Wireless Networks

BeibeiLi

RongxingLu

GaoxiXiao

Detection ofFalse

Data Injection

Attacks in Smart

Grid Cyber-Physical

Systems

Wireless Networks

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University of Waterloo, Waterloo, ON, Canada

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Beibei Li • Rongxing Lu • Gaoxi Xiao

Detection of False Data

Injection Attacks in Smart

Grid Cyber-Physical Systems

Beibei Li

College of Cybersecurity

Sichuan University

Chengdu, Sichuan, China

Rongxing Lu

Faculty of Computer Science

University of New Brunswick

Fredericton, NB, Canada

Gaoxi Xiao

School of Electrical and Electronic

Engineering

Nanyang Technological University

Singapore, Singapore

ISSN 2366-1186 ISSN 2366-1445 (electronic)

Wireless Networks

ISBN 978-3-030-58671-3 ISBN 978-3-030-58672-0 (eBook)

https://doi.org/10.1007/978-3-030-58672-0

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This book is wholeheartedly dedicated to my

respectable supervisors, as well as our group

members, with whom we have worked over

the years and have made it possible to reach

this moment.

To my beloved families.

Preface

Building an automated, green, and efﬁcient smart grid cyber-physical system (CPS)

while ensuring high reliability and security is an extraordinarily challenging task,

particularly in the ever-evolving cyber threat landscape. This challenge is also

compounded by the increasing pervasiveness of information and communications

technologies across the power infrastructure, as well as the growing availability

of advanced hacking tools in the hacker community. One of the most critical

security threats in smart grid CPSs lies in the high-proﬁle false data injection (FDI)

attacks, where attackers attempt to inject either fabricated measurement data to

mislead power grid state estimation and bad data detection or tampered command

data to misguide power management and control. Accordingly, FDI attacks can

be subdivided into false measurement data injection (FmDI) attacks and false

command data injection (FcDI) attacks.

Detection techniques for FDI attacks have been a signiﬁcant research focus for

smart grid CPSs to withstand these security threats and further protect the power

infrastructure. However, conventional state estimation based bad data detection

approaches have been proved vulnerable to the evolving FDI attacks. To meet this

gap, this monograph introduces four creative research works to analyze and detect

FDI attacks in smart grid CPSs.

First, a stochastic Petri net based analytical model is developed to evaluate and

analyze the system reliability of smart grid CPSs, particularly against topology

attacks under system countermeasures (such as intrusion detection systems and

malfunction recovery techniques) in place. Evolved from FmDI attacks, topology

attacks inject false data by tempering with both measurement data and grid topology

information. This analytical model is featured by bolstering both transient and

steady-state analysis of system reliability.

Second, a distributed host-based collaborative detection scheme is proposed to

detect FmDI attacks in smart grid CPSs. It is considered in this chapter that phasor

measurement units (PMUs), deployed to measure the operating status of power

grids, can be compromised by FmDI attackers. Trusted host monitors (HMs) are

assigned to each PMU to monitor and assess PMUs’ behaviors. Neighboring HMs

make use of the majority voting algorithm based on a set of predeﬁned normal

vii

viii Preface

behavior rules to identify the existence of abnormal measurement data collected

by PMUs. In addition, an innovative reputation system with an adaptive reputation

updating algorithm is designed to evaluate the overall operating status of PMUs, by

which FmDI attacks, as well as the attackers, can be distinctly observed.

Third, a Dirichlet-based detection scheme for FcDI attacks in hierarchical smart

grid CPSs is proposed. In the future hierarchical paradigm of a smart grid CPS, it is

considered that the decentralized LAs responsible for local management and control

can be compromised by FcDI attackers. By issuing fake or biased commands, the

attackers anticipate manipulating the regional electricity prices with the purpose of

illicit ﬁnancial gains. The proposed scheme builds a Dirichlet-based probabilistic

model to assess the reputation levels of LAs. This probabilistic model, used in

conjunction with a designed adaptive reputation incentive mechanism, enables quick

and efﬁcient detection of FcDI attacks as well as the attackers.

Lastly, we systematically explore the feasibility and limitations of detecting

FmDI attacks in smart grid CPSs using distributed ﬂexible AC transmission system

(D-FACTS) devices. Recent studies have investigated the possibilities of proactively

detecting FmDI attacks on smart grid CPSs by using D-FACTS devices. We term

this approach as proactive false data detection (PFDD). In this chapter, the feasibility

of employing PFDD approach to detect FmDI attacks is investigated under single-

bus, uncoordinated multiple-bus, and coordinated multiple-bus FmDI attacks. It is

proved that the PFDD approach is capable of detecting all these three types of FmDI

attacks targeted on buses or super-buses with degrees larger than 1, as long as the

deployment of D-FACTS devices covers branches forming at least a spanning tree

of the power grid graph. Then, the minimum efforts demanded for activating D-

FACTS devices to detect the considered three types of FmDI attacks are evaluated.

In addition, the limitations of PFDD are also discussed, and it is strictly proven

that this approach is not able to identify FmDI attacks on smart grid CPSs that are

targeted on buses or super-buses with degrees equaling 1.

Chengdu, Sichuan, China Beibei Li

Fredericton, NB, Canada Rongxing Lu

Singapore, Singapore Gaoxi Xiao

June 2020

Acknowledgements

There have been many people who have walked alongside me during my Ph.D.

journey. They have guided, supported, and accompanied me. I would like to, hereby,

thank each and every one of them sincerely.

First and foremost, I would like to express my deepest gratitude to my respectable

supervisors—Dr. Xiao Gaoxi at Nanyang Technological University (NTU), Singa-

pore, and Dr. Lu Rongxing at the University of New Brunswick (UNB), Canada—

for their unwavering support and constructive guidance throughout this thesis. They,

upon whose shoulders I stand, explored and paved the path before me. Without

them, this thesis would simply not have been possible. Such academic rigor as may

be found in this thesis is largely due to Dr. Xiao’s refusal to let me get away with

things, while his unerring sense of when and how to intervene has taught me not

only as a good researcher but also a potential good tutor. He is always willing

to take time to listen and usually provide insightful questions and comments, as

well as clear instructions as feedback. Dr. Lu is a renowned expert in cybersecurity

domain, whose passion for doing research and teaching has set a new standard for

everyone involved. His unstinting support and encouragement have driven me to

strive for excellence. Having also a friend ﬁgure, Dr. Lu is really a nice guy who

cares about his students not only on their research career but also on daily lives.

Many thanks are also due to Dr. Wang Licheng at Beijing University of Posts and

Telecommunications (BUPT), China, who started me down this road with selﬂess

support, encouragement, and guidance.

I would especially acknowledge my Thesis Advisory Committee (TAC)

members—Dr. Zhang Jie and Dr. Ma Maode at NTU. Thanks for their faith in

my ability and continuous support ever since I joined NTU. I hope this research

opens up opportunities for us to do research together in the future.

I would extend my heartfelt gratitude to Dr. Ali A. Ghorbani, Dr. Kim-Kwang

Raymond Choo, Dr. Bao Haiyong, Dr. Deng Ruilong, Dr. Wang Wei, and Dr.

Luo Sheng, who contributed to the making of this monograph. Thanks for their

constructive criticism, which enabled me to improve my research and writing skills.

Particular thanks must also be recorded to Dr. Xu Chang, Dr. Liu Yali, Dr. Kong

Qinglei, Dr. Zhao Ming, Dr. Meng Min, Dr. Lin Changlu, Dr. Liu Ximeng, Dr. Li

x Acknowledgements

Chen, Dr. Li Lichun, Dr. Hu Hao, Dr. Zhai Chao, Mr. Huang Cheng, Mr. Wang

Guoming, Mr. Katuwal Rakesh, Mr. Cheng Shuo, Mr. Hao Changyu, Mr. Zhang

Hehong, and Mr. Li Xiang who offered collegial guidance and support over the

years.

There are many of my friends to name individually; however, special thanks are

given to Dr. Yang Rong, Dr. Li Dan, Dr. Yang Ming, Dr. Bi Hui, Dr. Ma Lijia,

Dr. Zhang Heng, Dr. Wang Zeng, Dr. Chen Chunyang, Ms. Sun Meng, Ms. Gao

Yumeng, Ms. Gong Bo, Ms. Huang Yi, Ms. Wang Zhenzhen, Ms. Huang Rui, Ms.

Xin Jian, Ms. Chen Qian, Ms. Wang Yongheng, Ms. Li Yanan, Ms. Chen Shi, Ms.

Chen Qiyin, Ms. Zhang Lili, Ms. Ouyang Qingling, Ms. Zhang Ran, Mr. Cheng

Yanyu, Mr. Liu Yunxiang, Mr. Pan Zihan, Mr. Wang Yang, Mr. Shao Hongxin, Mr.

Li Xiaochen, Mr. Zhang Yunlong, Mr. Guo Zhihong, Mr. Zhang Songze, and Mr.

Gao Li at NTU and Dr. Yang Haomiao, Dr. Huang Junlin, Ms. Yang Xue, Ms.

Zheng Yandong, Ms. Li Huixia, Ms. Deepigha S. V. Babu, Mr. Guo Wei, Mr. Xu

Chenghao, Mr. Hassan Mahdikhani, Mr. Saeed Shafeiee Hasanabadi, Mr. Tao Xi,

Mr. Zhang Xichen, Mr. Xiao Hongtao, and Mr. Zhang Yongcan at UNB. Thanks

for giving me a wonderful and memorable life at both NTU and UNB. In addition,

my wholehearted thanks are given to Mrs. Han Yuhong, Ms. Xie Wanlun, Ms. Yu

Pan, Mr. Du Wuhang, and Mr. Chen Dawen at BUPT for their generous support,

understanding, and encouragement given in many moments of crisis over the years.

I cannot list all the names here, but you guys hold a special place in my heart.

Finally, and most importantly, my most heartfelt and forever gratitude goes to

my family and girlfriend, who have always been a constant source of support and

encouragement. Thanks to my parents and elder sister for putting me through the

best education possible and giving me the strength to reach for the stars and chase

my dreams. I appreciate their sacriﬁces and unending support, and I would not have

been able to get to this stage without them.