Special Issue "Cyber-Security in Smart Grid"
A special issue of Information (ISSN 2078-2489).
Deadline for manuscript submissions: 30 June 2019
Dr. Linqiang Ge (Editor)
Department of Computer Science, Georgia Southwestern State University, 800 Georgia Southwestern State, University Drive, Americus, Georgia 31709, USA
Website | E-Mail
Interests: cyber security and information assurance; computer networks; cyber-physical systems; Internet of Things; big data analysis
The smart grid, as an important energy-based cyber-physical system, integrates cyber systems with physical power infrastructure and applies advanced information technologies in sensing, communications, computing, and control to improve efficiency, sustainability, reliability, intelligence, and resiliency of the power grid. The smart meters, information communication networks, and other cyber components are vitally important to establish the dynamic and interactive infrastructure of the smart grid. Nonetheless, this cyber system inevitably exposes the smart grid to a variety of cyber-attacks, targeting information security and privacy. For example, false data injection attacks could lead to disruptions in the operation of the smart grid, further causing performance to degrade and failures in the grid. Thus, it is essential to model and analyse the risks of cyber threats to the smart grid and design effective defensive schemes to improve smart grid security resilience.
This Special Issue aims to foster the dissemination of state-of-the-art research in cyber security for the smart grid, as well as address the privacy concerns of real-time and fine-grained sensing information in such a system. Topics of interest include (but are not limited to) the following subject categories:
- Theoretical Foundation and Models for Smart Grid Security and Privacy
- Big Data Analysis in Smart Grid Security and Privacy
- Security of Integrating Renewable Energy Resources in the Smart Grid
- Security of Electric Vehicles in the Smart Grid
- Security of Energy Management in the Smart Grid
- Secure Real-Time Communication in the Smart Grid
- Machine Learning for Smart Grid Security and Privacy
- Integrated Simulation and Testbed and Case Studies for Smart Grid Security and Privacy
- Attack Detection, Mitigation and Attribution in the Smart Grid
Dr. Wei Yu
Dr. Linqiang Ge
Dr. Guobin Xu
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Smart Grid
- Cyber Security
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Author: Jin (Wei)Kocsis
Affiliation: Electrical & Computer Engineering Department, University of Akron
We develop an intelligent situational awareness system for securing the energy management systems (EMS) of the hybrid auxiliary power unit (APU) of more-electric aircrafts (MEAs) in emergency. Our proposed situational awareness system mainly consists of two mechanisms: (1) deep learning based mechanism that detects the cyber attacks on the critical measurements of the APU, and (2) adaptive neuro-fuzzy inference system (ANFIS)-based estimation method to calculate the true values of the compromised measurements. Our simulation results evaluate the performance of our proposed method in detecting the cyber attacks, such as false data injection (FDI) attacks, and mitigating the impact of the cyber attacks in the energy management for the hybrid APUs in MEAs.