Modern vehicles, nowadays, come loaded with hundreds of different sensors generating a huge amount of data. This data is shared and processed among different Electronic Control Units (ECUs) through an in-vehicle network, such as the CAN bus, to improve the driver’s experience and safety. However, the implementation of new features increases exposure to cyber-attacks. The CAN bus, which is designed to grant reliable communication, has many security weaknesses that might be exploited by an attacker. The need for highly accurate real-time intrusion detection systems (IDSs) for the automotive industry is limited to classical machine learning techniques, which are usually time-consuming and have hardware limitations. In this work, we analyze an optimized and efficient version of a network-based intrusion detection system for CAN bus attack detection based on Quantum Annealing. The models were tested on two different CAN bus datasets. The results show that the Quantum Annealing algorithm outperforms a classical classification algorithm in terms of time performance, which is important in the identification of attacks in the automotive sector. Furthermore, it is also shown that the algorithm achieves a detection accuracy similar to the classification algorithm used as an example.