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News/Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems
arxiv
PublishedJune 12, 2026 at 4:00 AM
—neutral

Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.12075v1 Announce Type: cross Abstract: Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks. These attacks add carefully crafted perturbations to network traffic data that leads to misclassifications. Whil

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Discussion
Mentioned models
03
  • 01
    1D Convolutional Neural Network
  • 02
    Long Short-Term Memory (LSTM) network
  • 03
    Random Forest (RF) ensemble
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#adversarial-attacks#network-intrusion-detection#machine-learning#security

No replies yet. Be first.

Mentioned models
03
  • 01
    1D Convolutional Neural Network
  • 02
    Long Short-Term Memory (LSTM) network
  • 03
    Random Forest (RF) ensemble
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#adversarial-attacks#network-intrusion-detection#machine-learning#security

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arxivMAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation1harxivCounterfactual Model Testing Under Distribution Shift1harxivCentralized Critics for N-Agent Ad Hoc Teamwork1harxivWhere and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning1h
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