AUTHOR=Ness Stephanie 

TITLE=Robust detection framework for adversarial threats in Autonomous Vehicle Platooning

JOURNAL=Frontiers in Big Data

VOLUME=Volume 8 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1617978

DOI=10.3389/fdata.2025.1617978

ISSN=2624-909X

ABSTRACT=IntroductionThe study addresses adversarial threats in Autonomous Vehicle Platooning (AVP) using machine learning.MethodsA novel method integrating active learning with RF, GB, XGB, KNN, LR, and AdaBoost classifiers was developed.ResultsRandom Forest with active learning yielded the highest accuracy of 83.91%.DiscussionThe proposed framework significantly reduces labeling efforts and improves threat detection, enhancing AVP system security.