Towards Fast and Semi-supervised Identification of Smart Meters Launching Data Falsification Attacks
Towards Fast and Semi-supervised Identification of Smart Meters Launching Data Falsification Attacks
复制标题
实现对发起数据伪造攻击的智能电表的快速半监督识别
DOI：
10.1145/3196494.3196551
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Sajal K. Das
中科院分区：
文献类型：
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作者：
Shameek Bhattacharjee;Aditya Thakur;Sajal K. Das
在高级计量基础设施（AMI）中，被入侵的智能电表发送虚假的电力消耗数据可能会对智能电网的运行产生严重后果。大多数现有的防御模型仅使用有监督分类技术处理单个客户的窃电行为（孤立攻击），无法提供可扩展或实时的解决方案。此外，AMI的网络和互联特性也可能被有组织的攻击者利用，他们在入侵多个电表后有能力策划同时的数据篡改攻击，并且其目标比单纯的窃电更为复杂。在本文中，我们首先提出一种基于实时半监督异常的一致性校正技术，该技术可检测智能电表数据篡改的存在和类型，然后相应地进行一致性校正。随后，我们提出一种基于半监督一致性的信任评分模型，该模型能够识别注入虚假数据的智能电表。所提出方法的主要贡献在于为被入侵的智能电表识别提供一个实用框架，该框架（i）无监督，（ii）能够快速识别，（iii）对于较大规模的AMI能更好地控制分类错误率；（iv）应对孤立攻击和协同攻击的威胁；以及（v）同时适用于多种数据篡改类型。使用来自美国和爱尔兰的两个真实数据集进行的大量实验验证，证明了我们所提出的方法能够在不同数据集上近乎实时地识别被入侵的电表。
Compromised smart meters sending false power consumption data in Advanced Metering Infrastructure (AMI) may have drastic consequences on the smart grid»s operation. Most existing defense models only deal with electricity theft from individual customers (isolated attacks) using supervised classification techniques that do not offer scalable or real time solutions. Furthermore, the cyber and interconnected nature of AMIs can also be exploited by organized adversaries who have the ability to orchestrate simultaneous data falsification attacks after compromising several meters, and also have more complex goals than just electricity theft. In this paper, we first propose a real time semi-supervised anomaly based consensus correction technique that detects the presence and type of smart meter data falsification, and then performs a consensus correction accordingly. Subsequently, we propose a semi-supervised consensus based trust scoring model, that is able to identify the smart meters injecting false data. The main contribution of the proposed approach is to provide a practical framework for compromised smart meter identification that (i) is not supervised (ii) enables quick identification (iii) scales classification error rates better for larger sized AMIs; (iv) counters threats from both isolated and orchestrated attacks; and (v) simultaneously works for a variety of data falsification types. Extensive experimental validation using two real datasets from USA and Ireland, demonstrates the ability of our proposed method to identify compromised meters in near real time across different datasets.