Introduction

We have applied unsupervised transfer learning approach to detect unseen anomalous traffic affected by the unseen attacks leveraging well-known unsupervised anomaly detection algorithms.

Data

We have leveraged the following three benchmarks:

First data is Available at: https://drive.google.com/drive/folders/1gfM5aKULWjnsjwHxOS_9MkZuEnIGoWip?usp=sharing

Second Data is Available at: https://drive.google.com/drive/folders/1IWF9gHyqhmYP1HfhYwi9nY6FnfNoKbOO?usp=sharing

Third Data is Available at: https://drive.google.com/drive/folders/1FYiK6JsD32glsYX-ZUwjNSqK0c7jFp5E

Code

First Data Application is available at: https://colab.research.google.com/drive/1pw0JuTZkkJaGNzxsRXniUEl2MZBFoqCD?usp=sharing

Second Data Application is available at: https://colab.research.google.com/drive/1hXd5jHoUICDTRY7ii3GJVml_895SitH_?usp=sharing

Third Data Application is available at : https://colab.research.google.com/drive/1gmC-bAoq6snAwM_pYSUnR-jPr4vCEULS?usp=sharing