US-20260129456-A1 - MISBEHAVIOR DETECTION AND VERIFICATION USING ENVIRONMENT MAPPING

Abstract

A system for vehicular misbehavior detection, comprising: a vehicular network, and a misbehavior authority in communication with vehicles in the vehicular network, the misbehavior authority configured to receive a misbehavior report from at least one of the vehicles in the vehicular network, the misbehavior report based on a determined discrepancy between a collective perception message (CPM) received from a malicious vehicle and sensor data of the at least one of the vehicles, receive sensor data from the vehicles in the vehicular network, generate an environment map based on the sensor data received from the vehicles in the vehicular network, analyze the misbehavior report by comparing information in the misbehavior report with the generated environment map, and initiate a certificate revocation process for the malicious vehicle if the misbehavior report is verified.

Inventors

• Mohit Kumar Sharma
• Anuj Abraham
• WASSIM HAMIDOUCHE

Assignees

• TECHNOLOGY INNOVATION INSTITUTE - SOLE PROPRIETORSHIP LLC

Dates

Publication Date

20260507

Application Date

20251028

Claims (20)

1. 1 . A system for vehicular misbehavior detection, comprising: a vehicular network; and a misbehavior authority in communication with vehicles in the vehicular network, the misbehavior authority configured to: receive a misbehavior report from at least one of the vehicles in the vehicular network, the misbehavior report based on a determined discrepancy between a collective perception message (CPM) received from a malicious vehicle and sensor data of the at least one of the vehicles, receive sensor data from the vehicles in the vehicular network, generate an environment map based on the sensor data received from the vehicles in the vehicular network, analyze the misbehavior report by comparing information in the misbehavior report with the generated environment map, and initiate a certificate revocation process for the malicious vehicle if the misbehavior report is verified.
2. 2 . The system of claim 1 , wherein the sensor data received from the vehicles in the vehicular network includes at least one of radio frequency (RF) signals, video data, radar data, and intra-sensor communication signals.
3. 3 . The system of claim 1 , wherein generating the environment map comprises using a deep learning-based tomography technique to process the sensor data received from the vehicles in the vehicular network.
4. 4 . The system of claim 1 , wherein analyzing the misbehavior report comprises: creating a first bounding box based on the generated environment map; creating a second bounding box based on information in the misbehavior report; and calculating an Intersection over Union (IoU) metric between the first and second bounding boxes.
5. 5 . The system of claim 4 , wherein analyzing the misbehavior report comprises comparing the calculated IoU metric against a predefined confidence threshold.
6. 6 . The system of claim 1 , wherein analyzing the misbehavior report comprises: reconstructing the CPM based on the generated environment map; comparing the reconstructed CPM with the CPM received from the vehicle reporting misbehavior; and determining if an attack has occurred based on the comparison.
7. 7 . The system of claim 6 , wherein comparing the reconstructed CPM with the received CPM comprises calculating a weighted sum of differences between corresponding fields in the reconstructed CPM with the received CPM.
8. 8 . The system of claim 1 , wherein the misbehavior authority is further configured to broadcast an updated CPM message to other vehicles in the vehicular network after initiating the certificate revocation process.
9. 9 . The system of claim 1 , wherein the misbehavior authority is integrated within a 5G core network.
10. 10 . The system of claim 9 , wherein initiating the certificate revocation process comprises sending a revoke request to a Session Management Function (SMF) in the 5G core network.
11. 11 . A method for vehicular misbehavior detection, comprising: receiving, by a misbehavior authority in communication with vehicles in a vehicular network, a misbehavior report from at least one of the vehicles in the vehicular network, the misbehavior report based on a determined discrepancy between a collective perception message (CPM) received from a malicious vehicle and sensor data of the at least one of the vehicles; receiving, by the misbehavior authority, sensor data from the vehicles in the vehicular network; generating, by the misbehavior authority, an environment map based on the sensor data received from the vehicles in the vehicular network; analyzing, by the misbehavior authority, the misbehavior report by comparing information in the misbehavior report with the generated environment map; and initiating, by the misbehavior authority, a certificate revocation process for the malicious vehicle if the misbehavior report is verified.
12. 12 . The method of claim 11 , wherein the sensor data received from the vehicles in the vehicular network includes at least one of radio frequency (RF) signals, video data, radar data, and intra-sensor communication signals.
13. 13 . The method of claim 11 , wherein generating the environment map comprises using a deep learning-based tomography technique to process the sensor data received from the vehicles in the vehicular network.
14. 14 . The method of claim 11 , wherein analyzing the misbehavior report comprises: creating a first bounding box based on the generated environment map; creating a second bounding box based on information in the misbehavior report; and calculating an Intersection over Union (IoU) metric between the first and second bounding boxes.
15. 15 . The method of claim 14 , wherein analyzing the misbehavior report comprises comparing the calculated IoU metric against a predefined confidence threshold.
16. 16 . The method of claim 11 , wherein analyzing the misbehavior report comprises: reconstructing the CPM based on the generated environment map; comparing the reconstructed CPM with the CPM received from the vehicle reporting misbehavior; and determining if an attack has occurred based on the comparison.
17. 17 . The method of claim 16 , wherein comparing the reconstructed CPM with the received CPM comprises calculating a weighted sum of differences between corresponding fields in the reconstructed CPM with the received CPM.
18. 18 . The method of claim 11 , comprising broadcasting, by the misbehavior authority, an updated CPM message to other vehicles in the vehicular network after initiating the certificate revocation process.
19. 19 . The method of claim 11 , wherein the misbehavior authority is integrated within a 5G core network.
20. 20 . The method of claim 19 , wherein initiating the certificate revocation process comprises sending a revoke request to a Session Management Function (SMF) in the 5G core network.

Description

CROSS-REFERENCE TO RELATED APPLICATION This application claims priority to U.S. Provisional Application No. 63/714,993, filed Nov. 1, 2024, which is incorporated by reference in its entirety. FIELD The present disclosure generally relates to misbehavior detection and verification in vehicular communication networks. In one example, the systems and methods identify and validate potential misbehavior in Cellular Vehicle-to-Everything (C-V2X) environments through the use of dynamically generated environment maps. BACKGROUND Cellular Vehicle-to-Everything (C-V2X) technology has emerged as a promising solution for enhancing road safety, traffic efficiency, and overall transportation system performance. C-V2X enables vehicles to communicate with each other, pedestrians, infrastructure, and network systems, facilitating the exchange of beneficial information such as vehicle position, speed, and road conditions. This technology relies on a combination of direct short-range communications and cellular network infrastructure to create a comprehensive and reliable vehicular communication ecosystem. Current C-V2X systems employ various security measures, including cryptography-based solutions and public key infrastructure (PKI) systems, to authenticate vehicles and protect against external threats. Despite these security measures, C-V2X networks remain vulnerable to attacks launched by malicious insiders who are already authenticated and part of the system. These insider threats can manipulate or falsify data payloads in messages such as Basic Safety Messages (BSM), Cooperative Awareness Messages (CAM), and Collective Perception Messages (CPM), potentially compromising the integrity of the network. Existing authentication systems struggle to detect and mitigate these internal attacks, which can lead to severe consequences in terms of road safety and traffic management. Additionally, the current approaches to misbehavior detection often lack the ability to independently verify reported incidents, making it challenging to distinguish between genuine threats and false alarms in real-time vehicular environments. SUMMARY This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. In one aspect, the present disclosure relates to a system for vehicular misbehavior detection, comprising a vehicular network, and a misbehavior authority in communication with vehicles in the vehicular network, the misbehavior authority configured to receive a misbehavior report from at least one of the vehicles in the vehicular network, the misbehavior report based on a determined discrepancy between a collective perception message (CPM) received from a malicious vehicle and sensor data of the at least one of the vehicles, receive sensor data from the vehicles in the vehicular network, generate an environment map based on the sensor data received from the vehicles in the vehicular network, analyze the misbehavior report by comparing information in the misbehavior report with the generated environment map, and initiate a certificate revocation process for the malicious vehicle if the misbehavior report is verified. In embodiments of this aspect, the disclosure according to the above example embodiment is provided, wherein the sensor data received from the vehicles in the vehicular network includes at least one of radio frequency (RF) signals, video data, radar data, and intra-sensor communication signals. In embodiments of this aspect, the disclosure according to any one of the above example embodiments is provided, wherein generating the environment map comprises using a deep learning-based tomography technique to process the sensor data received from the vehicles in the vehicular network. In embodiments of this aspect, the disclosure according to the above example embodiment is provided, wherein analyzing the misbehavior report comprises creating a first bounding box based on the generated environment map, creating a second bounding box based on information in the misbehavior report, and calculating an Intersection over Union (IoU) metric between the first and second bounding boxes. In embodiments of this aspect, the disclosure according to the above example embodiment is provided, wherein analyzing the misbehavior report further comprises comparing the calculated IoU metric against a predefined confidence threshold. In embodiments of this aspect, the disclosure according to the above example embodiment is provided, wherein analyzing the misbehavior report comprises reconstructing the CPM based on the generated environment map, comparing the reconstructed CPM with the CPM received from the vehicle reporting misbehavior, and determining if an attack has occurred b