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@article{188783,
        author = {Nagarjun 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 = {7},
        pages = {3508-3512},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188783},
        abstract = {Flight delays have long posed a challenge to the aviation industry, disrupting schedules, increasing operating costs, and frustrating passengers. Most current prediction systems rely solely on historical data and rarely adapt to real-time conditions or consider cybersecurity risks. This paper presents a Secure Flight Delay Prediction System integrated with Anomaly Detection. The model leverages both historical and real-time data to forecast potential delays accurately while safeguarding sensitive flight information. Supervised machine-learning models— Random Forest, XGBoost, and LSTM—predict probable delays, while unsupervised algorithms such as Isolation Forest and One-Class SVM detect irregular operational anomalies. The proposed system demonstrates improved accuracy and robustness compared with conventional models and introduces a transparent, user-friendly interface for airlines, airports, and passengers.},
        keywords = {Flight Delay Prediction, Machine Learning, Anomaly Detection, Data Security, Real-Time Systems, Aviation Analytics},
        month = {December},
        }