Accurate building energy prediction is useful in various applications starting from building energy automation and management to optimal storage control. However, vulnerabilities should be considered when designing building energy prediction models, as intelligent attackers can deliberately influence the model performance using sophisticated attack models. These may consequently degrade the prediction accuracy, which may affect the efficiency and performance of the building energy management systems. In this paper, we investigate the impact of bi-level poisoning attacks on regression models of energy usage obtained from household appliances. Furthermore, an effective countermeasure against the poisoning attacks on the prediction model is proposed in this paper. Attacks and defenses are evaluated on a benchmark dataset. Experimental results show that an intelligent cyber-attacker can poison the prediction model to manipulate the decision. However, our proposed solution successfully ensures defense against such poisoning attacks effectively compared to other benchmark techniques.
翻译:准确的建筑能源预测有助于从建设能源自动化和管理到优化储存控制等各种应用,但是,在设计能源预测模型时应考虑到脆弱性,因为智能攻击者可以使用复杂的攻击模型故意影响模型性能,从而降低预测的准确性,可能影响建筑能源管理系统的效率和性能。在本文中,我们调查双级中毒袭击对家用电器能源使用回归模型的影响。此外,本文件还提议对预测模型的中毒袭击采取有效的应对措施。在基准数据集上评估攻击和防御。实验结果表明,智能网络攻击者可以毒化预测模型,操纵决定。然而,我们提出的解决方案成功地确保与其他基准技术相比,有效防范此类中毒袭击。