Machine Learning (ML) has become ubiquitous, and its deployment in Network
Intrusion Detection Systems (NIDS) is inevitable due to its automated nature
and high accuracy in processing and classifying large volumes of data. However,
ML has been found to have several flaws, on top of them are adversarial
attacks, which aim to trick ML models into producing faulty predictions. While
most adversarial attack research focuses on computer vision datasets, recent
studies have explored the practicality of such attacks against ML-based network
security entities, especially NIDS.
This paper presents two distinct contributions: a taxonomy of practicality
issues associated with adversarial attacks against ML-based NIDS and an
investigation of the impact of continuous training on adversarial attacks
against NIDS. Our experiments indicate that continuous re-training, even
without adversarial training, can reduce the effect of adversarial attacks.
While adversarial attacks can harm ML-based NIDSs, our aim is to highlight that
there is a significant gap between research and real-world practicality in this
domain which requires attention.

本研究针对机器学习在网络入侵检测系统（NIDS）的部署中可能出现的对抗性攻击，提出一种与 NIDS 相关的优先分级体系，并对持续训练对对抗性攻击的影响进行了探索。研究表明，即使没有针对对抗性训练，持续重训可以减少对抗性攻击的影响。同时，本研究也强调了实际问题与研究之间存在的巨大差距，并呼吁对其给予更多关注。