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@article{172986,
        author = {Ms. Jyoti B.Maske and Prof. Rupali Maske and Prof.Sai Takwale},
        title = {Cyber Attacks Detection on Electric vehicles using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {1539-1543},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172986},
        abstract = {As a result of system integration and intellectualization, the significance of cyber-physical protection for power electronic device systems is continuously increasing. In  particular, driving systems that are part of power train systems. Due to the smart transportation system's connection to external networks, hybrid vehicles are increasingly vulnerable to cyberattacks these days. This work uses a machine learning system based on Advanced Long Short Term Memory (ALSTM) to detect cyberattacks on electric cars (EVs) based on different driving conditions. Both device-level and vehicle-level signals are obtained in order to depict the quick physical characteristics of EVs. Then, using a data-driven approach with very powerful gadgets and automotive designs, designers provide new data characteristics related to the vital system stability and mechanical behavior of the car. An advanced machine-learning-based classifier with exceptional accuracy under a variety of driving conditions is built based on the properties of the data.},
        keywords = {Electric vehicles, anomaly detection, one-class support vector machine, optimization algorithm.},
        month = {February},
        }