Automation and connectivity have increased as autonomous vehicle technology has advanced. CAN is one of numerous serial protocols used in a variety of cars. Contemporary automobiles are increasingly susceptible to attacks targeting automotive networks due to their increased functionality and connectivity. The CAN bus protocol is not secure by design, it is open to many assaults. To identify attacks on the CAN bus, system for intrusion detection (IDS) must be highly accurate in their design. The deep learning based model can achieve higher accuracy in detection and classification of network messages. In this research, we provide an efficient IDS strategy for binary classification based on deep learning. The best model is determined using the keras tuner library and the implementation results suggest that proposed strategy achieves the accuracy of 99% in classification of messages in CAN bus network. The results outperform the classification accuracy with respect to the supervised machine learning based approaches.