As the automotive industry hurtles toward an era of interconnected, smart vehicles, the need for robust cybersecurity measures becomes increasingly imperative. The advent of modern automobiles equipped with sophisticated digital systems has introduced novel security challenges, particularly in the realm of intrusion detection within vehicular networks. This paper explores the application of Ant Colony Optimization (ACO), a bio-inspired algorithm, as a powerful tool for enhancing automotive network security. By modeling the problem as a path-finding endeavor, ACO excels in detecting sequences of events that could lead to unauthorized actions. This research outlines a comprehensive methodology, including problem modeling, pheromone-based learning, and result visualization, demonstrating the efficacy of ACO in fortifying automotive cybersecurity. However, it is crucial to underscore the necessity of complementing ACO findings with domain knowledge and further analysis.