“What is the Problem Space?” Defining Host-space Adversarial Perturbations against Network Intrusion Detection Systems
Abstract
Network Intrusion Detection Systems (NIDS) are now increasingly leveraging Machine Learning (ML) techniques to detect malicious network activities. Numerous papers have scrutinized the security of ML-based NIDS (ML-NIDS) by testing them against various attacks involving adversarial perturbations. The findings were oftentimes worrying: by making imperceptible changes to a given input, powerful ML models would be bypassed. In this context, we took a step back and wondered: where (i.e., in what “space”) have these perturbations been applied?
Fields of Study & Subjects
Network Security and Intrusion Detection
Internet Traffic Analysis and Secure E-voting
Software-Defined Networks and 5G
Publication details
- Journal / source
- Proceedings of the ACM Asia Conference on Computer and Communications Security
- Publisher
- ACM
- Pages
- 1043–1059
- Publication date
- 1 June 2026