http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Cyber Security Solutions for Protecting and Building the Future Smart Grid
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.
Front Cover -- CYBER SECURITY SOLUTIONS FOR PROTECTING AND BUILDING THE FUTURE SMART GRID -- CYBER SECURITY SOLUTIONS FOR PROTECTING AND BUILDING THE FUTURE SMART GRID -- Copyright -- Contents -- Contributors -- I -- Renewable Energy system security: Vulnerabilities, challenges and impacts -- 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 -- References -- 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 -- References -- Further readings -- 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 -- References -- Further readings -- 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 -- Acknowledgments -- References -- 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 st ... -- 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 -- References -- 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 -- References -- II -- Case studies and practical techniques -- 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 -- References -- 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 -- References -- Further readings -- 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 -- References -- 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.
edited by Divya Asija, R.K. Viral, Resul Daş, Gürkan Tuna.
Includes bibliographical references and index.