New backdoor attacks threaten AI fault detection in critical infrastructure

Researchers have detailed a new type of backdoor attack targeting machine learning models used for fault detection in cyber-physical systems. These attacks involve subtly poisoning the training data with specific patterns, causing the model to misbehave only when these triggers are present. The study demonstrates that even a 10% data poisoning rate can be effective in compromising these critical systems, which are vital for infrastructure like smart grids and industrial automation. AI

IMPACT Highlights the vulnerability of AI in critical infrastructure, necessitating robust defenses against adversarial attacks.

RANK_REASON The cluster contains an academic paper detailing a new type of attack on AI systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Adversarial Machine Learning
• Backdoor Attacks
• Cyber-Physical Systems
• Deep Learning
• Industrial Automation
• Machine Learning
• Smart Grids

• paper
• safety

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