Evasion Attacks on Cost-Utility-Based Adversarial Training for Online AutoML in IoT Networks

arXiv:2609.31981 · cs.CR, cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.CR, cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/ChiNonsoHenry16/AutoML-AT-O

Terminology

• Accuracy — 23× in this paper · explained in 1 episode
• Robustness — 18× in this paper · explained in 5 episodes
• Concept Drift — 9× in this paper · explained in 4 episodes
• Adaptation — 7× in this paper · explained in 1 episode
• Adversarial Robustness — 5× in this paper · explained in 3 episodes
• Leveraging Bagging — 5× in this paper
• Adaptive Random Forest — 3× in this paper
• Adversarial Training — 3× in this paper · explained in 1 episode
• Feature Engineering — 3× in this paper · explained in 3 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Adaptive Random Forest (ARF) — 2× in this paper · explained in 1 episode
• Adversarial Examples — 2× in this paper · explained in 2 episodes
• Early Drift Detection Method — 2× in this paper
• Hoeffding Adaptive Tree — 2× in this paper
• In Internet of Things — 2× in this paper
• Streaming Random Patches — 2× in this paper
• Adaptability — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Adversarial Model — 1× in this paper · explained in 1 episode
• Adversarial Training (AT) — 1× in this paper · explained in 1 episode
• Bayesian Optimization — 1× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• Hoeffding Tree — 1× in this paper
• Information Gain — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes

Sources

• Adversarial Robustness for Tabular Data through Cost and Utility Awareness
• Explaining and Harnessing Adversarial Examples
• Towards Deep Learning Models Resistant to Adversarial Attacks
• Adversarial Examples Are Not Bugs, They Are Features

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