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项目作者： onurboyar

项目描述 ：
Anomaly Detection with Multiple Techniques using KDDCUP'99 Dataset
高级语言： Jupyter Notebook
项目地址: git://github.com/onurboyar/AnomalyDetection.git
创建时间： 2020-08-13T18:02:41Z
项目社区：https://github.com/onurboyar/AnomalyDetection
开源协议：
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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