MisbehaviorX: Comprehensive V2X Misbehavior Detection Dataset Enabled by the V2X Application Spoofing Platform

Citation Author(s):
Md Hasan
Shahriar
Virginia Polytechnic Institute and State University
Mohammad Raashid
Ansari
Qualcomm Technologies, Inc.
Jean-Philippe
Monteuuis
Qualcomm Technologies, Inc.
Cong
Chen
Qualcomm Technologies, Inc.
Jonathan
Petit
Qualcomm Technologies, Inc.
Y. Thomas
Hou
Qualcomm Technologies, Inc.
Wenjing
Lou
Qualcomm Technologies, Inc.
Submitted by:
Md Hasan Shahriar
Last updated:
Tue, 11/05/2024 - 15:11
DOI:
10.21227/s44z-8616
Data Format:
Research Article Link:
Links:
License:
0
0 ratings - Please login to submit your rating.

Abstract 

This dataset contains a comprehensive V2X misbehavior dataset simulated using VASP, an open-source framework. VASP allows the simulation of diverse types of V2X attacks and works as a sub-module for Veins, a well-established open-source framework for running vehicular network simulations. Veins runs on an event-based network simulator OMNeT ++, and road traffic simulator SUMO. Data are collected from the Boston traffic network, which is a good candidate to represent real-world traffic mobility. We run VASP simulation for 3,000 simulated seconds to collect benign traces without any attacks. Such simulation provided us with 1,018,098 benign BSMs from 475 different vehicles. Similarly, we ran a VASP simulation for 1360 simulated seconds to collect malicious traces with 68 distinct attacks. While running the attack, we selected the attack policy as persistent, where the attacker vehicle always transmits attack messages and 25% malicious vehicles.

Instructions: 

The dataset folder contains two subfolders: ambients and attacks. The Ambient folder contains the BSM traces in an ambient and benign scenario. On the other hand, each file in the attacks folder contains BSM traces for different attacks.

Funding Agency: 
The US National Science Foundation, The Office of Naval Research
Grant Number: 
1837519, 2235232, 2312447, N00014-19-1-2621