  Network traffic is growing at an outpaced speed globally. The modern network
infrastructure makes classic network intrusion detection methods inefficient to
classify an inflow of vast network traffic. This paper aims to present a modern
approach towards building a network intrusion detection system (NIDS) by using
various deep learning methods. To further improve our proposed scheme and make
it effective in real-world settings, we use deep transfer learning techniques
where we transfer the knowledge learned by our model in a source domain with
plentiful computational and data resources to a target domain with sparse
availability of both the resources. Our proposed method achieved 98.30%
classification accuracy score in the source domain and an improved 98.43%
classification accuracy score in the target domain with a boost in the
classification speed using UNSW-15 dataset. This study demonstrates that deep
transfer learning techniques make it possible to construct large deep learning
models to perform network classification, which can be deployed in the real
world target domains where they can maintain their classification performance
and improve their classification speed despite the limited accessibility of
resources.
