Cyber Security Solutions for Protecting and Building the Future Smart Grid

1st Edition - October 8, 2024
Imprint: Elsevier
Editors: Divya Asija, R K Viral, Resul Daş, Gürkan Tuna
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 4 0 6 6 - 2
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 4 0 6 7 - 9

Cyber Security Solutions for Protecting and Building the Future Smart Grid guides the reader from the fundamentals of grid security to practical techniques necessary for grid defe… Read more

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Cyber Security Solutions for Protecting and Building the Future Smart Grid guides the reader from the fundamentals of grid security to practical techniques necessary for grid defense. Through its triple structure, readers can expect pragmatic, detailed recommendations on the design of solutions and real-world problems. The book begins with a supportive grounding in the security needs and challenges of renewable-integrated modern grids. Next, industry professionals provide a wide range of case studies and examples for practical implementation.

Finally, cutting-edge researchers and industry practitioners guide readers through regulatory requirements and develop a clear framework for identifying best practices. Providing a unique blend of theory and practice, this comprehensive resource will help readers safeguard the sustainable grids of the future.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Contributors
Part I. Renewable Energy system security: Vulnerabilities, challenges and impacts
Chapter 1. Smart grids and cyber security
1.1 Introduction
1.2 Cyber security: Why a growing threat?
1.3 Cyber security objectives and requirement
1.4 Reported cyberattacks globally in power systems
1.5 Need of sustainable/renewable-integrated energy grids for cyber security
1.6 Risks across legacy power systems grid: Generation, transmission, and distribution
1.7 Cyber physical asset in power system and part of vulnerabilities
1.8 Path forward and latest development
1.9 Conclusion
Chapter 2. Smart grids: Sources of security intimidations, challenges, and opportunities
2.1 Introduction
2.2 Introduction to smart grid
2.2.1 Conventional systems
2.2.2 Smart systems
2.2.3 Smart grid
2.2.3.1 Regulating and observing
2.2.3.2 Smart meters
2.2.3.3 Intended benefits
2.3 Challenges (security)
2.3.1 Classifying types of attacks
2.3.2 Confidentiality attacks
2.3.3 Integrity attacks
2.3.4 Availability attacks
2.3.5 Attack security methods
2.3.5.1 Standard integrity attack guarding mechanisms
2.3.5.2 Confidentiality attack guarding mechanisms
2.3.5.3 Availability attack guarding mechanisms
2.3.5.4 FDI attack guarding mechanisms
2.4 Industry applications and future trends
2.4.1 Small scale implementation
2.4.1.1 Research/publications
2.4.1.2 Prototypes
2.4.2 Global implementation
2.4.2.1 Australia
2.4.2.2 India
2.4.2.3 United Kingdom
2.4.2.4 China
2.4.2.5 Canada
2.4.2.6 United States
2.4.2.7 Japan
2.4.3 Future research
2.4.3.1 Constraints
2.4.4 Future implementation expectations
2.5 Conclusion
Chapter 3. Emerging smart grid technologies and their impact on grid operations
3.1 Introduction
3.2 Digital communication system
3.2.1 Wide area network (WAN)
3.2.2 Neighborhood area network (NAN)
3.2.3 HAN
3.3 Description of smart grid communication technologies
3.3.1 Zigbee
3.3.2 WLAN
3.3.3 WiMAX
3.3.4 Bluetooth
3.3.5 Cellular communication
3.4 Communication architecture of smart grids
3.4.1 ICT model of power system
3.4.2 Substation automation system
3.4.2.1 Station level
3.4.2.2 Bay level
3.4.2.3 Process level
3.4.3 SCADA
3.4.4 IEDs (intelligent electronics devices)
3.4.5 Phasor measurement unit
3.4.6 PDC (phasor data concentrator)
3.5 Distribution system
3.5.1 AMI
3.5.2 DERs (distributive energy resources)
3.5.3 Distribution automation
3.6 Conclusion
Chapter 4. Multi-sensor data fusion perspective for smart grid analytics
4.1 Introduction
4.2 Data fusion
4.3 Related work
4.3.1 JDL data fusion model
4.3.2 Other data fusion models
4.3.3 Data fusion methods
4.4 Opportunities and challenges for data fusion
4.4.1 Opportunities
4.4.2 Challenges
4.5 Future research directions
4.6 Conclusion
Chapter 5. Impact of cybersecurity attacks on electrical system operation
5.1 Introduction
5.1.1 Understanding the crucial role of cybersecurity in the operation of electrical systems
5.1.2 Technologies used for electrical system operations
5.2 Various cybersecurity threats to electrical system operation
5.2.1 Exploring different types of cybersecurity threats affecting electrical system operations
5.2.1.1 State estimation (SE) model
5.2.1.2 False data injection (FDI) attack model
5.2.1.3 Man-in-the-middle (MitM) attack model
5.2.1.4 Denial-of-service (DoS) attack model
5.3 Case studies: Cybersecurity attacks on electrical system operation
5.3.1 Studying to understand severity and consequences Ukraine's cybersecurity attacks on electrical operations
5.3.2 Other cyber-security breaches
5.4 Impact on grid reliability and stability
5.4.1 Investigating how cybersecurity breaches affect power supply reliability, voltage, frequency stability, and overall grid stability
5.4.2 The cybersecurity domino effect
5.5 Economic and social consequences of cybersecurity breaches
5.5.1 Important of social responsibility
5.5.2 Assessing the economic losses and social disruptions caused by cybersecurity attacks on electrical system operations
5.5.3 Understanding the long-term economic impacts on affected regions
5.6 National security implications
5.6.1 Analyzing the national security implications of cyber threats on critical electrical infrastructure
5.6.2 Discussing the roles of government and defense agencies in safeguarding the electrical system
5.7 Cybersecurity measures and best practices for electrical system operation
5.7.1 Exploring cybersecurity frameworks, standards, and best practices tailored for securing electrical system operations
5.7.2 Holistic approach to cybersecurity: Employee training, secure design, security hardening and assessment
5.7.3 Intrusion detection systems (IDS)
5.8 Future challenges and emerging threats in electrical system operation
5.8.1 Anticipating future challenges and emerging cyber threats specific to electrical system operations
5.8.1.1 Artificial intelligence (AI)
5.8.1.2 Federated learning (FL)
5.8.1.3 Computer vision (CV)
5.8.1.4 5G, 6G and beyond: Advancing communication networks
5.8.2 Discussing strategies to integrate advanced technologies securely into the electrical grid
5.9 Conclusion
Chapter 6. Cyber security: Emerging trends best practices
6.1 Introduction
6.1.1 Application domains for cyber attacks
6.2 Types of cyberattacks/threats
6.2.1 Based on purpose
6.2.2 Based on legal classification
6.2.3 Based on severity of involvement
6.2.4 Based on scope
6.2.5 Based on network type
6.2.6 Malware attack
6.2.7 Attack on quantum key distribution
6.3 Learning from the past cyber attacks
6.4 Standard security framework
6.5 Emerging trends in cyber security
6.5.1 Rise of AI and machine learning in threat detection and response
6.5.1.1 Applications of AI/ML in cyber security
6.5.1.2 Benefits and challenges
6.5.2 Internet of things (IoT) security considerations
6.5.2.1 Vulnerabilities in IoT devices
6.5.2.2 Strategies for securing IoT ecosystems
6.5.3 Cloud security and its evolving landscape
6.5.3.1 Cloud-based threats and attacks
6.5.3.2 Ensuring data privacy and integrity in the cloud
6.6 Best practices in cyber security
6.6.1 Multifactor authentication (MFA) and strong authentication methods
6.6.2 Regular software patching and updates
6.7 Case studies: Real-world examples
6.7.1 Notable cyber security breaches and their impact
6.7.2 Successful cyber security implementations and lessons learned
6.8 Future outlook and challenges
6.9 Conclusion
Part II. Case studies and practical techniques
Chapter 7. Frameworks for smart grid cyber security analysis
7.1 Introduction
7.2 Authentication and access control for all communication flow throughout the system
7.3 Attack detection and countermeasures everywhere in smart grid
7.4 Node wise basic and light-weight cryptographic functions
7.5 Security of network protocols from the application layer to the MAC layer
7.6 Cyber security testbed platforms implementation to investigate the vulnerabilities of the power infrastructures
7.7 Related case study
7.8 Conclusion
Chapter 8. Cyberattack detection techniques for smart grid
8.1 Introduction
8.2 Related work
8.3 Proposed architecture
8.4 Implementation and result analysis
8.5 Conclusion
Chapter 9. Anomaly and intrusion detection systems for smart grids
9.1 Introduction
9.2 Literature review
9.3 SG architecture
9.3.1 AMI
9.3.2 Substation
9.3.3 PMU
9.3.4 SCADA
9.4 Overview of IDS
9.5 IDS in SG
9.5.1 IDS for AMI
9.5.2 IDS for substation
9.5.3 Intrusion detection systems for phasor measurement unit
9.5.4 Intrusion detection system for SCADA
9.6 Conclusion
Chapter 10. Artificial intelligence and machine learning in cyber security applications
10.1 Introduction
10.2 Foundations of AI and ML in cyber security
10.3 AI and ML-driven threat detection
10.3.1 Utilizing AI for anomaly detection
10.3.2 Behavior-based threat detection with machine learning
10.3.3 Predictive analysis and threat intelligence
10.4 AI and ML-driven incident response
10.4.1 Real-time incident identification and triage
10.4.2 Automated response to cyber attacks
10.4.3 AI-powered forensics and investigation
10.5 AI and ML in security operations
10.5.1 AI-enabled Security Information and Event Management (SIEM)
10.5.2 Advancements in Security Orchestration and Automation (SOAR)
10.5.3 Cognitive Security Operations Centers (SOCs)
10.6 AI and ML for endpoint security
10.6.1 Behavioral analysis for endpoint protection
10.6.2 AI-enhanced antivirus and antimalware solutions
10.6.3 Adaptive and self-learning endpoint protection
10.7 AI and ML in network security
10.7.1 AI-Based Intrusion Detection and Prevention Systems (IDPS)
10.7.2 Network traffic analysis with machine learning
10.7.3 AI-driven Network Access Control (NAC)
10.8 AI and ML in threat hunting
10.8.1 Proactive threat hunting using AI
10.8.2 Utilizing ML for identifying new and emerging threats
10.8.3 AI-enhanced threat intelligence gathering
10.9 The role of AI and ML in cloud security
10.9.1 AI-driven cloud security monitoring
10.9.2 Machine learning for cloud data protection
10.9.3 Cloud-native AI security solutions
10.10 Challenges and limitations of AI and ML in cyber security
10.10.1 The challenge of explainability and transparency
10.10.2 Bias and ethical concerns
10.10.3 Adversarial attacks on AI and ML systems
10.11 Future perspectives and trends
10.11.1 Reinforcement learning in cyber security
10.11.2 AI-ML hybrid models for enhanced cyber resilience
10.11.3 Quantum computing and AI-ML in cyber security
10.12 Conclusion
Part III. Cyber-security standards and best practices
Chapter 11. Smart grid: Interoperability and cyber security
11.1 State-of-the-art-interoperability
11.2 Benefits, control and model for interoperability
11.3 Smart grid interoperability standards globally
11.4 NERC, NIST, FERC standards
11.5 Conclusion
Chapter 12. International standards, regulations, and best practices for cyber security of smart grid
12.1 Introduction
12.1.1 Significance of cyber security in power grids
12.1.2 Significance of understanding the international standards and regulations for cyber security in power grids
12.2 International standards for smart grid cybersecurity
12.2.1 International electrotechnical commission (IEC) standards for cyber security in power grids
12.2.2 National Institute of Standards and Technology (NIST) standard for cyber security in power grids
12.2.3 International Organization for Standardization (ISO) standards for cyber security in power grids
12.2.4 Institute of Electrical and Electronics Engineers (IEEE) standard for cyber security in power grids
12.2.5 North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP) for cyber security in power grids
12.3 Regulations for smart grid cybersecurity
12.3.1 Cyber security regulations for the Indian power grid
12.4 Cyber attack case studies on smart grids
12.4.1 Ukraine power grid attack (2015 and 2016)
12.4.2 Israel's electricity authority attack (2016)
12.4.3 Other significant cyber attacks
12.5 Summary
Index

Divya Asija

Divya Asija is an Assistant Professor in the Electrical and Electronics Engineering Department of Amity School of Engineering and Technology at the Amity University Uttar Pradesh in India, with over 14 years’ teaching experience. Her research interests lie in the area of power systems, ranging from congestion management, renewable energy resources, distributed generation, smart grids and electric vehicles to big data analytics and multi-disciplinary areas. Dr Asija is the author of many book chapters, conference papers, and articles in peer-reviewed international journals. She has edited one book with CRC press, with another in progress and has filed eight national patents. Dr Asija has also served as TPC Member of several international conferences held worldwide, and served as Associate Editor for the IJEER Journal since 2022. She was also a winner of gold award for courseware creation using Moodle for outcome-based education and ethical commitment.

Affiliations and expertise

Assistant Professor, Department of Electrical and Electronics Engineering, Amity School of Engineering & Technology (ASET), Noida, Uttar Pradesh, India

R K Viral

Dr. Viral is currently an Assistant Professor (Senior Grade) in the Department of Electrical and Electronics Engineering, Amity School of Engineering & Technology in India, with more than 14 years’ teaching experience. His research interests include distribution system planning and optimization, renewable energy systems and applications, electric vehicle technologies and applications, and smart grids. Dr. Viral has filed more than 16 patents and published more than 75 peer-reviewed research publications in various international/national journals, conferences, and edited books. He has served as TPC Member of several international conferences held worldwide.

Affiliations and expertise

Assistant Professor, Department of Electrical and Electronics Engineering, Amity School of Engineering & Technology (ASET), Noida, Uttar Pradesh, India

Resul Daş

Prof. Resul Das is a Professor and Chair at the Department of Software Engineering of Firat University in Turkey, where he has been a faculty member for a decade. He has also been the CCNA/CCNP instructor and coordinated the Cisco Networking Academy Program at Firat University since 2002. Between September 2017 and June 2018, he was a visiting Professor at the Department of Computing Science at the University of Alberta in Canada. Alongside publishing many journal papers and international conference proceedings, he was Associate Editor for the Journal of IEEE Access and the Turkish Journal of Electrical Engineering and Computer Science from 2018 to 2021. Prof. Das was included on Stanford University’s list of the top 2% "World's Most Influential Scientists" in both 2020 and 2021. Research areas of interest include computer networks and security, cyber-security, software design methods, software testing, IoT/M2M applications, graph visualization, knowledge discovery, and data fusion.

Affiliations and expertise

Department Chair, Department of Software Engineering, Technology Faculty, Firat University, Turkey

Gürkan Tuna

Prof. Gürkan Tuna is currently a Professor at the Department of Computer Technologies and Head of the Graduate Program of Mechatronics Engineering at Trakya University in Turkey. His current research interests include smart grid communications, wireless ad-hoc and sensor networks, cyber security, and machine learning. He has authored several papers in refereed journals and international conference proceedings and has been serving as a reviewer and program committee member to numerous journals and conferences in these areas.

Affiliations and expertise

Head of Mechatronics Engineering Graduate Program, Institute of Science, Trakya University, Turkey Professor of Computer Programming Department, Trakya University, Turkey

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Cyber Security Solutions for Protecting and Building the Future Smart Grid

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Divya Asija

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