Does Demand Response Increase Vulnerability to Cyber Attacks by Adversarial Data Modifications?
arXiv:2607.06632v1 Announce Type: new Abstract: Adversarial attacks are crafted data manipulations that aim to deteriorate the outcomes of prediction or decision-making algorithms. In the energy systems literature, adversarial attacks have been studied with a focus on problems regarding the electricity grid. Such problems include forecasting and grid state estimation, where adversarial attacks are also known as false data injection attacks. Only few studies have analyzed the potential impact that adversarial attacks have on the demand side. We analyze how manipulated price forecasts impact the
The increasing reliance on AI and data-driven systems for critical infrastructure like energy grids makes them prime targets for sophisticated adversarial attacks.
This research highlights a growing vulnerability in smart grids, indicating that optimization for efficiency can inadvertently create new attack surfaces with significant societal and economic consequences.
The focus expands from traditional grid state estimation to the demand side, suggesting that consumers and businesses implementing demand response will also need robust cybersecurity measures.
- · Cybersecurity firms specializing in critical infrastructure
- · Developers of robust AI/ML for grid resilience
- · Energy utilities with weak cyber defenses
- · Consumers relying solely on unhardened demand response systems
Increased cybersecurity investment becomes mandatory for energy grid operators and demand response participants.
New regulatory frameworks emerge to mandate cybersecurity standards for smart grid components and AI-driven energy management systems.
The development of AI for grid optimization is coupled with adversarial AI defenses, creating a cybersecurity arms race within energy systems.
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Read at arXiv cs.LG