Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid †
Abstract
:1. Introduction
2. Power Grid Cybersecurity Governance
3. Power Grid ICS Network Architectures
- Level 5, Enterprise Network—Used for managing business-related activities.
- Level 4, Site Planning and Logistics Network—Used for managing production work flows.
- Level 3, Site Manufacturing Operations and Control—Used to manage control plant operations that produce the desired end product.
- Level 2, Area Control—Used for supervising, monitoring, and controlling the physical processes.
- Level 1, Basic Control—Sensing and manipulating the physical processes.
- Level 0, Physical Process—The physical process happens here.
4. Cybersecurity Threats in Energy System OT Networks
4.1. Availability Threats
4.2. Integrity Threats
4.3. Confidentiality Threats
5. Potential Countermeasures to Cybersecurity Threats
5.1. Potential Countermeasures for Availability Threats
5.2. Potential Countermeasures for Integrity and Confidentiality Threats
6. Recommended Gap Analysis Strategies for Cybersecurity Assurance in the Energy Sector
- Identify—Determine assets within the organization and their risk factors for potential Cybersecurity risks.
- Protect—Create safeguards to ensure delivery of infrastructure services through access control, awareness and training, data security, and information protection procedures.
- Detect—Identify any Cybersecurity events with continuous monitoring.
- Respond—Implement predefined procedures for response planning and communications.
- Recover—Develop plans to maintain resilience and restore capabilities of services.
- Prioritize and Scope
- Orient
- Create a Current Profile
- Conduct a Risk Assessment
- Create a Target Profile
- Determine, Analyze, and Prioritize Gaps
- Implement Action Plan
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ICS | Industrial Control System |
FERC | Federal Energy Regulatory Commission |
NIST | National Institute of Standards and Technology |
NERC | North American Electric Reliability Corporation |
CIP | Critical Infrastructure Protection |
IT | Information Technology |
OT | Operations Technology |
DiD | Defense-in-Depth |
DMZ | Demilitarized Zone |
CSF | Cybersecurity Framework |
C2M2 | Cybersecurity Capability Maturity Model |
ICPS | Industrial Cyber-Physical Systems |
DoS | Denial of Service |
SG | Smart Grid |
FDIA | False Data Injection Attack |
ES-C2M2 | Electricity Subsector Cybersecurity Capability Maturity Model |
CIP | Critical Infrastructure Protection |
ERO | Electricity Reliability Organization |
BES | Bulk Electric System |
ESP | Electronic Security Perimeter |
NRC | Nuclear Regulatory Commission |
NEI | Nuclear Energy Institute |
DHS | US Department of Homeland Security |
ISA | International Society of Automation |
IEC | International Electrotechnical Commission |
M2M | Machine-to-Machine |
MAS-SJ | Maximum Attacking Strategy using Spoofing and Jamming |
PMU | Phasor Measurement Unit |
AMI | Advanced Metering Infrastructure |
TDS | Time-Delay-Switch |
PMU | Phasor Measurement Units |
TSA | Time Synchronization Attack |
IDS/IPS | Intrusion Detection/Prevention Systems |
CRN | Cognitive Radio Network |
WSGN | Wireless Smart Grid Network |
References
- Skodvin, T. “Pivotal politics” in US energy and climate legislation. Energy Policy 2010, 38, 4214–4223. [Google Scholar] [CrossRef]
- CIP Standards. NERC 2022. Available online: https://www.nerc.com/pa/Stand/Pages/USRelStand.aspx (accessed on 28 April 2022).
- Kayan, H.; Nunes, M.; Rana, O.; Burnap, P.; Perera, C. Cybersecurity of Industrial Cyber-Physical Systems: A Review. ACM Comput. Surv. 2022, 54, 229. [Google Scholar] [CrossRef]
- Hassanzadeh, A.; Rasekh, A.; Galelli, S.; Aghashahi, M.; Taormina, R.; Ostfeld, A.; Banks, M.K. A review of cybersecurity incidents in the water sector. J. Environ. Eng. 2020, 146, 03120003. [Google Scholar] [CrossRef] [Green Version]
- Krause, T.; Ernst, R.; Klaer, B.; Hacker, I.; Henze, M. Cybersecurity in Power Grids: Challenges and Opportunities. Sensors 2021, 21, 6225. [Google Scholar] [CrossRef] [PubMed]
- Jacobs, N.; Hossain-McKenzie, S.; Jose, D.; Saleem, D.; Lai, C.; Cordeiro, P.; Hasandka, A.; Martin, M.; Howerter, C. Analysis of System and Interoperability Impact from Securing Communications for Distributed Energy Resources. In Proceedings of the 2019 IEEE Power and Energy Conference at Illinois (PECI), Champaign, IL, USA, 28 February–1 March 2019; pp. 1–8. [Google Scholar] [CrossRef]
- Shapsough, S.; Qatan, F.; Aburukba, R.; Aloul, F.; Al Ali, A.R. Smart grid cyber security: Challenges and solutions. In Proceedings of the 2015 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), Offenburg, Germany, 20–23 October 2015; pp. 170–175. [Google Scholar] [CrossRef]
- Christopher, J.D.; Gonzalez, D.; White, D.W.; Stevens, J.; Grundman, J.; Mehravari, N.; Dolan, T. Cybersecurity Capability Maturity Model (C2M2); Department of Homeland Security: Washington, DC, USA, 2014; pp. 1–76. [Google Scholar]
- Scali, D. Developing a Security Strategy to Cover ICS Assets. 17 August 2016. Available online: https://www.fireeye.com/blog/executive-perspective/2016/08/developing_a_securit.html. (accessed on 28 April 2022).
- Komninos, N.; Philippou, E.; Pitsillides, A. Survey in smart grid and smart home security: Issues, challenges and countermeasures. IEEE Commun. Surv. Tutor. 2014, 16, 1933–1954. [Google Scholar] [CrossRef]
- Line, M.B.; Tøndel, I.A.; Jaatun, M.G. Cyber security challenges in Smart Grids. In Proceedings of the 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, Manchester, UK, 5–7 December 2011; pp. 1–8. [Google Scholar]
- Tan, S.; De, D.; Song, W.Z.; Yang, J.; Das, S.K. Survey of security advances in smart grid: A data driven approach. IEEE Commun. Surv. Tutor. 2016, 19, 397–422. [Google Scholar] [CrossRef]
- Huseinović, A.; Mrdović, S.; Bicakci, K.; Uludag, S. A survey of denial-of-service attacks and solutions in the smart grid. IEEE Access 2020, 8, 177447–177470. [Google Scholar] [CrossRef]
- Mollah, M.B.; Zhao, J.; Niyato, D.; Lam, K.Y.; Zhang, X.; Ghias, A.M.; Koh, L.H.; Yang, L. Blockchain for future smart grid: A comprehensive survey. IEEE Internet Things J. 2020, 8, 18–43. [Google Scholar] [CrossRef]
- Fan, Z.; Kulkarni, P.; Gormus, S.; Efthymiou, C.; Kalogridis, G.; Sooriyabandara, M.; Zhu, Z.; Lambotharan, S.; Chin, W.H. Smart grid communications: Overview of research challenges, solutions, and standardization activities. IEEE Commun. Surv. Tutor. 2012, 15, 21–38. [Google Scholar] [CrossRef] [Green Version]
- Le, T.N.; Chin, W.L.; Chen, H.H. Standardization and security for smart grid communications based on cognitive radio technologies—A comprehensive survey. IEEE Commun. Surv. Tutor. 2016, 19, 423–445. [Google Scholar]
- Peng, C.; Sun, H.; Yang, M.; Wang, Y.L. A survey on security communication and control for smart grids under malicious cyber attacks. IEEE Trans. Syst. Man Cybern. Syst. 2019, 49, 1554–1569. [Google Scholar] [CrossRef]
- Yan, Y.; Qian, Y.; Sharif, H.; Tipper, D. A survey on smart grid communication infrastructures: Motivations, requirements and challenges. IEEE Commun. Surv. Tutor. 2012, 15, 5–20. [Google Scholar] [CrossRef] [Green Version]
- Rehmani, M.H.; Davy, A.; Jennings, B.; Assi, C. Software defined networks-based smart grid communication: A comprehensive survey. IEEE Commun. Surv. Tutor. 2019, 21, 2637–2670. [Google Scholar] [CrossRef]
- Tufail, S.; Parvez, I.; Batool, S.; Sarwat, A. A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid. Energies 2021, 14, 5894. [Google Scholar] [CrossRef]
- Musleh, A.S.; Chen, G.; Dong, Z.Y. A survey on the detection algorithms for false data injection attacks in smart grids. IEEE Trans. Smart Grid 2019, 11, 2218–2234. [Google Scholar] [CrossRef]
- Beasley, C.; Zhong, X.; Deng, J.; Brooks, R.; Venayagamoorthy, G.K. A survey of electric power synchrophasor network cyber security. In Proceedings of the IEEE PES Innovative Smart Grid Technologies, Europe, Istanbul, Turkey, 12–15 October 2014; pp. 1–5. [Google Scholar]
- Moussa, B.; Debbabi, M.; Assi, C. Security assessment of time synchronization mechanisms for the smart grid. IEEE Commun. Surv. Tutor. 2016, 18, 1952–1973. [Google Scholar] [CrossRef]
- Barrett, M.P. Framework for Improving Critical Infrastructure Cybersecurity Version 1.1; National Institute of Standards and Technology: Gaithersburg, MD, USA, 2018. [Google Scholar] [CrossRef]
- Allnutt, J.; Anand, D.; Arnold, D.; Goldstein, A.; Li-Baboud, Y.; Martin, A.; Nguyen, C.; Noseworthy, R.; Subramaniam, R.; Weiss, M. Timing challenges in the smart grid. NIST Spec. Publ. 2017, 1500, 08. [Google Scholar]
- Department of Homeland Security, U.D. Industrial Control Systems Cyber Emergency Response Team. Recommended Practice: Improving Industrial Control System Cybersecurity with Defense-In-Depth Strategies. 2016. Available online: https://www.cisa.gov/publication/cybersecurity-best-practices-for-industrial-control-systems (accessed on 28 April 2022).
- Greer, C.; Wollman, D.A.; Prochaska, D.; Boynton, P.A.; Mazer, J.A.; Nguyen, C.; FitzPatrick, G.; Nelson, T.L.; Koepke, G.H.; Hefner, A.R., Jr.; et al. Nist Framework and Roadmap for Smart Grid Interoperability Standards, Release 3.0; National Institute of Standards and Technology: Gaithersburg, MD, USA, 2014. [Google Scholar]
- Temple, W.G.; Chen, B.; Tippenhauer, N.O. Delay makes a difference: Smart grid resilience under remote meter disconnect attack. In Proceedings of the 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm), Vancouver, BC, Canada, 21–24 October 2013; pp. 462–467. [Google Scholar]
- Ma, J.; Liu, Y.; Song, L.; Han, Z. Multiact dynamic game strategy for jamming attack in electricity market. IEEE Trans. Smart Grid 2015, 6, 2273–2282. [Google Scholar] [CrossRef]
- Lu, Z.; Wang, W.; Wang, C. Modeling, evaluation and detection of jamming attacks in time-critical wireless applications. IEEE Trans. Mob. Comput. 2013, 13, 1746–1759. [Google Scholar] [CrossRef]
- Li, H.; Lai, L.; Qiu, R.C. A denial-of-service jamming game for remote state monitoring in smart grid. In Proceedings of the 2011 45th Annual Conference on Information Sciences and Systems, Baltimore, MD, USA, 23–25 March 2011; pp. 1–6. [Google Scholar] [CrossRef]
- Yi, P.; Zhu, T.; Zhang, Q.; Wu, Y.; Li, J. A denial of service attack in advanced metering infrastructure network. In Proceedings of the 2014 IEEE International Conference on Communications (ICC), Sydney, NSW, Australia, 10–14 June 2014; pp. 1029–1034. [Google Scholar]
- Choi, K.; Chen, X.; Li, S.; Kim, M.; Chae, K.; Na, J. Intrusion detection of NSM based DoS attacks using data mining in smart grid. Energies 2012, 5, 4091–4109. [Google Scholar] [CrossRef] [Green Version]
- Jin, D.; Nicol, D.M.; Yan, G. An event buffer flooding attack in DNP3 controlled SCADA systems. In Proceedings of the 2011 Winter Simulation Conference (WSC), Phoenix, AZ, USA, 11–14 December 2011; pp. 2614–2626. [Google Scholar]
- Cleveland, F.M. Cyber security issues for advanced metering infrasttructure (AMI). In Proceedings of the 2008 IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, USA, 20–24 July 2008; pp. 1–5. [Google Scholar]
- Wang, W.; Lu, Z. Cyber security in the smart grid: Survey and challenges. Comput. Netw. 2013, 57, 1344–1371. [Google Scholar] [CrossRef]
- Yi, P.; Zhu, T.; Zhang, Q.; Wu, Y.; Pan, L. Puppet attack: A denial of service attack in advanced metering infrastructure network. J. Netw. Comput. Appl. 2016, 59, 325–332. [Google Scholar] [CrossRef] [Green Version]
- Asri, S.; Pranggono, B. Impact of distributed denial-of-service attack on advanced metering infrastructure. Wirel. Pers. Commun. 2015, 83, 2211–2223. [Google Scholar] [CrossRef] [Green Version]
- Kolesnikov, V.; Lee, W. MAC aggregation protocols resilient to DoS attacks. Int. J. Secur. Netw. 2012, 7, 122–132. [Google Scholar] [CrossRef]
- Zhang, Z.; Gong, S.; Dimitrovski, A.D.; Li, H. Time synchronization attack in smart grid: Impact and analysis. IEEE Trans. Smart Grid 2013, 4, 87–98. [Google Scholar] [CrossRef]
- Risbud, P.; Gatsis, N.; Taha, A. Vulnerability analysis of smart grids to GPS spoofing. IEEE Trans. Smart Grid 2018, 10, 3535–3548. [Google Scholar] [CrossRef] [Green Version]
- Gai, K.; Qiu, M.; Ming, Z.; Zhao, H.; Qiu, L. Spoofing-jamming attack strategy using optimal power distributions in wireless smart grid networks. IEEE Trans. Smart Grid 2017, 8, 2431–2439. [Google Scholar] [CrossRef]
- Sargolzaei, A.; Yen, K.; Abdelghani, M.N. Delayed inputs attack on load frequency control in smart grid. In Proceedings of the ISGT 2014, Washington, DC, USA, 19–22 February 2014; pp. 1–5. [Google Scholar]
- Li, Q.; Ross, C.; Yang, J.; Di, J.; Balda, J.C.; Mantooth, H.A. The effects of flooding attacks on time-critical communications in the smart grid. In Proceedings of the 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 18–20 February 2015; pp. 1–5. [Google Scholar]
- Beigi-Mohammadi, N.; Mišić, J.; Khazaei, H.; Mišić, V.B. An intrusion detection system for smart grid neighborhood area network. In Proceedings of the 2014 IEEE International Conference on Communications (ICC), Sydney, NSW, Australia, 10–14 June 2014; pp. 4125–4130. [Google Scholar]
- Goel, S.; Hong, Y.; Papakonstantinou, V.; Kloza, D. Smart Grid Security; Springer: Berlin/Heidelberg, Germany, 2015; pp. 1–39. [Google Scholar] [CrossRef]
- Mohsenian-Rad, A.H.; Leon-Garcia, A. Distributed internet-based load altering attacks against smart power grids. IEEE Trans. Smart Grid 2011, 2, 667–674. [Google Scholar] [CrossRef]
- Li, Y.; Wang, R.; Wang, P.; Niyato, D.; Saad, W.; Han, Z. Resilient PHEV charging policies under price information attacks. In Proceedings of the 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm), Tainan, Taiwan, 5–8 November 2012; pp. 389–394. [Google Scholar]
- Anzalchi, A.; Sarwat, A. A survey on security assessment of metering infrastructure in smart grid systems. In Proceedings of the SoutheastCon 2015, Fort Lauderdale, FL, USA, 9–12 April 2015; pp. 1–4. [Google Scholar]
- Asghar, M.R.; Dán, G.; Miorandi, D.; Chlamtac, I. Smart meter data privacy: A survey. IEEE Commun. Surv. Tutor. 2017, 19, 2820–2835. [Google Scholar] [CrossRef]
- Chung, H.M.; Li, W.T.; Yuen, C.; Chung, W.H.; Wen, C.K. Local cyber-physical attack with leveraging detection in smart grid. In Proceedings of the 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), Dresden, Germany, 23–27 October 2017; pp. 461–466. [Google Scholar]
- Jiang, Q.; Chen, H.; Xie, L.; Wang, K. Real-time detection of false data injection attack using residual prewhitening in smart grid network. In Proceedings of the 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), Dresden, Germany, 23–27 October 2017; pp. 83–88. [Google Scholar]
- Sreenath, J.; Meghwani, A.; Chakrabarti, S.; Rajawat, K.; Srivastava, S. A recursive state estimation approach to mitigate false data injection attacks in power systems. In Proceedings of the 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, USA, 16–20 July 2017; pp. 1–5. [Google Scholar]
- Xu, R.; Wang, R.; Guan, Z.; Wu, L.; Wu, J.; Du, X. Achieving efficient detection against false data injection attacks in smart grid. IEEE Access 2017, 5, 13787–13798. [Google Scholar] [CrossRef]
- Liu, T.; Sun, Y.; Liu, Y.; Gui, Y.; Zhao, Y.; Wang, D.; Shen, C. Abnormal traffic-indexed state estimation: A cyber–physical fusion approach for smart grid attack detection. Future Gener. Comput. Syst. 2015, 49, 94–103. [Google Scholar] [CrossRef]
- Lukicheva, I.; Pozo, D.; Kulikov, A. Cyberattack detection in intelligent grids using non-linear filtering. In Proceedings of the 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Sarajevo, Bosnia and Herzegovina, 21–25 October 2018; pp. 1–6. [Google Scholar]
- Kallitsis, M.G.; Bhattacharya, S.; Stoev, S.; Michailidis, G. Adaptive statistical detection of false data injection attacks in smart grids. In Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Washington, DC, USA, 7–9 December 2016; pp. 826–830. [Google Scholar]
- Moslemi, R.; Mesbahi, A.; Velni, J.M. A fast, decentralized covariance selection-based approach to detect cyber attacks in smart grids. IEEE Trans. Smart Grid 2017, 9, 4930–4941. [Google Scholar] [CrossRef]
- Chen, Y.; Huang, S.; Liu, F.; Wang, Z.; Sun, X. Evaluation of reinforcement learning-based false data injection attack to automatic voltage control. IEEE Trans. Smart Grid 2018, 10, 2158–2169. [Google Scholar] [CrossRef]
- Tang, B.; Yan, J.; Kay, S.; He, H. Detection of false data injection attacks in smart grid under colored Gaussian noise. In Proceedings of the 2016 IEEE Conference on Communications and Network Security (CNS), Philadelphia, PA, USA, 17–19 October 2016; pp. 172–179. [Google Scholar]
- Akingeneye, I.; Wu, J. Low latency detection of sparse false data injections in smart grids. IEEE Access 2018, 6, 58564–58573. [Google Scholar] [CrossRef]
- Kurt, M.N.; Yılmaz, Y.; Wang, X. Real-time detection of hybrid and stealthy cyber-attacks in smart grid. IEEE Trans. Inf. Forensics Secur. 2018, 14, 498–513. [Google Scholar] [CrossRef] [Green Version]
- Manandhar, K.; Cao, X.; Hu, F.; Liu, Y. Detection of faults and attacks including false data injection attack in smart grid using Kalman filter. IEEE Trans. Control. Netw. Syst. 2014, 1, 370–379. [Google Scholar] [CrossRef]
- Rawat, D.B.; Bajracharya, C. Detection of false data injection attacks in smart grid communication systems. IEEE Signal Process. Lett. 2015, 22, 1652–1656. [Google Scholar] [CrossRef]
- Khalaf, M.; Youssef, A.; El-Saadany, E. Detection of false data injection in automatic generation control systems using Kalman filter. In Proceedings of the 2017 IEEE Electrical Power and Energy Conference (EPEC), Saskatoon, SK, Canada, 22–25 October 2017; pp. 1–6. [Google Scholar]
- Khalaf, M.; Youssef, A.; El-Saadany, E. Joint detection and mitigation of false data injection attacks in AGC systems. IEEE Trans. Smart Grid 2018, 10, 4985–4995. [Google Scholar] [CrossRef]
- Kurt, M.N.; Yılmaz, Y.; Wang, X. Distributed quickest detection of cyber-attacks in smart grid. IEEE Trans. Inf. Forensics Secur. 2018, 13, 2015–2030. [Google Scholar] [CrossRef]
- Jiang, Y.; Hui, Q. Kalman filter with diffusion strategies for detecting power grid false data injection attacks. In Proceedings of the 2017 IEEE International Conference on Electro Information Technology (EIT), Lincoln, NE, USA, 14–17 May 2017; pp. 254–259. [Google Scholar]
- Khalid, H.M.; Peng, J.C.H. Immunity toward data-injection attacks using multisensor track fusion-based model prediction. IEEE Trans. Smart Grid 2015, 8, 697–707. [Google Scholar] [CrossRef]
- Musleh, A.S.; Khalid, H.M.; Muyeen, S.; Al-Durra, A. A prediction algorithm to enhance grid resilience toward cyber attacks in WAMCS applications. IEEE Syst. J. 2017, 13, 710–719. [Google Scholar] [CrossRef]
- Karimipour, H.; Dinavahi, V. Robust massively parallel dynamic state estimation of power systems against cyber-attack. IEEE Access 2017, 6, 2984–2995. [Google Scholar] [CrossRef]
- Karimipour, H.; Dinavahi, V. On false data injection attack against dynamic state estimation on smart power grids. In Proceedings of the 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 14–17 August 2017; pp. 388–393. [Google Scholar]
- Shi, W.; Wang, Y.; Jin, Q.; Ma, J. PDL: An efficient prediction-based false data injection attack detection and location in smart grid. In Proceedings of the 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, Japan, 23–27 July 2018; Volume 2, pp. 676–681. [Google Scholar]
- Zhao, J.; Zhang, G.; La Scala, M.; Dong, Z.Y.; Chen, C.; Wang, J. Short-term state forecasting-aided method for detection of smart grid general false data injection attacks. IEEE Trans. Smart Grid 2015, 8, 1580–1590. [Google Scholar] [CrossRef]
- Anwar, A.; Mahmood, A.N.; Tari, Z. Ensuring data integrity of OPF module and energy database by detecting changes in power flow patterns in smart grids. IEEE Trans. Ind. Inform. 2017, 13, 3299–3311. [Google Scholar] [CrossRef]
- Li, Y.; Li, J.; Luo, X.; Wang, X.; Guan, X. Cyber attack detection and isolation for smart grids via unknown input observer. In Proceedings of the 2018 37th Chinese Control Conference (CCC), Wuhan, China, 25–27 July 2018; pp. 6207–6212. [Google Scholar]
- Wang, X.; Luo, X.; Zhang, M.; Guan, X. Distributed detection and isolation of false data injection attacks in smart grids via nonlinear unknown input observers. Int. J. Electr. Power Energy Syst. 2019, 110, 208–222. [Google Scholar] [CrossRef]
- Sahoo, S.; Mishra, S.; Peng, J.C.H.; Dragičević, T. A stealth cyber-attack detection strategy for DC microgrids. IEEE Trans. Power Electron. 2018, 34, 8162–8174. [Google Scholar] [CrossRef] [Green Version]
- Li, B.; Ding, T.; Huang, C.; Zhao, J.; Yang, Y.; Chen, Y. Detecting False Data Injection AttacNs Against Power System State Estimation with Fast Go-Decomposition (GoDec) Approach. IEEE Trans. Ind. Inform. 2014, 15, 2892–2904. [Google Scholar] [CrossRef]
- Liu, L.; Esmalifalak, M.; Ding, Q.; Emesih, V.A.; Han, Z. Detecting false data injection attacks on power grid by sparse optimization. IEEE Trans. Smart Grid 2014, 5, 612–621. [Google Scholar] [CrossRef]
- Kushal, T.R.B.; Lai, K.; Illindala, M.S. Risk-based mitigation of load curtailment cyber attack using intelligent agents in a shipboard power system. IEEE Trans. Smart Grid 2018, 10, 4741–4750. [Google Scholar] [CrossRef]
- Singh, S.K.; Khanna, K.; Bose, R.; Panigrahi, B.K.; Joshi, A. Joint-transformation-based detection of false data injection attacks in smart grid. IEEE Trans. Ind. Inform. 2017, 14, 89–97. [Google Scholar] [CrossRef]
- Ashok, A.; Govindarasu, M.; Ajjarapu, V. Online detection of stealthy false data injection attacks in power system state estimation. IEEE Trans. Smart Grid 2016, 9, 1636–1646. [Google Scholar] [CrossRef]
- Kumar, R.J.R.; Sikdar, B. Efficient detection of false data injection attacks on AC state estimation in smart grids. In Proceedings of the 2017 IEEE Conference on Communications and Network Security (CNS), Las Vegas, NV, USA, 9–11 October 2017; pp. 411–415. [Google Scholar]
- Sridhar, S.; Govindarasu, M. Model-based attack detection and mitigation for automatic generation control. IEEE Trans. Smart Grid 2014, 5, 580–591. [Google Scholar] [CrossRef]
- Hao, J.; Kang, E.; Sun, J.; Wang, Z.; Meng, Z.; Li, X.; Ming, Z. An adaptive Markov strategy for defending smart grid false data injection from malicious attackers. IEEE Trans. Smart Grid 2016, 9, 2398–2408. [Google Scholar] [CrossRef]
- Ameli, A.; Hooshyar, A.; El-Saadany, E.F. Development of a cyber-resilient line current differential relay. IEEE Trans. Ind. Inform. 2018, 15, 305–318. [Google Scholar] [CrossRef]
- Chaojun, G.; Jirutitijaroen, P.; Motani, M. Detecting false data injection attacks in AC state estimation. IEEE Trans. Smart Grid 2015, 6, 2476–2483. [Google Scholar] [CrossRef]
- Khanna, K.; Singh, S.K.; Panigrahi, B.K.; Bose, R.; Joshi, A. On detecting false data injection with limited network information using transformation based statistical techniques. In Proceedings of the 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, USA, 16–20 July 2017; pp. 1–5. [Google Scholar]
- Li, S.; Yılmaz, Y.; Wang, X. Quickest detection of false data injection attack in wide-area smart grids. IEEE Trans. Smart Grid 2014, 6, 2725–2735. [Google Scholar] [CrossRef]
- Huang, Y.; Tang, J.; Cheng, Y.; Li, H.; Campbell, K.A.; Han, Z. Real-time detection of false data injection in smart grid networks: An adaptive CUSUM method and analysis. IEEE Syst. J. 2014, 10, 532–543. [Google Scholar] [CrossRef]
- Yip, S.C.; Wong, K.; Hew, W.P.; Gan, M.T.; Phan, R.C.W.; Tan, S.W. Detection of energy theft and defective smart meters in smart grids using linear regression. Int. J. Electr. Power Energy Syst. 2017, 91, 230–240. [Google Scholar] [CrossRef]
- Esmalifalak, M.; Liu, L.; Nguyen, N.; Zheng, R.; Han, Z. Detecting stealthy false data injection using machine learning in smart grid. IEEE Syst. J. 2014, 11, 1644–1652. [Google Scholar] [CrossRef]
- Yan, J.; Tang, B.; He, H. Detection of false data attacks in smart grid with supervised learning. In Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 24–29 July 2016; pp. 1395–1402. [Google Scholar]
- Binna, S.; Kuppannagari, S.R.; Engel, D.; Prasanna, V.K. Subset level detection of false data injection attacks in smart grids. In Proceedings of the 2018 IEEE Conference on Technologies for Sustainability (SusTech), Long Beach, CA, USA, 11–13 November 2018; pp. 1–7. [Google Scholar]
- Foroutan, S.A.; Salmasi, F.R. Detection of false data injection attacks against state estimation in smart grids based on a mixture Gaussian distribution learning method. IET Cyber-Phys. Syst. Theory Appl. 2017, 2, 161–171. [Google Scholar] [CrossRef]
- Vimalkumar, K.; Radhika, N. A big data framework for intrusion detection in smart grids using apache spark. In Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 13–16 September 2017; pp. 198–204. [Google Scholar]
- Jindal, A.; Dua, A.; Kaur, K.; Singh, M.; Kumar, N.; Mishra, S. Decision tree and SVM-based data analytics for theft detection in smart grid. IEEE Trans. Ind. Inform. 2016, 12, 1005–1016. [Google Scholar] [CrossRef]
- Wang, D.; Wang, X.; Zhang, Y.; Jin, L. Detection of power grid disturbances and cyber-attacks based on machine learning. J. Inf. Secur. Appl. 2019, 46, 42–52. [Google Scholar] [CrossRef]
- Khanna, K.; Panigrahi, B.K.; Joshi, A. AI-based approach to identify compromised meters in data integrity attacks on smart grid. IET Gener. Transm. Distrib. 2018, 12, 1052–1066. [Google Scholar] [CrossRef] [Green Version]
- Zhao, H.; Liu, H.; Hu, W.; Yan, X. Anomaly detection and fault analysis of wind turbine components based on deep learning network. Renew. Energy 2018, 127, 825–834. [Google Scholar] [CrossRef]
- Xue, D.; Jing, X.; Liu, H. Detection of false data injection attacks in smart grid utilizing ELM-based OCON framework. IEEE Access 2019, 7, 31762–31773. [Google Scholar] [CrossRef]
- Yang, L.; Li, Y.; Li, Z. Improved-ELM method for detecting false data attack in smart grid. Int. J. Electr. Power Energy Syst. 2017, 91, 183–191. [Google Scholar] [CrossRef]
- Punmiya, R.; Choe, S. Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing. IEEE Trans. Smart Grid 2019, 10, 2326–2329. [Google Scholar] [CrossRef]
- Razavi, R.; Gharipour, A.; Fleury, M.; Akpan, I.J. A practical feature-engineering framework for electricity theft detection in smart grids. Appl. Energy 2019, 238, 481–494. [Google Scholar] [CrossRef]
- McLaughlin, S.; Holbert, B.; Fawaz, A.; Berthier, R.; Zonouz, S. A multi-sensor energy theft detection framework for advanced metering infrastructures. IEEE J. Sel. Areas Commun. 2013, 31, 1319–1330. [Google Scholar] [CrossRef]
- Sedghi, H.; Jonckheere, E. Statistical structure learning to ensure data integrity in smart grid. IEEE Trans. Smart Grid 2015, 6, 1924–1933. [Google Scholar] [CrossRef]
- Sedghi, H.; Jonckheere, E. Statistical structure learning of smart grid for detection of false data injection. In Proceedings of the 2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, Canada, 21–25 July 2013; pp. 1–5. [Google Scholar]
- Zanetti, M.; Jamhour, E.; Pellenz, M.; Penna, M.; Zambenedetti, V.; Chueiri, I. A tunable fraud detection system for advanced metering infrastructure using short-lived patterns. IEEE Trans. Smart Grid 2017, 10, 830–840. [Google Scholar] [CrossRef]
- Viegas, J.L.; Vieira, S.M. Clustering-based novelty detection to uncover electricity theft. In Proceedings of the 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy, 9–12 July 2017; pp. 1–6. [Google Scholar]
- Ahmed, S.; Lee, Y.; Hyun, S.H.; Koo, I. Unsupervised machine learning-based detection of covert data integrity assault in smart grid networks utilizing isolation forest. IEEE Trans. Inf. Forensics Secur. 2019, 14, 2765–2777. [Google Scholar] [CrossRef]
- Wei, L.; Gao, D.; Luo, C. False data injection attacks detection with deep belief networks in smart grid. In Proceedings of the 2018 Chinese Automation Congress (CAC), Xi’an, China, 30 November–2 December 2018; pp. 2621–2625. [Google Scholar]
- He, Y.; Mendis, G.J.; Wei, J. Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism. IEEE Trans. Smart Grid 2017, 8, 2505–2516. [Google Scholar] [CrossRef]
- Ghasemi, A.A.; Gitizadeh, M. Detection of illegal consumers using pattern classification approach combined with Levenberg–Marquardt method in smart grid. Int. J. Electr. Power Energy Syst. 2018, 99, 363–375. [Google Scholar] [CrossRef]
- Ntalampiras, S. Fault diagnosis for smart grids in pragmatic conditions. IEEE Trans. Smart Grid 2016, 9, 1964–1971. [Google Scholar] [CrossRef]
- Kurt, M.N.; Ogundijo, O.; Li, C.; Wang, X. Online cyber-attack detection in smart grid: A reinforcement learning approach. IEEE Trans. Smart Grid 2018, 10, 5174–5185. [Google Scholar] [CrossRef] [Green Version]
- Adhikari, U.; Morris, T.H.; Pan, S. Applying non-nested generalized exemplars classification for cyber-power event and intrusion detection. IEEE Trans. Smart Grid 2016, 9, 3928–3941. [Google Scholar] [CrossRef]
- Adhikari, U.; Morris, T.H.; Pan, S. Applying hoeffding adaptive trees for real-time cyber-power event and intrusion classification. IEEE Trans. Smart Grid 2017, 9, 4049–4060. [Google Scholar] [CrossRef]
- Pan, S.; Morris, T.; Adhikari, U. Classification of disturbances and cyber-attacks in power systems using heterogeneous time-synchronized data. IEEE Trans. Ind. Inform. 2015, 11, 650–662. [Google Scholar] [CrossRef]
- Adhikari, U.; Morris, T.H.; Pan, S. A causal event graph for cyber-power system events using synchrophasor. In Proceedings of the 2014 IEEE PES General Meeting| Conference & Exposition, National Harbor, MD, USA, 27–31 July 2014; pp. 1–5. [Google Scholar]
- Beg, O.A.; Nguyen, L.V.; Johnson, T.T.; Davoudi, A. Signal temporal logic-based attack detection in DC microgrids. IEEE Trans. Smart Grid 2018, 10, 3585–3595. [Google Scholar] [CrossRef]
- Ding, Y.; Liu, J. Real-time false data injection attack detection in energy internet using online robust principal component analysis. In Proceedings of the 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China, 26–28 November 2017; pp. 1–6. [Google Scholar]
- Li, B.; Lu, R.; Wang, W.; Choo, K.K.R. Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system. J. Parallel Distrib. Comput. 2017, 103, 32–41. [Google Scholar] [CrossRef]
- Villar-Rodriguez, E.; Del Ser, J.; Oregi, I.; Bilbao, M.N.; Gil-Lopez, S. Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis. Energy 2017, 137, 118–128. [Google Scholar] [CrossRef] [Green Version]
- Saad, A.A.; Faddel, S.; Mohammed, O. A secured distributed control system for future interconnected smart grids. Appl. Energy 2019, 243, 57–70. [Google Scholar] [CrossRef]
- Lu, Z.; Wang, W.; Wang, C. From jammer to gambler: Modeling and detection of jamming attacks against time-critical traffic. In Proceedings of the 2011 Proceedings IEEE INFOCOM, Shanghai, China, 10–15 April 2011; pp. 1871–1879. [Google Scholar]
- Wang, X.; Yi, P. Security framework for wireless communications in smart distribution grid. IEEE Trans. Smart Grid 2011, 2, 809–818. [Google Scholar] [CrossRef]
- Diovu, R.; Agee, J. A cloud-based openflow firewall for mitigation against DDoS attacks in smart grid AMI networks. In Proceedings of the 2017 IEEE PES PowerAfrica, Accra, Ghana, 27–30 June 2017; pp. 28–33. [Google Scholar]
- Morris, T.H.; Jones, B.A.; Vaughn, R.B.; Dandass, Y.S. Deterministic intrusion detection rules for MODBUS protocols. In Proceedings of the 2013 46th Hawaii International Conference on System Sciences, Wailea, HI, USA, 7–10 January 2013; pp. 1773–1781. [Google Scholar]
- Li, H.; Liu, G.; Jiang, W.; Dai, Y. Designing snort rules to detect abnormal DNP3 network data. In Proceedings of the 2015 International Conference on Control, Automation and Information Sciences (ICCAIS), Wailea, HI, USA, 7–10 January 2015; pp. 343–348. [Google Scholar]
- Kemal, M.S.; Aoudi, W.; Olsen, R.L.; Almgren, M.; Schwefel, H.P. Model-free detection of cyberattacks on voltage control in distribution grids. In Proceedings of the 2019 15th European Dependable Computing Conference (EDCC), Naples, Italy, 17–20 September 2019; pp. 171–176. [Google Scholar]
- Wang, J.; Shi, D.; Li, Y.; Chen, J.; Ding, H.; Duan, X. Distributed framework for detecting PMU data manipulation attacks with deep autoencoders. IEEE Trans. Smart Grid 2018, 10, 4401–4410. [Google Scholar] [CrossRef]
- Cui, M.; Wang, J.; Yue, M. Machine learning-based anomaly detection for load forecasting under cyberattacks. IEEE Trans. Smart Grid 2019, 10, 5724–5734. [Google Scholar] [CrossRef]
- Berthier, R.; Sanders, W.H. Specification-based intrusion detection for advanced metering infrastructures. In Proceedings of the 2011 IEEE 17th Pacific Rim International Symposium on Dependable Computing, Pasadena, CA, USA, 12–14 December 2011; pp. 184–193. [Google Scholar]
- Hong, J.; Liu, C.C.; Govindarasu, M. Detection of cyber intrusions using network-based multicast messages for substation automation. In Proceedings of the ISGT 2014, Washington, DC, USA, 19–22 February 2014; pp. 1–5. [Google Scholar]
- Smith, S.W. Cryptographic scalability challenges in the smart grid. In Proceedings of the 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA, 16–20 January 2012; pp. 1–3. [Google Scholar]
- Wu, D.; Zhou, C. Fault-tolerant and scalable key management for smart grid. IEEE Trans. Smart Grid 2011, 2, 375–381. [Google Scholar] [CrossRef]
- Rosinger, C.; Uslar, M. Smart grid security: Iec 62351 and other relevant standards. In Standardization in Smart Grids; Springer: Berlin/Heidelberg, Germany, 2013; pp. 129–146. [Google Scholar]
- Wang, Q.; Khurana, H.; Huang, Y.; Nahrstedt, K. Time valid one-time signature for time-critical multicast data authentication. In Proceedings of the IEEE INFOCOM 2009, Rio de Janeiro, Brazil, 19–25 April 2009; pp. 1233–1241. [Google Scholar]
- Pillitteri, V.Y.; Brewer, T.L. Guidelines for Smart Grid Cybersecurity; National Institute of Standards and Technology: Gaithersburg, MD, USA, 2014. [Google Scholar] [CrossRef]
- Tesfay, T.T.; Hubaux, J.P.; Le Boudec, J.Y.; Oechslin, P. Cyber-secure communication architecture for active power distribution networks. In Proceedings of the 29th Annual ACM Symposium On Applied Computing, Gyeongju, Republic of Korea, 24–28 March 2014; pp. 545–552. [Google Scholar]
- Lasseter, R.H. Microgrids. In Proceedings of the 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No. 02CH37309), New York, NY, USA, 27–31 January 2002; IEEE: New York, NY, USA, 2002; Volume 1, pp. 305–308. [Google Scholar]
- Isikman, A.O.; Altun, C.; Uludag, S.; Tavli, B. Power scheduling in privacy enhanced microgrid networks with renewables and storage. In Proceedings of the 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 9–12 January 2016; pp. 405–410. [Google Scholar]
- Dalamagkas, C.; Sarigiannidis, P.; Ioannidis, D.; Iturbe, E.; Nikolis, O.; Ramos, F.; Rios, E.; Sarigiannidis, A.; Tzovaras, D. A survey on honeypots, honeynets and their applications on smart grid. In Proceedings of the 2019 IEEE Conference on Network Softwarization (NetSoft), Paris, France, 24–28 June 2019; pp. 93–100. [Google Scholar]
- Rist, L. Introducing conpot. The Honeynet Project. Available online: https://www.honeynet.org/2013/05/11/introducing-conpot/ (accessed on 14 November 2022).
- Jicha, A.; Patton, M.; Chen, H. SCADA honeypots: An in-depth analysis of Conpot. In Proceedings of the 2016 IEEE conference on intelligence and security informatics (ISI), Tucson, AZ, USA, 28–30 September 2016; pp. 196–198. [Google Scholar]
- Paverd, A.J.; Martin, A.P. Hardware security for device authentication in the smart grid. In Proceedings of the International Workshop on Smart Grid Security, Berlin, Germany, 3 December 2012; Springer: Berlin/Heidelberg, Germany, 2012; pp. 72–84. [Google Scholar]
- Castelluccia, C.; Francillon, A.; Perito, D.; Soriente, C. On the difficulty of software-based attestation of embedded devices. In Proceedings of the 16th ACM conference on Computer and Communications Security, Chicago, IL, USA, 9–13 November 2009; pp. 400–409. [Google Scholar]
- Liu, Y.; Ning, P.; Reiter, M.K. False data injection attacks against state estimation in electric power grids. ACM Trans. Inf. Syst. Secur. TISSEC 2011, 14, 13. [Google Scholar] [CrossRef]
- Pal, S.; Sikdar, B.; Chow, J.H. Classification and detection of PMU data manipulation attacks using transmission line parameters. IEEE Trans. Smart Grid 2017, 9, 5057–5066. [Google Scholar] [CrossRef]
- Wang, Y.; Amin, M.M.; Fu, J.; Moussa, H.B. A novel data analytical approach for false data injection cyber-physical attack mitigation in smart grids. IEEE Access 2017, 5, 26022–26033. [Google Scholar] [CrossRef]
- El Hariri, M.; Harmon, E.; Youssef, T.; Saleh, M.; Habib, H.; Mohammed, O. The iec 61850 sampled measured values protocol: Analysis, threat identification, and feasibility of using nn forecasters to detect spoofed packets. Energies 2019, 12, 3731. [Google Scholar] [CrossRef] [Green Version]
- Li, B.; Lu, R.; Xiao, G. HMM-based fast detection of false data injections in advanced metering infrastructure. In Proceedings of the GLOBECOM 2017–2017 IEEE Global Communications Conference, Singapore, 4–8 December 2017; pp. 1–6. [Google Scholar]
- Marali, M.; Sudarsan, S.D.; Gogioneni, A. Cyber security threats in industrial control systems and protection. In Proceedings of the 2019 International Conference on Advances in Computing and Communication Engineering (ICACCE), Sathyamangalam, India, 4–6 April 2019; pp. 1–7. [Google Scholar]
- Mix, S.; Hadley, M.; Becker, F.; Cenzon, E.; Corrigan, R.; Dood, M.; Edgar, T.; Formea, J.; Goransan, C.; Huntley, C.; et al. IIEEE 1711.2-2019; IEEE Standard for Secure SCADA Communications Protocol (SSCP). IEEE Standards Association: Piscataway, NJ, USA, 2020; pp. 1–37. [CrossRef]
- Ferst, M.K.; de Figueiredo, H.F.; Denardin, G.; Lopes, J. Implementation of secure communication with modbus and transport layer security protocols. In Proceedings of the 2018 13th IEEE International Conference on Industry Applications (INDUSCON), Sao Paulo, Brazil, 12–14 November 2018; pp. 155–162. [Google Scholar]
Regulation, Standard, or Guideline | Summary | Category |
---|---|---|
U.S. Regulation | Energy Policy Act of 2005 | Statutory |
U.S. Regulation | Energy Independence and Security Act of 2007 | Statutory |
NERC CIP Standards | Enforceable set of standards for the Bulk Energy System | Standard |
DHS Nuclear Reactor Cybersecurity | Cybersecurity Framework Implementation Guidance for U.S. Nuclear Power Reactors | Guidance |
ES-C2M2 | Electricity Subsector Capability Maturity Model | Guidance |
DoE | Energy Sector Cybersecurity Framework Implementation Guidance | Guidance |
NIST CSWP 04162018 | Framework for Improving Critical Infrastructure Cybersecurity | Guidance |
NIST TN 2051 | Smart Grid Profile of the NIST Framework | Guidance |
NIST SP 1800-23 | Energy Sector Asset Management | Guidance |
NIST IR 7628 | Guidelines for Smart Grid Cybersecurity | Guidance |
NIST SP 1108r3 | NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 3.0 | Standard |
IEEE C37.1 | Standards for SCADA and Automation Systems | Standard |
IEEE 1379 | Recommended Practice for Data Communications between RTUs and IEDs | Guidance |
IEEE 1646 | Standard Communication Delivery Time Performance Requirements or Electric Power Substation Automation | Standard |
IEEE 1686 | Standard for Intelligent Electronic Devices Cyber Security Capabilities | Standard |
IEEE 692 | Standard for Criteria for Security Systems for Nuclear Power Generating Stations | Standard |
IEEE 1547.3 | Guide for Monitoring, Information Exchange, and Control of Distributed Resources | Guidance |
IEEE P1711 | Trial-Use Standard for a Cryptographic Protocol for Cyber Security of Substation Serial Links | Standard |
IEEE P2030 | IEEE Guide for Smart Grid Interoperability of Energy Technology and Information Technology Operation with the Electric Power System (EPS), End-Use Applications, and Loads | Guidance |
IEEE P1901 | High Speed Power Line Communications | Standard |
IEC 61850 | IED Communications (e.g., GOOSE) | Standard |
IEC 62351 | Security of Communication Protocols | Standard |
IEC 62541 | OPC Unified Architecture Security Model | Standard |
ANSI C12 | Metering Protocol | Standard |
IEEE C37.118 | Synchrophasor Measurements | Standard |
IEC 60870 | Family of Protocols for SCADA Communications | Standard |
IEEE 1815 | DNP3 Protocol | Standard |
Modbus | Modbus Protocol | Standard |
NRC Regulatory Guide 5.83 | Cybersecurity Event Notifications | Guidance |
NRC Regulatory Guide 5.71 | Cybersecurity Programs for Nuclear Facilities | Guidance |
Impacted Security Model Category | Attack Category | Possible Countermeasures | Compromised Application, Protocol, or Device | Attack Example |
---|---|---|---|---|
Availability | Denial of Service | SIEM, IDS, flow entropy, signal strength, sensing time measurement, transmission failure count, pushback, reconfiguration methods | AMI | puppet attack [32] |
smart grid | TDS [43] | |||
PMU, GPS | TSA [40] | |||
False Data Injection Attack | FDIA Detection [51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125] applied in DLP, IDS, SIEM, etc.; Secure DNP3; TLS; SSL; encryption, authentication; PKI | AMI, RTU, EMS, SCADA | [21] | |
Jamming | JADE, anti-jamming, (FHSS, DSSS) | PMU | [126] | |
CRN in WSGN | MAS-SJ [42] | |||
Malware Injection | DLP, IDS, SIEM, Anti-virus, Diversity technique | SCADA, PMU, Control device | Stuxnet [37] | |
SCADA | Duqu [37] | |||
Masquerade attack | DLP, IDS, Secure DNP3, SIEM, TLS, SSL, encryption, authentication, PKI | PLC | [35] | |
Integrity | Man-in-the-middle | Secure DNP3, PKI, TLS, SSL, encryption, authentication | HMI, PLC | eavesdropping |
SCADA | ||||
DNP3, SCADA | ||||
AMI | intercept/alter | |||
Replay attack | Secure DNP3, TLS, SSL, encryption, authentication, PKI | IED, SCADA, PLC | ||
AMI authentication | ||||
Confidentiality | Privacy violation | Secure DNP3, PKI, TLS, SSL, encryption, authentication | Demand response program, smart meters | |
Scanning (IP, Port, Service, Vulnerabilities) | IDS, SIEM, automated security compliance checks | Modbus protocol | Modbus network scanning | |
DNP3 protocol | DNP3 network scanning | |||
Social engineering | Secure DNP3, PKI, SSL, encryption, authentication | Modbus protocol, DNP3 protocol | phishing | |
Modbus protocol, DNP3 protocol | password pilfering | |||
Traffic analysis | Secure DNP3, PKI, SSL, encryption, authentication | Modbus protocol, DNP3 protocol |
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Boeding, M.; Boswell, K.; Hempel, M.; Sharif, H.; Lopez, J., Jr.; Perumalla, K. Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid. Energies 2022, 15, 8692. https://doi.org/10.3390/en15228692
Boeding M, Boswell K, Hempel M, Sharif H, Lopez J Jr., Perumalla K. Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid. Energies. 2022; 15(22):8692. https://doi.org/10.3390/en15228692
Chicago/Turabian StyleBoeding, Matthew, Kelly Boswell, Michael Hempel, Hamid Sharif, Juan Lopez, Jr., and Kalyan Perumalla. 2022. "Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid" Energies 15, no. 22: 8692. https://doi.org/10.3390/en15228692