Training an LSTM-based autoencoder to detect anomalies in the KDD99 network traffic dataset.
In this project, we will create and train an LSTM-based autoencoder to detect anomalies in the KDD99 network traffic dataset. For this task, you should use the
Note that KDD99 does not include timestamps as a feature. The simplest approach to making these discrete datapoints into time-domain data is to assume that each datapoint occurs at the timestep immediately after the previous datapoint. However, more sophisticated approaches can also be adopted (e.g., grouping by TCP connections). The choice of serialization technique (i.e., conversion into time-domain) is up to you.
This project must be implemented in a Jupyer Notebook, and must be compatible with Google Colab (i.e., if you are using a particular library that is not on Colab by default, your notebook must install it via !pip install ... ). Your notebook must also contain a section on performance analysis, where you report the performance of your IDS model via performance metrics and/or plots (similar to Section 5). the A very important note: You should not expect very high detection rates from the model.
Happy Coding!!!!!