| Title | Detectors of Smart Grid Integrity Attacks: an Experimental Assessment |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Bernardi, Simona, Javierre, Raúl, Merseguer, José, Requeno, José Ignacio |
| Conference Name | 2021 17th European Dependable Computing Conference (EDCC) |
| Keywords | anomaly-based detection, ARIMA, Artificial neural networks, Biological system modeling, clustering, Damage Assessment, Detectors, Europe, integrity attacks, Neural networks, Predictive models, pubcrawl, Resiliency, Smart grids, smart meters, Software |
| Abstract | Today cyber-attacks to critical infrastructures can perform outages, economical loss, physical damage to people and the environment, among many others. In particular, the smart grid is one of the main targets. In this paper, we develop and evaluate software detectors for integrity attacks to smart meter readings. The detectors rely upon different techniques and models, such as autoregressive models, clustering, and neural networks. Our evaluation considers different "attack scenarios", then resembling the plethora of attacks found in last years. Starting from previous works in the literature, we carry out a detailed experimentation and analysis, so to identify which "detectors" best fit for each "attack scenario". Our results contradict some findings of previous works and also offer a light for choosing the techniques that can address best the attacks to smart meters. |
| DOI | 10.1109/EDCC53658.2021.00018 |
| Citation Key | bernardi_detectors_2021 |
Detectors of Smart Grid Integrity Attacks: an Experimental Assessment