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.

翻译：网络入侵检测系统（NIDS）是识别网络中潜在威胁的重要工具。近年来，利用机器学习（ML）算法的不同流式NIDS设计被提出，作为高效检测入侵的潜在解决方案。然而，由于传统基于ML的分类器域适应能力较差，未能在实际场景中得到广泛应用。本研究旨在探索提升NIDS域适应能力的可能性。我们提出的方法采用了自然语言处理（NLP）技术和基于Transformer的双向编码器表示（BERT）框架。该方法在不同域的数据上测试时取得了积极的结果。