Towards Fast and Semi-supervised Identification of Smart Meters Launching Data Falsification Attacks
Title | Towards Fast and Semi-supervised Identification of Smart Meters Launching Data Falsification Attacks |
Publication Type | Conference Paper |
Year of Publication | 2018 |
Authors | Bhattacharjee, Shameek, Thakur, Aditya, Das, Sajal K. |
Conference Name | Proceedings of the 2018 on Asia Conference on Computer and Communications Security |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-5576-6 |
Keywords | advanced metering infrastructure, anomaly detection, cyber-physical system security, data falsification, False Data Injection, false trust, Metrics, pubcrawl, Resiliency, Scalability, smart grid security |
Abstract | 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. |
URL | http://doi.acm.org/10.1145/3196494.3196551 |
DOI | 10.1145/3196494.3196551 |
Citation Key | bhattacharjee_towards_2018 |