Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (7): 512-520.doi: 10.23940/ijpe.22.07.p6.512520
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Divya Singhala,*, Laxmi Ahujaa, and Ashish Sethb
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* E-mail address: divyasinghal021@gmail.com
Divya Singhal, Laxmi Ahuja, and Ashish Seth. An Insight into Combating Security Attacks for Smart Grid [J]. Int J Performability Eng, 2022, 18(7): 512-520.
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1. Faheem, M., Shah, S.B.H., Butt, R.A., Raza, B., Anwar, M., Ashraf, M.W., Ngadi, M.A., and Gungor, V.C. Smart Grid Communication and Information Technologies in the Perspective of Industry 4.0: Opportunities and Challenges. 2. Gunduz, M.Z. and Das, R. Analysis of Cyber-attacks on Smart Grid Applications. 3. Yun, M. and Yuxin, B. Research on the Architecture and Key Technology of Internet of Things (IoT) Applied on Smart Grid. 4. Akhtar M.A.K. and Kumar, M. Detection of DDoS Attack Using Naive Bayes Classifier. 5. Wang, P. and Govindarasu, M.Multi-agent Based Attack-resilient System Integrity Protection for Smart Grid. 6. Ghiasi M., Dehghani M., Niknam T., andKavousi-Fard, A. Investigating Overall Structure of Cyber-attacks on Smart-grid Control Systems to Improve Cyber Resilience in Power System. 7. Singh R., Singh A., andBhattacharya P.A Machine Learning Approach for Anomaly Detection to Secure Smart Grid Systems. 8. Kaur, G. and Tomar, P.Genesis of Cloud-Based IoT Systems for Smart Generation. 9. Meloni A., Pegoraro P.A., Atzori L., Benigni A., andSulis S.Cloud-based IoT Solution for State Estimation in Smart Grids: Exploiting Virtualization and Edge-Intelligence Technologies. 10. Anzalchi A., Sundararajan A., Wei L., Moghadasi A., andSarwat A.Future Directions to the Application of Distributed Fog Computing in Smart Grid Systems. 11. Forcan, M. and Maksimović, M.Cloud-fog-based Approach for Smart Grid Monitoring. 12. Ahuja S.P., Czarnecki E., andWillison S.Multi-factor Performance Comparison of Amazon Web Services Elastic Compute Cluster and Google Cloud Platform Compute Engine. 13. Ponce-Jara, M.A., Ruiz, E., Gil, R., Sancristóbal, E., Pérez-Molina, C., and Castro, M. Smart Grid: Assessment of the past and Present in Developed and Developing Countries. 14. Dhaou I.S.B., Kondoro, A., Kakakhel, S.R.U., Westerlund, T., and Tenhunen, H. Internet of Things Technologies for Smart Grid. 15. Acharjee P.Strategy and Implementation of Smart Grids in India. 16. Chawla Y.,Kowalska-Pyzalska, A.and Skowronska-Szmer, A. HSC Research Report. 17. Joseph A.Smart Grid and Retail Competition in India: A Review on Technological and Managerial Initiatives and Challenges. 18. Asaad M., Ahmad F., Alam M.S., andSarfraz M.Smart Grid and Indian Experience: A Review. 19. Dalipi, F. and Yayilgan, S.Y. Security and Privacy Considerations for IoT Application on Smart Grids: Survey and Research Challenges. 20. Reka, S.S. and Dragicevic, T.Future Effectual Role of Energy Delivery: A Comprehensive Review of Internet of Things and Smart Grid. 21. Depuru, S.S.S.R., Wang, L., Devabhaktuni, V., and Gudi, N. Smart Meters for Power Grid—Challenges, Issues, Advantages and Status. 22. Miao, H. and Junshan, Z.A Dependency Graph Approach for Fault Detection and Localization Towards Secure Smart Grid. Smart Grid. 23. Kim, T.T. and Poor, H.V.Strategic Protection against Data Injection Attacks on Power Grids. 24. Chen, P.Y. and Chen, K.C. Intentional Attack and Fusion-based Defense Strategy in Complex Networks. 25. Yan Y., Hu R.Q., Das S.K., Sharif H., andQian Y.An Efficient Security Protocol for Advanced Metering Infrastructure in Smart Grid. 26. Yan Y., Qian Y., Sharif H., andTipper D.A Survey on Cyber Security for Smart Grid Communications. 27. Tidrea A., Korodi A., andSilea I.Cryptographic Considerations for Automation and SCADA Systems Using Trusted Platform Modules. 28. Efthymiou, C. and Kalogridis, G. Smart Grid Privacy via Anonymization of Smart Metering Data. 29. Bekara C.Security Issues and Challenges for the IoT-based Smart Grid. 30. Gunduz, M.Z. and Das, R. A Comparison of Cyber-Security Oriented Testbeds for IoT-based Smart Grids. 31. Mallapuram S., Moulema P., andYu W.A Smart Grid Simulation Testbed Using Matlab/Simulink. 32. Mehmi S., Verma H.K., andSangal A.L.Simulation Modeling of Cloud Computing for Smart Grid Using CloudSim. 33. Zhang P.H., Xiao C.D., Xue Y., andZhang X.L.Modeling and Simulation of Smart Meters Based on Matlab/Simulink Software. 34. Liu X., Golab L., Golab W., Ilyas I.F., andJin S.Smart Meter Data Analytics: Systems, Algorithms, and Benchmarking. 35. Li, Y., Qiu, R. and Jing, S.Intrusion Detection System Using Online Sequence Extreme Learning Machine (OS-ELM) In Advanced Metering Infrastructure of Smart Grid. 36. Lopes, Y., Fernandes, N.C., de Castro, T.B., dos Santos Farias, V., Noce, J.D., Marques, J.P., and Muchaluat-Saade, D.C. Vulnerabilities and Threats in Smart Grid Communication Networks. 37. On T.V., Don N.C., Hang N.M., andVan Le Khoa, T. Hydrodynamic Flw and Salinity Intrusion in the Red River Delta, Vietnam. no, 1, pp.1-6, 2014. 38. Zhang Y.J.A., Schwefel, H.P., Mohsenian-Rad, H., Wietfeld, C., Chen, C., and Gharavi, H. Guest Editorial Special Issue on Communications and Data Analytics in Smart Grid. 39. Aali N.A., Baina A., andEchabbi L.Trust Management Issues for Sensors Security and Privacy in the Smart Grid. 40. Krommyda, M.K. and Kantere, V.The Big Data Era: Data Management Novelties for Visualizing, Exploring, and Processing Big Data. 41. Iyer, G.N. and Iyer, G.N.Smart Grid and Cloud Computing. 42. Sharma, O. and Anusha, S.Large-scale Data Streaming in Fog Computing and Its Applications. 43. Saini, D. and Saini, J.Examining Data Lake Design Principle for Cloud Computing Technology and IoT. 44. Kaur, J. and Sharma, M.Extending IoTs into the Cloud-based Platform for Examining Amazon Web Services. 45. L. More, “3002017143_An Introduction to AI_ its Use Cases_ and Requirements for the Electric Power Industry,” no. August, 2019. 46. Mohanta B.K., Jena D., Satapathy U., andPatnaik S.Survey on IoT Security: Challenges and Solution Using Machine Learning, Artificial Intelligence and Blockchain Technology. 47. Musiolik G.Predictability of AI Decisions. |
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