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Detection of False Data Injection Attacks in Smart Grid Cyber-Physical Systems / by Beibei Li, Rongxing Lu, Gaoxi Xiao.
- Format:
- Book
- Author/Creator:
- Li, Beibei, Author.
- Lu, Rongxing, Author.
- Xiao, Gaoxi, Author.
- Series:
- Computer Science (SpringerNature-11645)
- Wireless Networks, 2366-1445
- Language:
- English
- Subjects (All):
- Telecommunication.
- Computational intelligence.
- Computer networks.
- Communications Engineering, Networks.
- Computational Intelligence.
- Computer Communication Networks.
- Local Subjects:
- Communications Engineering, Networks.
- Computational Intelligence.
- Computer Communication Networks.
- Physical Description:
- 1 online resource (XVI, 157 pages) : 61 illustrations, 39 illustrations in color.
- Edition:
- 1st ed. 2020.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2020.
- System Details:
- text file PDF
- Summary:
- This book discusses cybersecurity issues of smart grid cyber-physical systems, focusing on the detection techniques against false data injection attacks. The authors discuss passive and proactive techniques that combat and mitigate two categories of false data injection attacks, false measurement data injections and false command data injections in smart grid cyber-physical systems. These techniques are easy to follow for either professionals or beginners. With this book, readers can quickly get an overview of this topic and get ideas of new solutions for false data injections in smart grid cyber-physical systems. Readers include researchers, academics, students, and professionals. Presents a comprehensive summary for the detection techniques of false data injection attacks in smart grid cyber-physical systems; Reviews false data injections for either measurement data or command data; Analyzes passive and proactive approaches to smart grid cyber-physical systems.
- Contents:
- Introduction
- Foundations and Related Literature
- SPNTA: Stochastic Petri-Net-Based Reliability Analysis under Topology Attacks
- DHCD: Distributed Host-Based Collaborative Detection for FDI Attacks
- DDOA: Dirichlet-Based Detection for Opportunistic Attacks
- PFDD: On Feasibility and Limitations of Detecting FDI Attacks Using DFACTS
- Conclusion.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-58672-0
- 9783030586720
- Access Restriction:
- Restricted for use by site license.
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