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The Controller Area Network (CAN) is widely used in the automotive industry for its ability to create inexpensive and fast networks. However, it lacks an authentication scheme, making vehicles vulnerable to spoofing attacks. Evidence shows that attackers can remotely control vehicles, posing serious risks to passengers and pedestrians. Several strategies have been proposed to ensure CAN data integrity by identifying senders based on physical layer characteristics, but high computational costs limit their practical use. This paper presents a framework to efficiently identify CAN bus system senders by fingerprinting them. By modeling the CAN sender identification problem as an image classification task, the need for expensive handcrafted feature engineering is eliminated, improving accuracy using deep neural networks. Experimental results show the proposed methodology achieves a maximum identification accuracy of 98.34%, surpassing the state-of-the-art method’s 97.13%. The approach also significantly reduces computational costs, cutting data processing time by a factor of 27, making it feasible for real-time application in vehicles. When tested on an actual vehicle, the proposed methodology achieved a no-attack detection rate of 97.78% and an attack detection rate of 100%, resulting in a combined accuracy of 98.89%. These results highlight the framework’s potential to enhance vehicle cybersecurity by reliably and efficiently identifying CAN bus senders.