Adversarial Machine Learning in Smart Energy Systems
Adversarial Machine Learning in Smart Energy Systems
复制标题
智能能源系统中的对抗性机器学习
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
U. Roedig
中科院分区：
文献类型：
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作者：
Martin C. Bor;Angelos K. Marnerides;A. Molineux;S. Wattam;U. Roedig
智能能源系统代表了能源生产和需求方法的根本转变，这是由能源系统分散到大量低容量设备驱动的。管理这种灵活性通常由机器学习驱动，需要实时控制和聚合这些设备，涉及各种公司和设备，并创建更长的信任链。这会带来安全风险，因为它对对抗性机器学习很敏感，模型会通过恶意输入被愚弄，无论是为了经济利益还是导致系统中断。我们通过分析一个真实的系统的经验数据，这种攻击的可行性，并提出了未来的研究方向，这些攻击的检测和防御机制。
Smart Energy Systems represent a radical shift in the approach to energy generation and demand, driven by decentralisation of the energy system to large numbers of low-capacity devices. Managing this flexibility is often driven by machine learning, and requires real-time control and aggregation of these devices, involving a diverse set of companies and devices and creating a longer chain of trust. This poses a security risk, as it is sensitive to adversarial machine learning, whereby models are fooled through malicious input, either for financial gain or to cause system disruption. We show the feasibility of such an attack by analysing empirical data of a real system, and propose directions for future research related to detection and defence mechanisms for these kind of attacks.