional space, and the decoder restores the lower dimensional representation to the output in the input dimensional space. The autoencoder is then trained to minimize the reconstruction error while training it using only normal behavior data. After that, the model can then be used to classify normal and abnormal data based on a pre-defined error rate threshold.

In our previous work [16], we introduced a misbehavior detection and prevention system for V2N, integrated with the 5G core network, to protect C-ITS application servers from position falsification attacks and revoke access for misbehaving vehicles. We also proposed a novel plausibility check, which we call the On-Road Plausibility Check (ORPC). It
leverages historical position data to improve detection results.

In [8] Lv et al. proposed a misbehavior detection system for Vehicular Ad Hoc Networks (VANETs) using privacy-preserving federated learning and blockchain technology. Their approach allows vehicles to request receiving a shared training model from a Road-Side Unit (RSU), train this model using its local data, and upload the updated model's parameters without sharing local training data with the RSU. The RSU receives models from multiple vehicles and aggregates them based on accuracy scores to produce a new averaged model, which is stored on the blockchain.

In [5] Kamel et al. created an extended version of the VeReMi dataset. This expansion comprises 19 attack types, including a large set of sophisticated V2X attacks like DoS, Data Replay, and Sybil, at various vehicles densities. The study also uses basic misbehavior detection mechanism based on LSTM and DNN. The results are considered a baseline for further research. Other researchers can use this large dataset to compare and enhance their detection mechanisms and develop new ones. In this paper, we study this extended version of the VeReMi dataset.

Alladi et al. proposed in [2] a misbehavior categorization scheme based on deep learning for intrusion detection in V2V. They propose centralized training in the cloud, while the detection nodes are located on Edge servers close to RSUs. They exclude Delayed Messages and Eventual Stop attacks, and study the seventeen remaining attacks in the VeReMi-extension dataset. Single-stage and multi-stage classifiers are proposed with similar performances, and several deep learning model architectures are compared. They consist of several layers of Convolutional Neural Network (CNN) and/or Long Short-Term Memory (LSTM). The authors found that the models with 2-CNN/1-LSTM layers and 4-LSTM layers performed slightly better than other models.

In this article, we propose a distributed learning scheme using Federated Learning, where both training and evaluation take place on Edge servers. Despite that our proposal leverages Federated Learning like [8], our approach is focused on V2N communications, not V2V. Another key difference is that, in our proposal, Federated Learning clients are running on the Edge servers, not in the vehicles. The dataset used in [12], [11], [6], [16], and [8] is the original VeReMi dataset [15] consisting of five position falsification attacks only. We conduct our study and evaluate the performance using all nineteen attacks of VeReMi-extension dataset [5].

# 4 5G Edge Misbehavior Detection System Architecture

V2X Application Servers (V2X ASs) are essential for collecting data from vehicles and delivering various C-ITS services to them. V2X ASs can be hosted in the central cloud, or on the edge. To improve the security and protection of V2X ASs

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Architecture of 5G Edge Misbehavior Detection System Proposal</figcaption></figure>

against V2X misbehavior/attacks, a misbehavior detection system must be implemented.

When low latency is required, C-ITS services may need to be hosted on the edge network as multiple application instances. In this case, the misbehavior detection system needs to be implemented accordingly.

Our proposal, depicted in Fig. 2, consists of numerous instances of Misbehavior Detection Functions (MDFs) distributed geographically on Edge networks, a Federated Learning Central (FL-C) server, a Misbehavior Reporting Function (MRF) in the central cloud coupled with the 5G Core (5GC) network, and a Honeypot server.

The MDFs are in charge of the real-time processing and monitoring of V2N traffic packets. Each MDF instance is connected to an Intermediate-UPF (I-UPF) on a Local Access Data Network (LADN). The MDF is also considered a Federated Learning client, where it shares its local model parameters with the FL-C server without sharing local training data collected from vehicles.

The Federated Learning Central server acts as a central hub for organizing and compiling improved machine learning models derived from the received models from MDF instances. Without having any access to local data, it averages the models' weights to enable collaborative learning. This methodology preserves data security and privacy while enabling scalability and leveraging the collective intelligence of all MDF instances.

Misbehavior Reporting Function (MRF) is considered an Application Function (AF) co-located with the 5GC network. In case an attack is detected, the MRF has the ability to initiate traffic-steering requests to the core network, which can re-route the attacker's traffic to the Honeypot server instead of the C-ITS application instance, protecting the application from falsified data. It also records security incidents and notifies Telecommunication network operators or legal authorities, who can completely revoke access for the misbehaving vehicle or take legal action if necessary.
The Honeypot server has the role of a decoy server that will collect malicious V2X messages, creating a new dataset of attacks that can be studied and leveraged to improve the training and efficiency of misbehavior detection models.

Our proposed architecture offers enhanced data privacy, improved application security, and effective misbehavior detection for V2X ASs in C-ITS systems by combining the power of Federated Learning, 5G control functions and traffic steering functionality, and cybersecurity best practices.

# 5 Federated Learning Proposal

We propose an AI model that leverages the advantages of both Long Short-Term Memory (LSTM) and Federating Learning. LSTM emerges as an intuitive option for processing vehicle movement data because of its distinct features. LSTM, which is a type of Recurrent Neural Networks (RNNs), has sophisticated components called memory cells. LSTMs are able to recognize and store long-term dependencies in sequential data. Input, forget, and output gates are built into the memory cells in LSTMs, giving them the ability to selectively retain or discard information. This effectively increases their ability to model, predict, and classify sequential data with long-range dependencies. Because positions, velocities, and other variables that make up vehicle movement data are fundamentally time-dependent, LSTM successfully captures the underlying patterns and dependencies by maintaining an internal memory state. It is capable of modeling the dynamic characteristics of vehicle movement. In tasks like abnormal route detection or route prediction, knowing the complex links between past and future positions is essential, and it can be helpful in vehicle movement analysis. LSTM excels at conserving crucial contextual details about previous positions by selectively keeping essential information from previous states and eliminating unnecessary data. This gives the model the ability to accurately classify normal and abnormal trajectories and forecast future positions, ensuring a thorough understanding of the vehicle's movement dynamics. A window, in LSTM, is subset of sequential data that is analyzed at each time step. It denotes a predetermined amount of time or a specified number of prior time steps that the LSTM model will take into account while making predictions or doing analysis. The input sequence can be overlapping or non-overlapping. The LSTM model uses these windows as input, which enables it to recognize temporal correlations and patterns in the data. In other words, the amount of historical data that the LSTM takes into account at each time step depends on the size or duration of the window. Additionally, LSTM networks have good feature extraction capabilities. They can independently discover useful representations or features from the initial raw input data. A thorough comprehension of vehicle movement dynamics and precise classification are made possible by LSTM's capable discovery of related spatial and temporal elements.

Basic vehicle movement features are mainly related to time, position, and speed in both x and y directions. They might also include acceleration and heading. Training a deep learning misbehavior detection model using only raw features may not lead to optimal performance. Also, using the time and position values of a dataset in their raw format may cause the model to overfit, which might degrade accuracy with new data while performing well on training data. To avoid this problem, we use differential features like *deltaTime* and *deltaPosition*, which represent the differences between the current received position and time and their recently received values from the same vehicle. Also, to verify the consistency between the received values of time, position, and speed, we calculate a new speed value based on *deltaTime* and *deltaPosition*, independently from the received speed value. After that, we deduct the newly calculated speed value from the received speed value to obtain *deltaSpeed*. In summary, the features we used comprise: *time*, *deltaTime*, *position*, *deltaPosition*, *speed*, and *deltaSpeed*.

Federated Learning (FL), which was introduced by Google in [9], has the potential to significantly improve AI deployments due to its numerous advantages. Data locality is a noteworthy advantage of federated learning. Data processing and model training can be done directly on user devices, vehicles, or edge nodes, bringing them closer to the data source. This improves efficiency by eliminating the need to send enormous amounts of data to a central server, while simultaneously reducing latency. Another key benefit of federated learning is scalability. The Federated Learning central server combines local models received from several participants through parallel and distributed training on a variety of clients. This collaborative approach results in a robust and thorough global model. Federated Learning effectively manages big datasets by utilizing the processing capacity of a variety of devices. Federated learning also encourages better data representation. The training method captures rich variations and nuances by combining a variety of data sources from diverse devices or places. The resulting global model benefits from a wider range of viewpoints, guaranteeing improved generalization across various contexts. Beyond these technological benefits, federated learning enables co-operation between multiple Telecommunications operators. It makes it easier for them to collaborate without exchanging users' data. It promotes collaboration and information sharing while adhering to privacy laws and protecting company interests by allowing model training without data sharing.

The implementation of the Federated Averaging function, summarized in Algorithm 1, accepts three parameters, namely *client_models*, *data_sizes*, and *performances*. The *client_models* parameter is a set of client models which are participating in the federated averaging process. The *data_sizes* parameter denotes the corresponding size of training data utilized by each client model. The *performances* parameter
**Algorithm 1** Federated Averaging Function

Require: client_models, data Sizes, performances
1: norm_data_size ← normalize(data_size)
2: norm_perf ← normalize(performances)
3: combine ← (2/3) * norm_data_size + (1/3) * norm_perf
4: client_norm Coeffs ← nomalize(combine)
5: averaged weights ← []
6: client_models weights ← []
7: **for** model in client_models **do**
8:     client_models weights.append(model.get weights())
9:     **end for**
10:    **for** weights in client_models weights **do**
11:        initialize(averaged layer)
12:        **for** model_id in client_models **do**
13:            averaged layer ← averaged layer +
            client_norm Coeffs(model_id] * weights
14:            **end for**
15:        averaged weights.append(averaged layer)
16:    **end for**
17:    averaged model.set weights(averaged weights)
18:    **return** averaged model

represents the accuracy scores of each client model. To determine the coefficient assigned to each client model during the averaging process, we utilize training data sizes and performance, giving the former double the importance of the latter. To obtain the averaged model, the algorithm aggregates the multiplications of the layer weights of each model by its corresponding client model coefficient, calculated in the previous step. The averaged model will then be shared with clients. This process is considered one cycle and can be repeated as necessary.

# 6 Performance Evaluation

This section outlines the 5G network evaluation environment specifications, dataset criteria, dataset splitting methodology, deep learning and federated learning hyper-parameters utilized in the evaluation, performance metrics, simulation results, and results discussion.

The assessment of performance was conducted on a modern testing platform, utilizing a machine with high specifications, equipped with an Nvidia GeForce RTX 3090 graphics card, an Intel Core i7-11700KF processor, 64 GB of DDR4 RAM, and 2 TB of NVMe SSD storage. The computational capacity of the machine facilitated the efficient processing of demanding deep learning algorithms that were utilized for the misbehavior detection model. The experimental software setup consisted of VirtualBox 6.1 as the hypervisor, Ubuntu 20.04 as the operating system, TensorFlow 2.12.0 [1] as the deep learning framework running on Python 3.9.16, free5GC 3.1.1 [4] as the 5G core network emulator, and UER-ANSIM [14] as the user equipment and radio network simulator.

To enhance the privacy of vehicles in a V2V ad-hoc environment, each vehicle is assigned multiple pseudo certificates and pseudo IDs, which are frequently swapped to protect its real identity. When a pseudo ID change occurs, IP and MAC addresses are also replaced in parallel on the PC5 interface, which is the interface used for V2V, V2I, and V2P communications in C-V2X. The intention is to protect the vehicle from being tracked by malicious peers. However, in a 5G V2N environment, which is a client-server architecture, the interface utilized is the Uu interface, and the privacy requirements are different. To communicate on V2N, the vehicle has to pass the 5G authentication process to receive an IP address during the PDU session establishment procedure. Therefore, assuming 5G security best-practices are followed and IP spoofing attacks are not possible, the 5G core network can always identify the source of V2N traffic and link it to a specific vehicle or UE (User Equipment), even if it uses multiple pseudo IDs. For this reason, our model, which intends to protect V2N traffic, analyzes the dataset based on sender IDs instead of pseudo IDs.

The VeReMi-extension dataset consists of more than 63 gigabytes of recorded vehicle traces in JSON format. It covers 19 types of V2X attacks in a total of 39 datasets. Two datasets per attack (high density: 37.03 vehicles/km² and low density: 16.36 vehicles/km²), each containing two hours of recorded vehicle messages; and one comprehensive dataset named MixAll, which encompassed all 19 attacks that occurred within a 24-hour timeframe (23.29 vehicles/km²). All of the datasets have a misbehaving vehicle ratio of 30%.

After converting all datasets to CSV format, we appended them with the three differential features discussed in Section 5 (deltaTime, deltaPosition, and deltaSpeed). Then, in order to implement federated learning, we split the map into six equal geographical areas. The data size distribution between areas 1, 2, 3, 4, 5, and 6 is approximately 22%, 9%, 22%, 31%, 13%, and 3% respectively. To correctly split vehicles' data between areas, we utilize the Ground Truth files provided in the VeReMi-extension dataset. They contain authentic data that would have been transmitted by the attacking vehicles if they were not malicious.

To evaluate the performance and efficiency of our proposed federated learning scheme, we compare three approaches: i) a single centralized model, trained on all data from all areas. ii) six standalone models, each trained exclusively on the subset of its respective area. iii) our proposed scheme, which consists of six federated models, each trained solely with its corresponding subset while leveraging the Federated Averaging process.

The models' design consists of two bidirectional LSTM layers with 64 nodes each, two Dropout layers with the rate set to 0.2 to minimize overfitting, and one Dense layer with 24 nodes. During training, we optimized the chosen hyperparameters of the model. It utilizes a window size of 20, allowing for the inclusion of the preceding 20 vehicle
<table><caption>Table 1. Offline Results: Comprehensive Models Comparison</caption><thead><tr><th rowspan="2">Label</th><th rowspan="2">Dataset</th><th rowspan="2">Density</th><th colspan="4">Centralized</th><th colspan="4">Standalone</th><th colspan="4">Federated (our proposal)</th></tr><tr><th>Acc.</th><th>Prec.</th><th>Recall</th><th>F1-score</th><th>Acc.</th><th>Prec.</th><th>Recall</th><th>F1-score</th><th>Acc.</th><th>Prec.</th><th>Recall</th><th>F1-score</th></tr></thead><tbody><tr><td rowspan="2">1</td><td rowspan="2">ConstPos</td><td>High</td><td>0.9903</td><td>0.9942</td><td>0.9728</td><td>0.9834</td><td>0.9810</td><td>0.9927</td><td>0.9412</td><td>0.9663</td><td>0.9859</td><td>0.9960</td><td>0.9553</td><td>0.9753</td></tr><tr><td>Low</td><td>0.9841</td><td>0.9915</td><td>0.9575</td><td>0.9742</td><td>0.9768</td><td>0.9866</td><td>0.9390</td><td>0.9622</td><td>0.9845</td><td>0.9949</td><td>0.9557</td><td>0.9749</td></tr><tr><td rowspan="2">2</td><td rowspan="2">ConstPosOffset</td><td>High</td><td>0.9903</td><td>0.9942</td><td>0.9728</td><td>0.9834</td><td>0.9810</td><td>0.9927</td><td>0.9412</td><td>0.9663</td><td>0.9859</td><td>0.9960</td><td>0.9553</td><td>0.9753</td></tr><tr><td>Low</td><td>0.8563</td><td>0.8309</td><td>0.5517</td><td>0.6631</td><td>0.8639</td><td>0.9708</td><td>0.5778</td><td>0.7244</td><td>0.8468</td><td>0.9751</td><td>0.5187</td><td>0.6772</td></tr><tr><td rowspan="2">3</td><td rowspan="2">RandomPos</td><td>High</td><td>0.9895</td><td>0.9941</td><td>0.9709</td><td>0.9824</td><td>0.9981</td><td>0.9934</td><td>1.0000</td><td>0.9967</td><td>0.9988</td><td>0.9960</td><td>1.0000</td><td>0.9980</td></tr><tr><td>Low</td><td>0.9976</td><td>0.9925</td><td>1.0000</td><td>0.9962</td><td>0.9964</td><td>0.9887</td><td>1.0000</td><td>0.9943</td><td>0.9985</td><td>0.9951</td><td>1.0000</td><td>0.9976</td></tr><tr><td rowspan="2">4</td><td rowspan="2">RandomPosOffset</td><td>High</td><td>0.9978</td><td>0.9943</td><td>0.9980</td><td>0.9962</td><td>0.8441</td><td>0.9280</td><td>0.4711</td><td>0.6249</td><td>0.9878</td><td>0.9955</td><td>0.9625</td><td>0.9788</td></tr><tr><td>Low</td><td>0.9974</td><td>0.9925</td><td>0.9995</td><td>0.9960</td><td>0.8263</td><td>0.9016</td><td>0.4609</td><td>0.6100</td><td>0.9886</td><td>0.9949</td><td>0.9686</td><td>0.9816</td></tr><tr><td rowspan="2">5</td><td rowspan="2">ConstSpeed</td><td>High</td><td>0.9939</td><td>0.9942</td><td>0.9846</td><td>0.9894</td><td>0.9912</td><td>0.9930</td><td>0.9767</td><td>0.9848</td><td>0.9911</td><td>0.9956</td><td>0.9737</td><td>0.9845</td></tr><tr><td>Low</td><td>0.9925</td><td>0.9918</td><td>0.9843</td><td>0.9880</td><td>0.9896</td><td>0.9885</td><td>0.9785</td><td>0.9835</td><td>0.9915</td><td>0.9950</td><td>0.9778</td><td>0.9864</td></tr><tr><td rowspan="2">6</td><td rowspan="2">ConstSpeedOffset</td><td>High</td><td>0.9973</td><td>0.9943</td><td>0.9963</td><td>0.9953</td><td>0.9921</td><td>0.9933</td><td>0.9793</td><td>0.9862</td><td>0.9925</td><td>0.9961</td><td>0.9782</td><td>0.9871</td></tr><tr><td>Low</td><td>0.9959</td><td>0.9919</td><td>0.9951</td><td>0.9935</td><td>0.9887</td><td>0.9871</td><td>0.9770</td><td>0.9820</td><td>0.9894</td><td>0.9949</td><td>0.9713</td><td>0.9830</td></tr><tr><td rowspan="2">7</td><td rowspan="2">RandomSpeed</td><td>High</td><td>0.9983</td><td>0.9943</td><td>1.0000</td><td>0.9971</td><td>0.9980</td><td>0.9931</td><td>1.0000</td><td>0.9966</td><td>0.9988</td><td>0.9959</td><td>1.0000</td><td>0.9980</td></tr><tr><td>Low</td><td>0.9974</td><td>0.9919</td><td>1.0000</td><td>0.9960</td><td>0.9962</td><td>0.9882</td><td>1.0000</td><td>0.9941</td><td>0.9985</td><td>0.9951</td><td>1.0000</td><td>0.9976</td></tr><tr><td rowspan="2">8</td><td rowspan="2">RandomSpeedOffset</td><td>High</td><td>0.9968</td><td>0.9943</td><td>0.9949</td><td>0.9946</td><td>0.9427</td><td>0.9912</td><td>0.8099</td><td>0.8914</td><td>0.9853</td><td>0.9957</td><td>0.9539</td><td>0.9744</td></tr><tr><td>Low</td><td>0.9962</td><td>0.9919</td><td>0.9962</td><td>0.9941</td><td>0.9411</td><td>0.9802</td><td>0.8253</td><td>0.8961</td><td>0.9858</td><td>0.9949</td><td>0.9600</td><td>0.9771</td></tr><tr><td rowspan="2">9</td><td rowspan="2">EventualStop</td><td>High</td><td>0.8525</td><td>0.9899</td><td>0.5020</td><td>0.6662</td><td>0.8938</td><td>0.9896</td><td>0.6438</td><td>0.7801</td><td>0.8895</td><td>0.9943</td><td>0.6263</td><td>0.7685</td></tr><tr><td>Low</td><td>0.8360</td><td>0.9816</td><td>0.4870</td><td>0.6510</td><td>0.8805</td><td>0.9808</td><td>0.6320</td><td>0.7687</td><td>0.8848</td><td>0.9923</td><td>0.6388</td><td>0.7772</td></tr><tr><td rowspan="2">10</td><td rowspan="2">Disruptive</td><td>High</td><td>0.9983</td><td>0.9943</td><td>1.0000</td><td>0.9971</td><td>0.9971</td><td>0.9931</td><td>0.9969</td><td>0.9950</td><td>0.9987</td><td>0.9957</td><td>1.0000</td><td>0.9979</td></tr><tr><td>Low</td><td>0.9971</td><td>0.9919</td><td>0.9989</td><td>0.9954</td><td>0.9921</td><td>0.9875</td><td>0.9877</td><td>0.9876</td><td>0.9978</td><td>0.9951</td><td>0.9978</td><td>0.9965</td></tr><tr><td rowspan="2">11</td><td rowspan="2">DataReplay</td><td>High</td><td>0.9962</td><td>0.9943</td><td>0.9929</td><td>0.9936</td><td>0.9872</td><td>0.9930</td><td>0.9626</td><td>0.9775</td><td>0.9949</td><td>0.9957</td><td>0.9874</td><td>0.9915</td></tr><tr><td>Low</td><td>0.9920</td><td>0.9912</td><td>0.9831</td><td>0.9872</td><td>0.9763</td><td>0.9870</td><td>0.9369</td><td>0.9613</td><td>0.9892</td><td>0.9950</td><td>0.9707</td><td>0.9827</td></tr><tr><td rowspan="2">12</td><td rowspan="2">DelayedMessages</td><td>High</td><td>0.9036</td><td>0.9918</td><td>0.6761</td><td>0.8040</td><td>0.9829</td><td>0.9926</td><td>0.9481</td><td>0.9699</td><td>0.9882</td><td>0.9956</td><td>0.9640</td><td>0.9795</td></tr><tr><td>Low</td><td>0.8861</td><td>0.9863</td><td>0.6462</td><td>0.7809</td><td>0.9773</td><td>0.9871</td><td>0.9392</td><td>0.9626</td><td>0.9845</td><td>0.9948</td><td>0.9552</td><td>0.9746</td></tr><tr><td rowspan="2">13</td><td rowspan="2">DoS</td><td>High</td><td>0.9989</td><td>0.9986</td><td>0.9997</td><td>0.9991</td><td>0.9985</td><td>0.9982</td><td>0.9993</td><td>0.9988</td><td>0.9993</td><td>0.9989</td><td>0.9999</td><td>0.9994</td></tr><tr><td>Low</td><td>0.9989</td><td>0.9982</td><td>1.0000</td><td>0.9991</td><td>0.9972</td><td>0.9965</td><td>0.9991</td><td>0.9978</td><td>0.9991</td><td>0.9985</td><td>1.0000</td><td>0.9993</td></tr><tr><td rowspan="2">14</td><td rowspan="2">DoSRandom</td><td>High</td><td>0.9992</td><td>0.9987</td><td>1.0000</td><td>0.9993</td><td>0.9989</td><td>0.9982</td><td>1.0000</td><td>0.9991</td><td>0.9994</td><td>0.9991</td><td>1.0000</td><td>0.9996</td></tr><tr><td>Low</td><td>0.9987</td><td>0.9979</td><td>1.0000</td><td>0.9990</td><td>0.9980</td><td>0.9969</td><td>1.0000</td><td>0.9984</td><td>0.9992</td><td>0.9986</td><td>1.0000</td><td>0.9993</td></tr><tr><td rowspan="2">15</td><td rowspan="2">DoSDisruptive</td><td>High</td><td>0.9991</td><td>0.9986</td><td>1.0000</td><td>1.0000</td><td>0.9988</td><td>0.9980</td><td>1.0000</td><td>0.9990</td><td>0.9994</td><td>0.9990</td><td>1.0000</td><td>0.9995</td></tr><tr><td>Low</td><td>0.9987</td><td>0.9979</td><td>1.0000</td><td>0.9989</td><td>0.9980</td><td>0.9968</td><td>1.0000</td><td>0.9984</td><td>0.9992</td><td>0.9986</td><td>1.0000</td><td>0.9993</td></tr><tr><td rowspan="2">16</td><td rowspan="2">GridSybil</td><td>High</td><td>0.9993</td><td>0.9990</td><td>1.0000</td><td>0.9995</td><td>0.9980</td><td>0.9988</td><td>0.9993</td><td>0.9990</td><td>0.9995</td><td>0.9992</td><td>1.0000</td><td>0.9996</td></tr><tr><td>Low</td><td>0.9988</td><td>0.9983</td><td>1.0000</td><td>0.9992</td><td>0.9979</td><td>0.9977</td><td>0.9993</td><td>0.9985</td><td>0.9993</td><td>0.9990</td><td>1.0000</td><td>0.9995</td></tr><tr><td rowspan="2">17</td><td rowspan="2">DataReplaySybil</td><td>High</td><td>0.9965</td><td>0.9943</td><td>0.9940</td><td>0.9942</td><td>0.9876</td><td>0.9930</td><td>0.9642</td><td>0.9784</td><td>0.9945</td><td>0.9962</td><td>0.9856</td><td>0.9909</td></tr><tr><td>Low</td><td>0.9930</td><td>0.9918</td><td>0.9858</td><td>0.9888</td><td>0.9783</td><td>0.9873</td><td>0.9434</td><td>0.9648</td><td>0.9916</td><td>0.9950</td><td>0.9782</td><td>0.9865</td></tr><tr><td rowspan="2">18</td><td rowspan="2">DoSRandomSybil</td><td>High</td><td>0.9987</td><td>0.9975</td><td>1.0000</td><td>0.9988</td><td>0.9985</td><td>0.9970</td><td>1.0000</td><td>0.9985</td><td>0.9992</td><td>0.9985</td><td>1.0000</td><td>0.9992</td></tr><tr><td>Low</td><td>0.9983</td><td>0.9967</td><td>1.0000</td><td>0.9984</td><td>0.9975</td><td>0.9952</td><td>1.0000</td><td>0.9976</td><td>0.9989</td><td>0.9979</td><td>1.0000</td><td>0.9989</td></tr><tr><td rowspan="2">19</td><td rowspan="2">DoSDisruptiveSybil</td><td>High</td><td>0.9987</td><td>0.9975</td><td>1.0000</td><td>0.9988</td><td>0.9986</td><td>0.9971</td><td>1.0000</td><td>0.9986</td><td>0.9992</td><td>0.9985</td><td>1.0000</td><td>0.9992</td></tr><tr><td>Low</td><td>0.9982</td><td>0.9965</td><td>1.0000</td><td>0.9983</td><td>0.9975</td><td>0.9952</td><td>1.0000</td><td>0.9976</td><td>0.9989</td><td>0.9979</td><td>1.0000</td><td>0.9989</td></tr><tr><td colspan="2">Overall</td><td></td><td>0.9807</td><td>0.9890</td><td>0.9408</td><td>0.9589</td><td>0.9755</td><td>0.9902</td><td>0.9250</td><td>0.9505</td><td><b>0.9852</b></td><td><b>0.9963</b></td><td><b>0.9540</b></td><td><b>0.9709</b></td></tr></tbody></table>

messages. The loss function is binary cross-entropy with Adam as the optimization algorithm, which is widely utilized in neural network models, with a learning rate of 0.0001. The batch size is set to 64. The number of epochs is not fixed, as we implemented an Early Stop mechanism with a patience value of 5, leading to early termination of training if the validation loss did not improve for 5 consecutive epochs.

The performance of the models is determined by the number of True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN) they produce. Accuracy is the ratio of correctly classified instances to the total number of instances: $\frac{TP+TN}{TP+TN+FP+FN}$. Precision indicates the ratio of correctly predicted attacks to the overall detection predicted by the model: $\frac{TP}{TP+FP}$. Recall, also called detection rate, represents the ratio of correctly predicted attacks to the overall actual attacks: $\frac{TP}{TP+FN}$. A high precision implies a small number of false positives. And high recall suggests fewer false

negatives, which also translates to an increased detection rate. Consequently, a performing model is characterized by high precision and high recall. The F1-score is not a distinct metric; rather, it combines precision and recall into a single value. It is calculated using: $2 \times \frac{Precision \times Recall}{Precision+Recall}$.

The evaluation is conducted in two stages. The initial phase involves a numerical evaluation, wherein the models' performance is assessed offline by directly analyzing the datasets without transmitting the data through the network. The second phase involves utilizing 5GC emulation and UE simulation, wherein the data is transmitted through the 5G network and analyzed on the edge servers.

For the numerical analysis stage, the three previously mentioned models are trained on 90% of the MixAll dataset, 10% used for validation, and the remaining 38 datasets are utilized for extensive testing. All datasets were split into six subsets, representing the six areas. The results of this comprehensive
<table><caption>**Table 2.** Online Scenario: Models Performance Comparison (windows-level)</caption><thead><tr><th>Metric</th><th>Model</th><th>Area 1</th><th>Area 2</th><th>Area 3</th><th>Area 4</th><th>Area 5</th><th>Area 6</th><th>Overall</th></tr></thead><tbody><tr><td rowspan="3">Accuracy</td><td>Centralized</td><td>0.9817</td><td>0.9769</td><td>0.9838</td><td>0.9838</td><td>0.9795</td><td>0.9570</td><td>0.9813</td></tr><tr><td>Standalone</td><td>0.9640</td><td>0.9533</td><td>0.9780</td><td>0.9553</td><td>0.9555</td><td>0.9620</td><td>0.9619</td></tr><tr><td>Federated</td><td>0.9891</td><td>0.9816</td><td>0.9847</td><td>0.9803</td><td>0.9833</td><td>0.9851</td><td><strong>0.9838</strong></td></tr><tr><td rowspan="3">Precision</td><td>Centralized</td><td>0.9945</td><td>0.9938</td><td>0.9929</td><td>0.9959</td><td>0.9962</td><td>0.9613</td><td>0.9934</td></tr><tr><td>Standalone</td><td>0.9988</td><td>0.9860</td><td>0.9961</td><td>1.0000</td><td>0.9905</td><td>0.9941</td><td><strong>0.9958</strong></td></tr><tr><td>Federated</td><td>0.9946</td><td>0.9888</td><td>0.9897</td><td>0.9925</td><td>0.9992</td><td>1.0000</td><td>0.9933</td></tr><tr><td rowspan="3">Recall</td><td>Centralized</td><td>0.9609</td><td>0.9570</td><td>0.9684</td><td>0.9656</td><td>0.9573</td><td>0.9667</td><td>0.9631</td></tr><tr><td>Standalone</td><td>0.9140</td><td>0.9141</td><td>0.9513</td><td>0.8944</td><td>0.9081</td><td>0.9417</td><td>0.9159</td></tr><tr><td>Federated</td><td>0.9789</td><td>0.9720</td><td>0.9738</td><td>0.9606</td><td>0.9631</td><td>0.9750</td><td><strong>0.9692</strong></td></tr><tr><td rowspan="3">F1-score</td><td>Centralized</td><td>0.9775</td><td>0.9751</td><td>0.9805</td><td>0.9805</td><td>0.9764</td><td>0.9640</td><td>0.9780</td></tr><tr><td>Standalone</td><td>0.9545</td><td>0.9487</td><td>0.9732</td><td>0.9442</td><td>0.9475</td><td>0.9672</td><td>0.9542</td></tr><tr><td>Federated</td><td>0.9867</td><td>0.9804</td><td>0.9817</td><td>0.9763</td><td>0.9808</td><td>0.9873</td><td><strong>0.9811</strong></td></tr></tbody></table>

<table><caption>**Table 3.** Online Scenario: Correctly Classified Vehicles</caption><thead><tr><th>Vehicle Type</th><th>Veh. Count</th><th>Cent.</th><th>S.A.</th><th>Fed.</th></tr></thead><tbody><tr><td>0-Normal</td><td>3176</td><td>3121</td><td>3141</td><td>3116</td></tr><tr><td>1-ConstPos</td><td>72</td><td>72</td><td>68</td><td>71</td></tr><tr><td>2-ConstPosOffset</td><td>64</td><td>50</td><td>35</td><td>41</td></tr><tr><td>3-RandomPos</td><td>71</td><td>71</td><td>71</td><td>71</td></tr><tr><td>4-RandomPosOffset</td><td>77</td><td>77</td><td>16</td><td>75</td></tr><tr><td>5-ConstSpeed</td><td>70</td><td>70</td><td>68</td><td>70</td></tr><tr><td>6-ConstSpeedOffset</td><td>56</td><td>56</td><td>56</td><td>56</td></tr><tr><td>7-RandomSpeed</td><td>78</td><td>78</td><td>78</td><td>78</td></tr><tr><td>8-RandomSpeedOffset</td><td>72</td><td>69</td><td>49</td><td>70</td></tr><tr><td>9-EventualStop</td><td>81</td><td>51</td><td>57</td><td>57</td></tr><tr><td>10-Disruptive</td><td>75</td><td>75</td><td>75</td><td>75</td></tr><tr><td>11-DataReplay</td><td>68</td><td>68</td><td>64</td><td>68</td></tr><tr><td>12-DelayedMessages</td><td>59</td><td>59</td><td>59</td><td>59</td></tr><tr><td>13-DoS</td><td>64</td><td>64</td><td>64</td><td>64</td></tr><tr><td>14-DoSRandom</td><td>78</td><td>78</td><td>78</td><td>78</td></tr><tr><td>15-DoS Disruptive</td><td>83</td><td>83</td><td>83</td><td>83</td></tr><tr><td>16-GridSybil</td><td>88</td><td>88</td><td>88</td><td>88</td></tr><tr><td>17-DataReplaySybil</td><td>61</td><td>61</td><td>61</td><td>61</td></tr><tr><td>18-DoSRandomSybil</td><td>77</td><td>77</td><td>77</td><td>77</td></tr><tr><td>19-DoSDisruptiveSybil</td><td>74</td><td>74</td><td>74</td><td>74</td></tr><tr><td>All Attacks</td><td>1368</td><td>1321</td><td>1221</td><td>1316</td></tr></tbody></table>

evaluation are presented in Table 1. The table represents a weighted average aggregation of the results from all six areas.

All three models did not perform well on three attack datasets, which are Constant Position Offset low density and the two datasets of Eventual Stop attack.

Also, we can observe that distributed schemes (standalone and federated) significantly improved detection rates on Delayed Messages attacks compared to centralized model. This attack consists of sending accurate position and speed information after a time delay. It is possible that a certain vehicle has moved into a new area while reporting its old position

in a previous area, which makes it easier for the distributed models to detect out-of-area positions.

In the detailed version of the table, which we could not include due to space limitations, we noticed that the areas with the lowest amount of training data, namely area 2 and area 6, performed poorly in standalone mode. Which is even more obvious with Random Position Offset and Random Speed Offset attacks. On the other hand, federated models were able to mitigate the lack of sufficient training data and greatly improve performance in these two areas due to federated averaging.

The primary outcome of this offline stage indicates that our proposed federated learning model has superior performance across all metrics. However, the aim of our study is not centered on enhancing performance but rather on demonstrating the feasibility of utilizing federated learning to achieve privacy and scalability benefits without sacrificing the level of protection of C-ITS application servers.

For online analysis, we utilize the MixAll dataset, divided into six subsets representing the six coverage areas. These subsets were further partitioned into three parts: training, validation, and testing. The size of the data allocated for training is 72%, enabling the model to learn and generalize patterns from a significant portion of the available samples. The validation set, which forms 8% of a subset, to enable fine-tuning of the model's hyperparameters. Finally, the testing part, which is the remaining 20% of the data, was never exposed to the model during training and validation, and it's used to assess the performance of the model. Our online testing scenario contains 4544 vehicles: 3176 normal and 1368 misbehaving, sending more than 400,000 messages.

Table 2 compares the performances of the models per area during the online scenario. The Federated model demonstrated similar to slightly better performance compared to the Centralized model, with both models outperforming the Standalone model.

The Federated model significantly enhanced the performance of areas 2 and 6, which have the least amount of
training data. This further validates the previous observation made during numerical analysis.

In Table 3, we present a summary of the models' detection performances at vehicle-level during the online scenario. Although the Federated model demonstrated superior performance in offline analysis and online window-level results, the Centralized model detected five additional vehicles.

The majority of misbehaving vehicles that went undetected by the models can be attributed to Constant Position Offset and Eventual Stop attacks. The former can be effectively resolved by using the plausibility check we proposed in [16], or alternatively, by utilizing a map. The latter can be attributed to the labeling methodology employed by the authors of the dataset, which is a persistent problem inherited from the original VeReMi dataset. The authors assert that they have addressed the labeling issue of this attack by individually labeling each message; however, in the publicly available version of the dataset, the labeling is still assigned on a vehicle basis.

Lastly, in the Delayed Messages attack, the Centralized model managed to detect at least one window per vehicle, while missing many others, which can explain the difference between window-level and vehicle-level detection scores in this particular attack.

In summary, our proposed Federated model demonstrated comparable performance to that of the Centralized model while providing the benefits of scalability and privacy.

## 7 Conclusion

In this paper, we proposed a novel approach for protecting V2X application servers for 5G edge networks through the implementation of a distributed V2X misbehavior detection system that relies on Federated Learning. After rigorous testing using a large public dataset, we demonstrated the feasibility and advantages of Federated Learning in V2X misbehavior detection in protecting V2X application servers in 5G core networks.

## References

[1] Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xi aoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/ Software available from tensorflow.org.

[2] Tejasvi Alladi, Varun Kohli, Vinay Chamola, and F. Richard Yu. 2022. A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems. Digital Communications and Networks (2022). https://doi.org/10.1016/j.dcan.2022.06.018

[3] Lara Codeca, Raphael Frank, and Thomas Engel. 2015. Luxembourg SUMO Traffic (LuST) Scenario: 24 hours of mobility for vehicular networking research. In 2015 IEEE Vehicular Networking Conference (VNC). 1–8. https://doi.org/10.1109/VNC.2015.7385539

[4] free5GC Project. [n. d.]. https://www.free5gc.org/.

[5] Joseph Kamel, Michael Wolf, Rens W. van der Hei, Arnaud Kaiser, Pascal Urien, and Frank Kargl. 2020. VeReMi Extension: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs. In ICC 2020 - 2020 IEEE International Conference on Communications (ICC). 1–6. https://doi.org/10.1109/ICC40277.2020.9149132

[6] Chiho Kim, Sang-Yoon Chang, Dongeun Lee, Jonghyun Kim, Kyungmin Park, and Jinoh Kim. 2023. Reliable Detection of Location Spoofing and Variation Attacks. IEEE Access 11 (2023), 10813–10825. https://doi.org/10.1109/ACCESS.2023.3241236

[7] Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, Yun-Pang Flötteröd, Robert Hilbrich, Leonhard Lücken, Johannes Rummel, Peter Wagner, and Evamarie Wießner. 2018. Microscopic Traffic Simulation using SUMO, In The 21st IEEE International Conference on Intelligent Transportation Systems. IEEE Intelligent Transportation Systems Conference (ITSC). https://elib.dlr.de/124092/

[8] Pin Lv, Linyan Xie, Jia Xu, Xu Wu, and Taoshen Li. 2022. Misbehavior Detection in Vehicular Ad Hoc Networks Based on Privacy-Preserving Federated Learning and Blockchain. IEEE Transactions on Network and Service Management 19, 4 (2022), 3936–3948. https://doi.org/10.1109/TNSM.2022.3220779

[9] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 54), Aarti Singh and Jerry Zhu (Eds.). PMLR, 1273–1282. https://proceedings.mlr.press/v54/mcmahan17a.html

[10] Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. Journal of machine learning research 12, Oct (2011), 2825–2830.

[11] Prinkle Sharma and Hong Liu. 2021. A Machine-Learning-Based Data-Centric Misbehavior Detection Model for Internet of Vehicles. IEEE Internet of Things Journal 8, 6 (March 2021), 4991–4999. https://doi.org/10.1109/JIOT.2020.3035035

[12] Steven So, Prinkle Sharma, and Jonathan Petit. 2018. Integrating Plausibility Checks and Machine Learning for Misbehavior Detection in VANET. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, Orlando, FL, 564–571. https://doi.org/10.1109/ICMLA.2018.00091

[13] Christoph Sommer, Reinhard German, and Falko Dressler. 2011. Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis. IEEE Transactions on Mobile Computing (TMC) 10, 1 (January 2011), 3–15. https://doi.org/10.1109/TMC.2010.133

[14] UERANSIM. [n. d.]. https://github.com/aligungr/UERANSIM.

[15] Kargl F. van der Heijden R.W., Lukaseder T. 2018. VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs. Chang B., Li Y., Zhu S. (eds) Security and Privacy in Communication Networks. SecureComm 254 (2018). https://doi.org/10.1007/978-3-030-01701-9_18

[16] Hadi Yakan, Ilhem Fajjari, Nadjib Aitsaadi, and Cedric Adjih. 2023. A Novel AI Security Application Function of 5G Core Network for V2X C-ITS Facilities Layer. In ICC 2023 - IEEE International Conference on Communications.