An Integrated Machine Learning Framework for Power Data Security and Misbehavior Detection in Next‐Generation <scp>VANETs</scp>

ABSTRACT Future generation Vehicular Ad‐hoc Networks (VANETs) models primarily consider continuous power‐aware communication to support Intelligent Transportation Systems (ITS) in exchanging diverse sensitive vehicular information. Existing systems often struggle to capture hidden misbehaviors, particularly in power‐domain parameters such as abnormal power transmission, unexpected power changes, and malicious increases in signal strength. These threats disrupt sensitive vehicular communication and real‐time vehicle coordination. This study aims to develop a lightweight, power‐aware malicious‐detection framework capable of identifying hidden, short‐term power‐domain attacks in resource‐constrained VANET environments. To address these challenges, this study proposes a novel integrated machine learning (IML) framework that combines the strength of Feather Layer Perceptron (FLP) for feature encoding with a Dense Tree Module (DTM) for effective decision‐making and Tiny‐LSTM with the Endomode Sliding Window Approach to quickly analyze short‐term changes in vehicle power signals. The suggested system is a future‐expected green model that can efficiently analyze multidimensional power features in low‐computation mode to identify malicious power patterns. The model is simulated using two distinct datasets: Secure VANET Vehicle Dataset and “The Indoor Localization Dataset,” both adapted from a public repository, which capture vehicular communication, power‐related features, and motion information under everyday and attack scenarios. It also provides additional signal‐based measurements and environmental features to enhance feature diversity. To improve the simulation, we consider additional synthetic features. Experimental results demonstrate that the proposed IML model achieves 99.7% test detection accuracy on standard P‐OBU power data and maintains 97.6% accuracy under noisy power‐domain inputs. By leveraging these advantages, the proposed framework effectively enhances power‐domain security in VANETs by accurately detecting anomalies under realistic, noisy conditions. Also, it provides a scalable solution for next‐generation intelligent vehicular networks.