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@article{179080,
        author = {Honey K Amin and Prof. Deepak Upadhyay},
        title = {Deep Learning based Enhanced Intrusion Detection for Vehicular Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5473-5479},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179080},
        abstract = {The rapid advancement of autonomous vehicle technologies has significantly improved vehicle control systems, primarily through the Controller Area Network (CAN) bus protocol. However, the inherent complexity and openness of CAN networks expose them to numerous cybersecurity issues. Despite the CAN bus's crucial role, its susceptibility to cybersecurity threats, particularly spoofing attacks, remains a significant concern. Our study presents an optimized Intrusion Detection System based on Bidirectional Long Short-Term Memory (LSTM) along with Convolutional Neural Networks (CNN). This work is designed to detect and mitigate attacks on CAN networks through the application of advanced deep learning techniques. In addition to the core CNN model, the incorporation with LSTM to enhance the system's accuracy and robustness.},
        keywords = {Autonomous Vehicles., Bidirectional LSTM (BiLSTM), Controller Area Network (CAN), CyberSecurity, Convolutional Neural Network (CNN), Intrusion Detection System (IDS)},
        month = {May},
        }