FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems
arXiv:2606.03430v1 Announce Type: cross Abstract: Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) deployed in energy infrastructure are vulnerable to model theft attacks, which allow adversaries to create evasive traffic offline. Current defences against model extraction rely either on identity-bound query monitoring, which is ineffective against distributed attackers (Sybil), or on prediction poisoning through soft-label perturbation, which is inapplicable to hard-label IDS deployments. Therefore, we propose FlowGuard, an identity-independent defence based on flow matchin
The increasing deployment of AI-based Intrusion Detection Systems in critical energy infrastructure necessitates robust defenses against sophisticated model theft attacks.
This research addresses a critical vulnerability in AI-powered cybersecurity for energy systems, ensuring the integrity and reliability of essential infrastructure against evolving threats.
The introduction of identity-independent defense mechanisms changes the landscape of protecting AI models from data-free stealing attacks, making traditional distributed attack methods less effective.
- · Energy sector operators
- · Cybersecurity firms
- · AI defense researchers
- · Critical infrastructure providers
- · Adversarial hacking groups
- · Model stealing attackers
Increased resilience of energy infrastructure against AI-driven cyber threats.
Accelerated adoption of advanced, identity-independent AI security protocols across critical infrastructure sectors beyond energy.
Deterrence of nation-state actors from developing or deploying certain AI model stealing tactics due to heightened defense capabilities.
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Read at arXiv cs.AI