Anomaly Detection with Multiple Techniques using KDDCUP'99 Dataset.
The file inzva_week4_anomaly_detection.ipyb
- XGBoost
- Local Outlier Factor
- Isolation Forest
- Autoencoders
models. Each model is applied to detect specific Cyber Attack. This notebook is a part of the lectures I have given at inzva Applied AI study group. Whole repository is here: https://github.com/inzva/Applied-AI-Study-Group-2020-June
Lectures are available on YouTube in Turkish. You can see them from: https://www.youtube.com/watch?v=aSHM3NMZy2s&list=PLhnxo6HZwBgnTokZUfwM-sM0-DXRyp-rl&index=29&ab_channel=inzvateam.
The other file inzva_week2_anomaly_detection_plus_lightgbm.ipyb is from another batch of Applied AI program. This file has more coverage. It includes:
- XGBoost
- LightGBM
- CatBoost
- Hyperparameter Optimization Methods: RandomSearch and Bayesian Optimization
- Local Outlier Factor
- Isolation Forest
- Autoencoders
You can find the lectures on YouTube: https://www.youtube.com/watch?v=w70eDYefQEI&list=PLhnxo6HZwBgn6xBKvsx9bHzEVVZMO8qnn&ab_channel=inzvateam