  A Network Intrusion Detection System (NIDS) is an important tool that
identifies potential threats to a network. Recently, different flow-based NIDS
designs utilizing Machine Learning (ML) algorithms have been proposed as
potential solutions to detect intrusions efficiently. However, conventional
ML-based classifiers have not seen widespread adoption in the real-world due to
their poor domain adaptation capability. In this research, our goal is to
explore the possibility of improve the domain adaptation capability of NIDS.
Our proposal employs Natural Language Processing (NLP) techniques and
Bidirectional Encoder Representations from Transformers (BERT) framework. The
proposed method achieved positive results when tested on data from different
domains.
