Towards Privacy-preserving Anomaly-based Attack Detection against Data Falsification in Smart Grid
Towards Privacy-preserving Anomaly-based Attack Detection against Data Falsification in Smart Grid
复制标题
针对智能电网中数据篡改的隐私保护异常攻击检测
DOI：
10.1109/smartgridcomm47815.2020.9303009
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Sajal K. Das
中科院分区：
文献类型：
--
作者：
Yu Ishimaki;Shameek Bhattacharjee;H. Yamana;Sajal K. Das
在智能电网高级计量基础设施(AMI)中，提出了一种基于隐私保护的异常数据篡改攻击检测框架。具体地，我们提出了一个针对同态加密数据的异常检测框架。与现有的隐私保护异常检测器不同，我们的框架不仅可以检测到能量盗窃(即演绎攻击)的存在，还可以检测到针对加密数据的更高级的数据完整性攻击(即加法攻击和伪装攻击)，而不会降低检测敏感度。我们对异常检测过程进行了优化，以避免在同态加密空间上进行可能代价高昂的操作。此外，我们优化了针对智能电表等资源受限设备设计的加密方法，完成加密的时间比天真地采用加密方法快40倍。我们还使用来自智能计量基础设施的真实数据集对该框架进行了验证，并证明了该框架可以在不牺牲用户隐私的情况下以高敏感度检测数据完整性攻击。对德克萨斯州一个AMI的200套房屋的真实数据集进行的实验结果表明，明文算法的检测灵敏度不会因为使用同态加密而降低。
In this paper, we present a novel framework for privacy-preserving anomaly-based data falsification attack detection in a smart grid advanced metering infrastructure (AMI). Specifically, we propose an anomaly detection framework over homomorphically encrypted data. Unlike existing privacy-preserving anomaly detectors, our framework detects the presence of not only energy theft (i.e., deductive attack), but also more advanced data integrity attacks (i.e., additive and camouflage attacks) over encrypted data without diminishing detection sensitivity. We optimize the anomaly detection procedure such that potentially expensive operations over homomorphically encrypted space are avoided. Moreover, we optimize the encryption method designed for a resource constrained device such as smart meters, and the time to complete encryption gets 40x faster over the naïve adoption of the encryption method. We also validate the proposed framework using a real dataset from smart metering infrastructures, and demonstrate that the data integrity attacks can be detected with high sensitivity, without sacrificing user privacy. Experimental results with a real dataset of 200 houses from an AMI in Texas showed that the detection sensitivity of the plaintext algorithm is not degraded due to the use of homomorphic encryption.
智能计量基础设施中隐形数据篡改的检测和取证
影响因子：
7.3
作者：
Bhattacharjee, Shameek;Das, Sajal K.
通讯作者：
Das, Sajal K.