  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 the
prior and latent space, effectively de-correlating spurious feature
correlations. Collectively, the joint optimization will facilitate
domain-invariant feature learning. We assess the model's efficacy across
multiple cybersecurity datasets, using standard classification metrics on both
unseen IN and OOD sets, and juxtapose the results with contemporary domain
generalization methods.
