arXiv subjects

Ioannis Zografopoulos

Publications and source records attributed to Ioannis Zografopoulos.

21 records · Page 2Linked to original sources

arXiv2020

Harness the Power of DERs for Secure Communications in Electric Energy Systems

Electric energy systems are undergoing significant changes to improve system reliability and accommodate increasing power demands. The penetration of distributed energy resources (DERs) including roof-top solar panels, energy storage, electric vehicles, etc., enables the on-site generation of economically dispatchable power curtailing operational costs. The effective control of DERs requires communication between utilities and DER system operators. The communication protocols employed for DER management and control lack sophisticated cybersecurity features and can compromise power systems secure operation if malicious control commands are issued to DERs. To overcome authentication-related protocol issues, we present a bolt-on security extension that can be implemented on Distributed Network Protocol v3 (DNP3). We port an authentication framework, DERauth, into DNP3, and utilize real-time measurements from a simulated DER battery energy storage system to enhance communication security. We evaluate our framework in a testbed setup using DNP3 master and outstation devices performing secure authentication by leveraging the entropy of DERs.

cs.CR↗

arXiv2020

A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems

Over the last decade, the number of cyberattacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, False Data Injection Attacks (FDIAs) is a class of cyberattacks against power grid monitoring systems. Adversaries can successfully perform FDIAs in order to manipulate the power system State Estimation (SE) by compromising sensors or modifying system data. SE is an essential process performed by the Energy Management System (EMS) towards estimating unknown state variables based on system redundant measurements and network topology. SE routines include Bad Data Detection (BDD) algorithms to eliminate errors from the acquired measurements, e.g., in case of sensor failures. FDIAs can bypass BDD modules to inject malicious data vectors into a subset of measurements without being detected, and thus manipulate the results of the SE process. In order to overcome the limitations of traditional residual-based BDD approaches, data-driven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation of sensor data due to their fast execution times and accurate results. This paper provides a comprehensive review of the most up-to-date machine learning methods for detecting FDIAs against power system SE algorithms.

eess.SY↗

arXiv2020

DERauth: A Battery-based Authentication Scheme for Distributed Energy Resources

Over the past decades, power systems have experienced drastic transformations in order to address the growth in energy demand, reduce carbon emissions, and enhance power quality and energy efficiency. This shift to the smart grid concept involves, among others, the utilization of distributed energy resources (DERs) such as rooftop solar panels and storage systems, contributing towards grid decentralization while improving control over power generation. In order to seamlessly integrate DERs into power systems, embedded devices are used to support the communication and control functions of DERs. As a result, vulnerabilities of such components can be ported to the industrial environment. Insecure control networks and protocols further exacerbate the problem. Towards reducing the attack surface, we present an authentication scheme for DERs, DERauth, which leverages the inherent entropy of the DER battery energy storage system (BESS) as a root-of-trust. The DER authentication is achieved using a challenge-reply mechanism that relies on the corresponding DER's BESS state-of-charge (SoC) and voltage measurements. A dynamically updating process ensures that the BESS state is up-to-date. We evaluate our proof-of-concept in a prototype development that uses lithium-ion (li-ion) batteries for the BESS. The robustness of our design is assessed against modeling attacks performed by neural networks.

cs.CR↗