Integration of complex and high-speed electronic components in the state of art electric power system enhances the need for improved security infrastructure and resilience against invasive and non-invasive attacks on the smart grid. A modern smart grid system integrates a variety of instruments and standards to achieve cost-effective and time-effective energy measurement and management. As the fundamental component in the smart grid, the smart meter supports real-time monitoring, automatic control, and high-speed communication along with power consumption recording. However, the wide use of smart meters also increases privacy and security concerns. In this paper, we demonstrate the vulnerability of side-channel attacks on secure communication in smart grids for software-based and hardware-based implementations.
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False data injection attacks against contingency analysis in power grids: poster
The smart grid provides efficient and cost-effective management of the electric energy grid by allowing real-time monitoring, coordinating, and controlling the system using communication networks between physical components. This inherent complexity significantly increases the vulnerabilities and attack surface in the smart grid due to misconfigurations or the lack of security hardening. Therefore, it is important to ensure a secure and resilient operation of the smart grid by proactive identification of potential threats, impact assessment, and cost-efficient mitigation planning. This paper aims to achieve these goals through the development of an efficient security framework for the Energy Management System (EMS), a core smart grid component. In this paper, we present a framework that combines formal analytic with PowerWorld simulator which verifies the solution model to investigate the feasibility of false data injection attacks against contingency analysis in the power grid. We evaluate the impact of such attacks by running experiments using synthetic data on the standard IEEE test cases.
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- Award ID(s):
- 1929183
- NSF-PAR ID:
- 10145191
- Date Published:
- Journal Name:
- ACM Conference on Security and Privacy in Wireless and Mobile Networks
- Page Range / eLocation ID:
- 343 to 344
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Smart grid is an intelligent cyber physical system (CPS). The CPS generates a massive amount of data for efficient grid operation. In this paper, a big data‐driven, cloud‐based information and communication technology (ICT) framework for smart grid CPS is proposed. The proposed ICT framework deploys hybrid cloud servers to enhance scalability and reliability of smart grid communication infrastructure. Because the data in the ICT framework contains much privacy of customers and important data for automated controlling, the security of data transmission must be ensured. In order to secure the communications over the Internet in the system, identity‐based schemes are proposed especially because of their advantage in key management. Specifically, an identity‐based signcryption (IBSC) scheme is proposed to provide confidentiality, non‐repudiation, and data integrity. For practical purposes, an identity‐based signature scheme is relaxed from the proposed IBSC to provide non‐repudiation only. Moreover, identity‐based schemes are also proposed to achieve signature delegation within the ICT framework. Security of the proposed IBSC scheme is rigorously analyzed in this work. Efficiency of the proposed IBSC scheme is demonstrated with an implementation using modified Weil pairing over an elliptic curve. Copyright © 2016 John Wiley & Sons, Ltd.
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null (Ed.)In recent years, smart grid communications (SGC) has evolved to use new technologies not only for data delivery but also for enhanced smart grid (SG) security and reliability. Software Defined Networks (SDN) has proved to be a reliable and efficient architecture for handling diverse communication systems due to their ability to divide responsibilities of the network using control plane and data plane. This paper presents a graph learning approach for detecting and identifying Distributed Denial of Service (DDoS) attacks in SDN-SGC systems (GLASS). GLASS is a two phase framework that (1) detects if SDN-SGC is under DDoS attack using supervised graph deep learning and then (2) identifies the compromised entities using unsupervised learning methods. Network performance statistics are used for modeling SDN-SGC graphs, which train Graph Convolutional Neural Networks (GCN) to extract latent representations caused by DDoS attacks. Finally, spectral clustering is used to identify compromised entities. The experimental results, obtained by analysis of an IEEE 118-bus system, show the average throughput for compromised entities is able to maintain 84% of normal traffic level with GLASS, compared to achieving only 4% of normal throughput caused by DDoS attacks on compromised entities without the GLASS framework.more » « less
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Optimal Power Flow (OPF) is a crucial part of the Energy Management System (EMS) as it determines individual generator outputs that minimize generation cost while satisfying transmission, generation, and system level operating constraints. OPF relies on a core EMS routine, namely state estimation, which computes system states, principally bus voltages/phase angles at the buses. However, state estimation is vulnerable to false data injection attacks in which an adversary can alter certain measurements to corrupt the estimator's solution without being detected. It is also shown that a stealthy attack on state estimation can increase the OPF cost. However, the impact of stealthy attacks on the economic and secure operation of the system cannot be comprehensively analyzed due to the very large size of the attack space. In this paper, we present a hybrid framework that combines formal analytics with Simulink-based system modeling to investigate the feasibility of stealthy attacks and their influence on OPF in a time-efficient manner. The proposed approach is illustrated on synthetic case studies demonstrating the impact of stealthy attacks in different attack scenarios. We also evaluate the impact analysis time by running experiments on standard IEEE test cases and the results show significant scalability of the framework.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3317549.3326323
