Online AutoML: Evaluating Poisoning Attacks on Cost-Utility Adversarial Training Defence Strategy for IoT Networks

arXiv:2610.05810 · cs.CR, cs.LG · Submitted 2026-10-05 · Read on arXiv

cs.CR, cs.LG

Submitted: 2026-10-05

Updated: 2026-10-11

Comments: Accepted and to appear in IEEE CASCON 2026. Code is available at: https://github.com/ChiNonsoHenry16/Label-Flip-NoiseInjection_OnAutoML

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adversarial Training — 1× in this paper · explained in 1 episode

Sources

• Poisoning Attacks against Support Vector Machines
• Adversarial Robustness for Tabular Data through Cost and Utility Awareness
• Explaining and Harnessing Adversarial Examples
• Towards Deep Learning Models Resistant to Adversarial Attacks

Related papers

• SoK: AI-Augmented Binary Reversing
• Relaxed Sender Anonymity for CBDC Interbank Settlement: A Zero-Knowledge Approach on Permissioned EVM
• Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages
• Efficient Fuzzy PSI under One-Sided Assumptions
• Sealing the Audit-Runtime Gap for LLM Skills
• Token Composition: A Graph Based on EVM Logs