Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space. (arXiv:2312.17300v1 [cs.CR])

Domain generalization focuses on leveraging knowledge from multiple related
domains with ample training data and labels to enhance inference on unseen
in-distribution (IN) and out-of-distribution (OOD) domains. In our study, we
introduce a two-phase representation learning technique using multi-task
learning. This approach aims to cultivate a latent space from features spanning
multiple domains, encompassing both native and cross-domains, to amplify
generalization to IN and OOD territories. Additionally, we attempt to
disentangle the latent space by minimizing the mutual information between …

data detection distribution domain domains intrusion intrusion detection knowledge representation space study task training training data