An Edge-based Architecture for Phasor Measurements in Smart Grids

This paper investigates the application of Kubernetes and Edge computing technologies to operate IT services in the context of power systems and smart grids. Traditional services for grid monitoring such as Phasor Measurement Units (PMUs) and Phasor Data Concentrators (PDCs) require a centralized architecture and a rigid networking infrastructure in order to properly function, which today is only achieved at the High Voltage (HV) transmission level. Furthermore, manual intervention is often the only option for PMUs/PDCs maintenance. In this work, the traditional PMU/PDC services were deployed as docker-containers in a decentralized Kubernetes cluster, which can represent any kind of geographically dispersed TCP/IP network. By leveraging remote orchestration, several key benefits are achieved: (1) no manual reconfiguration of the PMU-PDC communications upon network reconfiguration, (2) automatic PMU traffic redirection in case of PDC service redeployment in a different location, and (3) reduced data-loss upon PDC failure and enhanced overall system resiliency due to minimized ICT services down-time

A Bayesian Network Approach for the Interpretation of Cyber Attacks to Power Systems

The focus of this paper is on the analysis of the cyber security resilience of digital infrastructures deployed by power grids, internationally recognized as a priority since several recent cyber attacks targeted energy systems and in particular the power service. In response to the regulatory framework, this paper presents an analysis approach based on the Bayesian Networks formalism and on real world threat scenarios. Our approach enables analyses oriented to planning of security measures and monitoring, and to forecasting of adversarial behaviours

Analisi e rilevamento intelligente di processi di attacco alle Smart-Grid

Proponiamo una metodologia basata sulle Reti Bayesiane come strumento di supporto all’analisi della sicurezza di Smart Grid, ed in particolare per la previsione di intrusioni e attività ostili

A Modular Infrastructure for the Validation of Cyberattack Detection Systems

We propose a framework for the evaluation of cyberattack detection systems in which theoretical results can be tested in a realistic setup. We emulate a power control infrastructure, an attacker and a monitoring system. In this controlled environment, through a modular approach, it is possible to evaluate a variety of detection models: we inject adversarial activity, collect logs from the systems, analyze such logs and produce evidences that are later processed by artificial intelligence models that can raise alerts, and give diagnostic or predictive information. In particular, we test our framework with detection models based on Dynamic Bayesian Networks, that take into account the evolution of adversarial activities over time. The testbed allows us to effectively test the adequacy of the detection mechanisms for early warning of suspicious events; currently, it includes man-in-the-middle attacks and false data injection