Open yue123161 opened 3 years ago
Thank you for your interest in our paper. In Kitsune each autoencoder receives a subset of features as input but it reconstructs the same subset. So essentially each autoencoder would approximate an identity function. Whereas in our work the model is forced to reconstruct the hidden parts of the input based on the visible parts.
Thanks for you response Can you share the packet and flow preprocess code in this git
Your solution is so excellent.Can you share the packet and flow preprocess code in this git?
It is a good idea to use partial reconstruction error for anomaly detection. As you say in the paper, the RePo is compared with Kitsune, which is based on ensemble of autoencoders. But as i see in Kitsune Paper,they use a sub-space of features as input for each autoencoder. So what is the difference between partial reconstruction and subspace reconstruction.
Best Regards.