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@article{176130,
        author = {SANGEETHA S and Sankara Narayanan S.T},
        title = {Drone Net Scout- Threat Detection and Mitigation for UAV security},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5399-5402},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176130},
        abstract = {The increasing reliance on drones for various applications such as surveillance, delivery, and data collection has made them vulnerable to cyber threats. Securing drone systems against potential cyberattacks is critical for ensuring their safe and reliable operation. This project proposes a machine learning-based approach for enhancing the cybersecurity of drone systems by employing three powerful algorithms: Random Forest Classifier, Gradient Boosting, and XGBoost Classifier. These algorithms are used to detect and classify different types of cyberattacks on drones, such as Hulk attack, data manipulation, unauthorized access, and network intrusion. The models are trained on datasets containing both normal and attack scenarios and evaluated based on accuracy, precision, recall, and F1-score. The integration with Django allows for real-time monitoring and threat detection through a user-friendly interface. This project aims to improve drone security by leveraging ML techniques to predict and mitigate cyber threats.},
        keywords = {Cyber-attacks, Gradient Boosting, Machine learning, Random Forest Classifier, Threat Detection, XGBoost Classifier},
        month = {April},
        }