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Gudipati, Raga Malika (2024) DDoS attacks on airlines and Mitigation techniques using Artificial Intelligence aided system. Masters thesis, Dublin, National College of Ireland.
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Abstract
Increased DDoS attacks on airlines underscore the need for improved defence measures to preserve operational continuity and protect critical data. Recently, fraudsters have targeted airlines with DDoS attacks, which can overrun networks, halt flights, and disrupt ticketing and booking systems. AI (Artificial Intelligence) – powered solutions can help identify and stop airline DDoS attacks. Real-time network traffic monitoring can detect DDoS attacks, which can trigger artificial intelligence to reroute traffic or construct firewalls. Machine Learning (ML, a part of AI) algorithms can identify regular traffic baselines and immediately warn about unexpected surges, enabling predictive analysis. AI in aviation cyber security frameworks, industry stakeholder collaboration, and rising standards can help airlines defend against threats faster making air travel safer and more reliable. In this work, various machine learning techniques like Decision Tree Classifier, Random Forest Classifier, K-Nearest Neighbors Classifier, Tabular Neural Network, CNN-GRU Architecture and XGBoost are applied to this problem using publicly available CIC IOT datasets. The performance of the different machine learning techniques are compared based on figures of Accuracy, F1 score, precision and recall.
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