onurboyar / anomalydetection

Anomaly Detection with Multiple Techniques using KDDCUP'99 Dataset

AnomalyDetection

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