Browsing by Subject "Xgboost"
Now showing 1 - 1 of 1
- Results Per Page
- Sort Options
Item Using machine learning to detect web application attacks(2020-12-02) Franklin, Robin Maria; Caramanis, ConstantineWith the increased ease in cloud deployment platforms, web applications have become an easy target for cyber-criminals and state-sponsored hackers. In this paper, I propose a detection solution to help identify network traffic generated by web application attacks. My experimental results reveal that the gradient boosting models, like LightGBM and XGBoost, all yielded extremely high ROC-AUC scores above .98. When compared to traditional anomaly detection models, like K-nearest neighbors, the ROC-AUC scores are higher and training times are much faster. Finally, I also identified several network data features like the window length in bytes, packet length, and flow duration that are critical when identifying web application attacks using network traffic data.