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Über dieses Buch

This book targets the key concern of protecting critical infrastructures such as smart grids. It explains various static and dynamic security analysis techniques that can automatically verify smart grid security and resiliency and identify potential attacks in a proactive manner. This book includes three main sections. The first presents the idea of formally verifying the compliance of smart grid configurations with the security and resiliency guidelines. It provides a formal framework that verifies the compliance of the advanced metering infrastructure (AMI) configurations with the security and resiliency requirements, and generates remediation plans for potential security violations. The second section covers the formal verification of the security and resiliency of smart grid control systems by using a formal model to analyze attack evasions on state estimation, a core control module of the supervisory control system in smart grids. The model identifies attack vectors that can compromise state estimation. This section also covers risk mitigation techniques that synthesize proactive security plans that make such attacks infeasible. The last part of the book discusses the dynamic security analysis for smart grids. It shows that AMI behavior can be modeled using event logs collected at smart collectors, which in turn can be verified using the specification invariants generated from the configurations of the AMI devices. Although the focus of this book is smart grid security and resiliency, the included formal analytics are generic enough to be extended to other cyber-physical systems, especially those related to industrial control systems (ICS). Therefore, industry professionals and academic researchers will find this book an exceptional resource to learn theoretical and practical aspects of applying formal methods for the protection of critical infrastructures.

Inhaltsverzeichnis

Frontmatter

Introduction

Frontmatter

Chapter 1. Smart Grids and Security Challenges

Abstract
Smart grids are the modernization of the legacy power systems with the development of communication infrastructures. They are perfect examples of cyber-physical systems (CPS). To delineate the importance of the safety and reliability of smart grids, the Schneider Electric report in June 2010 can be cited [4]: “The financial impact of power disruption was demonstrated during the August 2003 blackout, which affected 45 million people in eight US states and 10 million people in parts of Canada. Healthcare facilities experienced hundreds of millions of dollars in lost revenue from canceled services, legal liability, and damaged reputations. Six hospitals were in bankruptcy one year later.” This incident clearly illustrates the extent of the impact due to operational interruption in energy networks.
Ehab Al-Shaer, Mohammad Ashiqur Rahman

Chapter 2. Analytics for Smart Grid Security and Resiliency

Abstract
The security and resiliency analysis of a smart grid needs to consider the target component(s), flexible attack model, and the integration among different smart grid components and attack properties. An exhaustive security analysis is not only expensive but also infeasible using testbeds. Formal analytics can play an important role toward comprehensive security analysis of the system, which can identify potential threats provably, that can further be verified on testbeds.
Ehab Al-Shaer, Mohammad Ashiqur Rahman

Formal Analytics for Secure and Resilient Smart Grids

Frontmatter

Chapter 3. Security Analytics for AMI and SCADA

Abstract
The correct functioning of a smart grid stands on consistent and secure execution of tasks in time. The safe security configuration depends not only on the local device parameters but also on the secure interactions and flows of these parameters across the network. There is a significant number of logical constraints on configuration parameters of many smart grid devices, which need to be satisfied to ensure safe and secure communications among smart grid components. NIST has developed security guidelines (e.g., NISTIR 7628 and NIST SP 800-82  [4, 10]) consisting of hundreds of security controls for ensuring trusted path, resource availability, boundary security protection, etc., toward controlling different security threats on smart grids. Implementing these security controls in a scalable manner is one of the major challenges for analyzing smart grid security and resiliency.
Ehab Al-Shaer, Mohammad Ashiqur Rahman

Chapter 4. Security Analytics for EMS Modules

Abstract
In modern energy control centers, the energy management system (EMS) refers to a set of computational tools which are employed for system wide monitoring, analysis, control, and operation. A schematic diagram of EMS and its modules are shown in Fig. 1.​6 in Chap. 1 State estimation is the core module in EMS that estimates the system state variables from a set of real-time telemetered measurements (from meters) and topology statuses (from breakers and switches). The term “states” denotes bus voltages, from which power flows through transmission lines can be computed. As seen in Fig. 1.​6, the output of state estimation is required by several other modules, i.e., optimal power flow (OPF) , contingency analysis , and automatic generation control (AGC) , for economic dispatch calculations and security assessment.
Ehab Al-Shaer, Mohammad Ashiqur Rahman

Chapter 5. Intrusion Detection Systems for AMI

Abstract
Recent studies have shown that AMI is potential to immense number of threats [7, 14, 19, 24, 25], which can affect the deployment and growth of smart grids. These studies outline that although there are some secure communication protocols used in smart grids, many vulnerabilities and exploitations have been observed. Despite these facts, limited progress has been made so far in order to detect malicious behaviors in smart grids [3, 4, 10]. In Chap. 1, Fig. 1.3 presents a typical AMI network. Smart meters communicate with intelligent data collectors using various mediums. These collectors communicate with the headend system (and vice versa) using WAN. Unlike traditional networks, AMI has its own requirements which pose significant challenges for monitoring and intrusion detection.
Ehab Al-Shaer, Mohammad Ashiqur Rahman

Backmatter

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