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@article{180876,
        author = {Nagarjun H R and Sahithya SY and Sameeksha C and Shashank V Gowda},
        title = {Secure Flight Delay Prediction with Anomaly Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2937-2949},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180876},
        abstract = {In today’s rapidly evolving aviation sector, unexpected flight delays continue to disrupt passenger schedules and strain airline operations. While several existing systems attempt to predict delays using historical data, many lack real-time adaptability, overlook unusual disruptions (anomalies), and often compromise on data security. Our project addresses these challenges by developing a secure, intelligent flight delay prediction model integrated with anomaly detection. We utilize machine learning algorithms trained on historical and live flight datasets to forecast potential delays more accurately. In parallel, anomaly detection techniques are applied to identify sudden disruptions caused by factors like weather changes, air traffic fluctuations, or technical issues. The system is designed with a focus on data privacy and security, ensuring that sensitive flight and passenger information is handled responsibly. By integrating real-time data streams, robust ML models, and secure architecture, this project not only improves the accuracy of delay predictions but also contributes to the reliability and resilience of flight recommendation systems. The proposed solution has potential applications in airline management systems, travel platforms, and airport operations, offering a more dependable and passenger-friendly aviation experience. Furthermore, the system offers transparency in its predictions, providing users with clear insights into why a particular flight may be delayed.},
        keywords = {},
        month = {June},
        }