In Vehicular Ad-hoc Networks (VANETs), participating vehicles periodically transmit basic safety messages (BSMs) containing critical information about their current status, including position, speed, acceleration and heading. BSMs are broadcast wirelessly to surrounding vehicles and help improve situational awareness. However, it also introduces critical security concerns and requires robust security measures and innovative approaches to ensure the reliability of communication within VANETs. This paper addresses the emerging threat of Traffic Congestion Sybil attacks, where malicious entities inject spurious data into BSMs to fabricate artificial traffic congestion. We implement models, using classical machine learning algorithms, to accurately detect malicious BSMs containing such spurious data. Preliminary results indicate that the proposed models can detect these sybil attacks with a high degree of accuracy and low false-positive and false-negative rates. Comparison with existing techniques demonstrate that the proposed models perform as well or better than more complex deep learning models available in the literature.