Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey (2401.05896v1)

Published 11 Jan 2024 in cs.CR, cs.LG, and cs.NI

Abstract: As smart grids (SG) increasingly rely on advanced technologies like sensors and communication systems for efficient energy generation, distribution, and consumption, they become enticing targets for sophisticated cyberattacks. These evolving threats demand robust security measures to maintain the stability and resilience of modern energy systems. While extensive research has been conducted, a comprehensive exploration of proactive cyber defense strategies utilizing Deep Learning (DL) in {SG} remains scarce in the literature. This survey bridges this gap, studying the latest DL techniques for proactive cyber defense. The survey begins with an overview of related works and our distinct contributions, followed by an examination of SG infrastructure. Next, we classify various cyber defense techniques into reactive and proactive categories. A significant focus is placed on DL-enabled proactive defenses, where we provide a comprehensive taxonomy of DL approaches, highlighting their roles and relevance in the proactive security of SG. Subsequently, we analyze the most significant DL-based methods currently in use. Further, we explore Moving Target Defense, a proactive defense strategy, and its interactions with DL methodologies. We then provide an overview of benchmark datasets used in this domain to substantiate the discourse.{ This is followed by a critical discussion on their practical implications and broader impact on cybersecurity in Smart Grids.} The survey finally lists the challenges associated with deploying DL-based security systems within SG, followed by an outlook on future developments in this key field.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (168)
  1. A. U. Rehman, G. Hafeez, F. R. Albogamy, Z. Wadud, F. Ali, I. Khan, G. Rukh, and S. Khan, “An efficient energy management in smart grid considering demand response program and renewable energy sources,” IEEE Access, vol. 9, pp. 148 821–148 844, 2021.
  2. M. I. Ibrahem, M. Mahmoud, M. M. Fouda, F. Alsolami, W. Alasmary, and X. Shen, “Privacy preserving and efficient data collection scheme for ami networks using deep learning,” IEEE Internet of Things Journal, vol. 8, no. 23, pp. 17 131–17 146, 2021.
  3. M. Yigit, V. C. Gungor, G. Tuna, M. Rangoussi, and E. Fadel, “Power line communication technologies for smart grid applications: A review of advances and challenges,” Computer Networks, vol. 70, pp. 366–383, 2014.
  4. A. Alsharif, M. Nabil, A. Sherif, M. Mahmoud, and M. Song, “Mdms: Efficient and privacy-preserving multidimension and multisubset data collection for ami networks,” IEEE Internet of Things Journal, vol. 6, no. 6, pp. 10 363–10 374, 2019.
  5. P. Kumar, Y. Lin, G. Bai, A. Paverd, J. S. Dong, and A. Martin, “Smart grid metering networks: A survey on security, privacy and open research issues,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2886–2927, 2019.
  6. A. Alsharif, M. Nabil, M. M. Mahmoud, and M. Abdallah, “Epda: Efficient and privacy-preserving data collection and access control scheme for multi-recipient ami networks,” IEEE Access, vol. 7, pp. 27 829–27 845, 2019.
  7. K. N. Mallikarjunan, K. Muthupriya, and S. M. Shalinie, “A survey of distributed denial of service attack,” in 2016 10th International Conference on Intelligent Systems and Control (ISCO).   IEEE, 2016, pp. 1–6.
  8. D. Jin, D. M. Nicol, and G. Yan, “An event buffer flooding attack in dnp3 controlled scada systems,” in Proceedings of the 2011 Winter Simulation Conference (WSC).   IEEE, 2011, pp. 2614–2626.
  9. N. Komninos, E. Philippou, and A. Pitsillides, “Survey in smart grid and smart home security: Issues, challenges and countermeasures,” IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 1933–1954, 2014.
  10. C. Kim, S.-Y. Chang, J. Kim, D. Lee, and J. Kim, “Automated, reliable zero-day malware detection based on autoencoding architecture,” IEEE Transactions on Network and Service Management, 2023.
  11. A. Ayad, M. Khalaf, M. Salama, and E. F. El-Saadany, “Mitigation of false data injection attacks on automatic generation control considering nonlinearities,” Electric Power Systems Research, vol. 209, p. 107958, 2022.
  12. A. Segall, “Distributed network protocols,” IEEE transactions on Information Theory, vol. 29, no. 1, pp. 23–35, 1983.
  13. Y. Yang, K. McLaughlin, T. Littler, S. Sezer, B. Pranggono, and H. Wang, “Intrusion detection system for iec 60870-5-104 based scada networks,” in 2013 IEEE power & energy society general meeting.   IEEE, 2013, pp. 1–5.
  14. P. Maynard, K. McLaughlin, and B. Haberler, “Towards understanding man-in-the-middle attacks on iec 60870-5-104 scada networks,” in 2nd International Symposium for ICS & SCADA Cyber Security Research 2014 (ICS-CSR 2014) 2, 2014, pp. 30–42.
  15. S. Ponpuram Sathar, S. Al-Kuwari, A. Albaseer, M. Qaraqe, and M. M. Abdallah, “Mitigating IEC-60870-5-104 vulnerabilities: Anomaly detection in smart grid based on LSTM autoencoder,” in 2023 International Symposium on Networks, Computers and Communications (ISNCC): Trust, Security and Privacy (ISNCC-2023 TSP), October 2023, p. 6.
  16. P. Radoglou-Grammatikis, P. Sarigiannidis, I. Giannoulakis, E. Kafetzakis, and E. Panaousis, “Attacking iec-60870-5-104 scada systems,” in 2019 IEEE World Congress on Services (SERVICES), vol. 2642.   IEEE, 2019, pp. 41–46.
  17. E. Hodo, S. Grebeniuk, H. Ruotsalainen, and P. Tavolato, “Anomaly detection for simulated iec-60870-5-104 trafiic,” in Proceedings of the 12th international conference on availability, reliability and security, 2017, pp. 1–7.
  18. Y. Xu, Y. Yang, T. Li, J. Ju, and Q. Wang, “Review on cyber vulnerabilities of communication protocols in industrial control systems,” in 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2).   IEEE, 2017, pp. 1–6.
  19. I. Darwish, O. Igbe, O. Celebi, T. Saadawi, and J. Soryal, “Smart grid dnp3 vulnerability analysis and experimentation,” in 2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing.   IEEE, 2015, pp. 141–147.
  20. A. Elmasry, A. Albaseer, and M. M. Abdallah, “OpenPLC and lib61850 smart grid testbed: Performance evaluation and analysis of GOOSE communication,” in 2023 International Symposium on Networks, Computers and Communications (ISNCC): Trust, Security and Privacy (ISNCC-2023 TSP), October 2023, p. 6.
  21. C. Wilkerson and M. El Hariri, “Iec 61850-based renewable energy systems: A survey on cybersecurity aspects,” in 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe).   IEEE, 2022, pp. 1–6.
  22. M. K. Hasan, A. A. Habib, Z. Shukur, F. Ibrahim, S. Islam, and M. A. Razzaque, “Review on cyber-physical and cyber-security system in smart grid: Standards, protocols, constraints, and recommendations,” Journal of Network and Computer Applications, p. 103540, 2022.
  23. J. Hiller, M. Henze, M. Serror, E. Wagner, J. N. Richter, and K. Wehrle, “Secure low latency communication for constrained industrial iot scenarios,” in 2018 IEEE 43rd Conference on Local Computer Networks (LCN).   IEEE, 2018, pp. 614–622.
  24. T. Krause, R. Ernst, B. Klaer, I. Hacker, and M. Henze, “Cybersecurity in power grids: challenges and opportunities,” Sensors, vol. 21, no. 18, p. 6225, 2021.
  25. H. Teryak, A. Albaseer, M. Abdallah, S. Al-Kuwari, and M. Qaraqe, “Double-edged defense: Thwarting cyber attacks and adversarial machine learning in iec 60870-5-104 smart grids,” IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 629–642, 2023.
  26. Y. Xin, L. Kong, Z. Liu, Y. Chen, Y. Li, H. Zhu, M. Gao, H. Hou, and C. Wang, “Machine learning and deep learning methods for cybersecurity,” Ieee access, vol. 6, pp. 35 365–35 381, 2018.
  27. A. Halbouni, T. S. Gunawan, M. H. Habaebi, M. Halbouni, M. Kartiwi, and R. Ahmad, “Machine learning and deep learning approaches for cybersecurity: A review,” IEEE Access, vol. 10, pp. 19 572–19 585, 2022.
  28. P. Podder, S. Bharati, M. Mondal, P. K. Paul, and U. Kose, “Artificial neural network for cybersecurity: A comprehensive review,” arXiv preprint arXiv:2107.01185, 2021.
  29. Y. Yang, K. Zheng, C. Wu, and Y. Yang, “Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network,” Sensors, vol. 19, no. 11, p. 2528, 2019.
  30. C. Hu, J. Yan, and X. Liu, “Reinforcement learning-based adaptive feature boosting for smart grid intrusion detection,” IEEE Transactions on Smart Grid, 2022.
  31. H. Chaudhary, A. Detroja, P. Prajapati, and P. Shah, “A review of various challenges in cybersecurity using artificial intelligence,” in 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS).   IEEE, 2020, pp. 829–836.
  32. L. Deng, D. Yu et al., “Deep learning: methods and applications,” Foundations and trends® in signal processing, vol. 7, no. 3–4, pp. 197–387, 2014.
  33. J. Ding, A. Qammar, Z. Zhang, A. Karim, and H. Ning, “Cyber threats to smart grids: Review, taxonomy, potential solutions, and future directions,” Energies, vol. 15, no. 18, p. 6799, 2022.
  34. T. Berghout, M. Benbouzid, and S. Muyeen, “Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects,” International Journal of Critical Infrastructure Protection, p. 100547, 2022.
  35. G. Kocher and G. Kumar, “Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges,” Soft Computing, vol. 25, no. 15, pp. 9731–9763, 2021.
  36. H. Liu and B. Lang, “Machine learning and deep learning methods for intrusion detection systems: A survey,” applied sciences, vol. 9, no. 20, p. 4396, 2019.
  37. M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, “Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study,” Journal of Information Security and Applications, vol. 50, p. 102419, 2020.
  38. M. Massaoudi, H. Abu-Rub, S. S. Refaat, I. Chihi, and F. S. Oueslati, “Deep learning in smart grid technology: A review of recent advancements and future prospects,” IEEE Access, vol. 9, pp. 54 558–54 578, 2021.
  39. J.-H. Cho, D. P. Sharma, H. Alavizadeh, S. Yoon, N. Ben-Asher, T. J. Moore, D. S. Kim, H. Lim, and F. F. Nelson, “Toward proactive, adaptive defense: A survey on moving target defense,” IEEE Communications Surveys & Tutorials, vol. 22, no. 1, pp. 709–745, 2020.
  40. R. E. Navas, F. Cuppens, N. B. Cuppens, L. Toutain, and G. Z. Papadopoulos, “Mtd, where art thou? a systematic review of moving target defense techniques for iot,” IEEE internet of things journal, vol. 8, no. 10, pp. 7818–7832, 2020.
  41. R. Sun, Y. Zhu, J. Fei, and X. Chen, “A survey on moving target defense: Intelligently affordable, optimized and self-adaptive,” Applied Sciences, vol. 13, no. 9, p. 5367, 2023.
  42. D. Upadhyay and S. Sampalli, “Scada (supervisory control and data acquisition) systems: Vulnerability assessment and security recommendations,” Computers & Security, vol. 89, p. 101666, 2020.
  43. P. Siano, “Demand response and smart grids—a survey,” Renewable and sustainable energy reviews, vol. 30, pp. 461–478, 2014.
  44. J. Jo and J. Park, “Demand-side management with shared energy storage system in smart grid,” IEEE Transactions on Smart Grid, vol. 11, no. 5, pp. 4466–4476, 2020.
  45. W. Zhong, K. Xie, Y. Liu, C. Yang, S. Xie, and Y. Zhang, “Online control and near-optimal algorithm for distributed energy storage sharing in smart grid,” IEEE Transactions on Smart Grid, vol. 11, no. 3, pp. 2552–2562, 2019.
  46. A. Ghosal and M. Conti, “Key management systems for smart grid advanced metering infrastructure: A survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2831–2848, 2019.
  47. Z. Shuai, Y. Sun, Z. J. Shen, W. Tian, C. Tu, Y. Li, and X. Yin, “Microgrid stability: Classification and a review,” Renewable and Sustainable Energy Reviews, vol. 58, pp. 167–179, 2016.
  48. Y. Li and J. Yan, “Cybersecurity of smart inverters in the smart grid: A survey,” IEEE Transactions on Power Electronics, vol. 38, no. 2, pp. 2364–2383, 2023.
  49. Y. Shah and S. Sengupta, “A survey on classification of cyber-attacks on iot and iiot devices,” in 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).   IEEE, 2020, pp. 0406–0413.
  50. B. Tang, Z. Chen, G. Hefferman, S. Pei, T. Wei, H. He, and Q. Yang, “Incorporating intelligence in fog computing for big data analysis in smart cities,” IEEE Transactions on Industrial informatics, vol. 13, no. 5, pp. 2140–2150, 2017.
  51. R. Mitchell and R. Chen, “Behavior-rule based intrusion detection systems for safety critical smart grid applications,” IEEE Transactions on Smart Grid, vol. 4, no. 3, pp. 1254–1263, 2013.
  52. H. Bao, R. Lu, B. Li, and R. Deng, “Blithe: Behavior rule-based insider threat detection for smart grid,” IEEE Internet of Things Journal, vol. 3, no. 2, pp. 190–205, 2015.
  53. Y. Li, Y. Qin, P. Zhang, and A. Herzberg, “Sdn-enabled cyber-physical security in networked microgrids,” IEEE Transactions on Sustainable Energy, vol. 10, no. 3, pp. 1613–1622, 2018.
  54. K. Jhala, P. Pradhan, and B. Natarajan, “Perturbation-based diagnosis of false data injection attack using distributed energy resources,” IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 1589–1601, 2020.
  55. M. A. Faisal, Z. Aung, J. R. Williams, and A. Sanchez, “Securing advanced metering infrastructure using intrusion detection system with data stream mining,” in Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2012, Kuala Lumpur, Malaysia, May 29, 2012. Proceedings.   Springer, 2012, pp. 96–111.
  56. ——, “Data-stream-based intrusion detection system for advanced metering infrastructure in smart grid: A feasibility study,” IEEE Systems Journal, vol. 9, no. 1, pp. 31–44, 2015.
  57. R. Vijayanand, D. Devaraj, and B. Kannapiran, “Support vector machine based intrusion detection system with reduced input features for advanced metering infrastructure of smart grid,” in 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), 2017, pp. 1–7.
  58. I. Ullah and Q. H. Mahmoud, “An intrusion detection framework for the smart grid,” in 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), 2017, pp. 1–5.
  59. R. Samrin and D. Vasumathi, “Review on anomaly based network intrusion detection system,” in 2017 international conference on electrical, electronics, communication, computer, and optimization techniques (ICEECCOT).   IEEE, 2017, pp. 141–147.
  60. P.-H. Wang, I.-E. Liao, K.-F. Kao, and J.-Y. Huang, “An intrusion detection method based on log sequence clustering of honeypot for modbus tcp protocol,” in 2018 IEEE International Conference on Applied System Invention (ICASI), 2018, pp. 255–258.
  61. J. Franco, A. Aris, B. Canberk, and A. S. Uluagac, “A survey of honeypots and honeynets for internet of things, industrial internet of things, and cyber-physical systems,” IEEE Communications Surveys & Tutorials, vol. 23, no. 4, pp. 2351–2383, 2021.
  62. A. Albaseer and M. Abdallah, “Fine-tuned lstm-based model for efficient honeypot-based network intrusion detection system in smart grid networks,” in 2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA).   IEEE, 2022, pp. 1–6.
  63. ——, “Privacy-preserving honeypot-based detector in smart grid networks: A new design for quality-assurance and fair incentives federated learning framework,” in 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC).   IEEE, 2023, pp. 722–727.
  64. W. Tian, M. Du, X. Ji, G. Liu, Y. Dai, and Z. Han, “Honeypot detection strategy against advanced persistent threats in industrial internet of things: A prospect theoretic game,” IEEE Internet of Things Journal, vol. 8, no. 24, pp. 17 372–17 381, 2021.
  65. A. Patel, H. Alhussian, J. M. Pedersen, B. Bounabat, J. C. Júnior, and S. Katsikas, “A nifty collaborative intrusion detection and prevention architecture for smart grid ecosystems,” Computers & Security, vol. 64, pp. 92–109, 2017.
  66. P. Jokar and V. C. Leung, “Intrusion detection and prevention for zigbee-based home area networks in smart grids,” IEEE Transactions on Smart Grid, vol. 9, no. 3, pp. 1800–1811, 2016.
  67. S. Garg, K. Kaur, G. Kaddoum, J. J. Rodrigues, and M. Guizani, “Secure and lightweight authentication scheme for smart metering infrastructure in smart grid,” IEEE Transactions on Industrial Informatics, vol. 16, no. 5, pp. 3548–3557, 2019.
  68. D. Syed, S. S. Refaat, and O. Bouhali, “Privacy preservation of data-driven models in smart grids using homomorphic encryption,” Information, vol. 11, no. 7, p. 357, 2020.
  69. C. Dishington, D. P. Sharma, D. S. Kim, J.-H. Cho, T. J. Moore, and F. F. Nelson, “Security and performance assessment of ip multiplexing moving target defence in software defined networks,” in 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).   IEEE, 2019, pp. 288–295.
  70. H. Alavizadeh, J. B. Hong, J. Jang-Jaccard, and D. S. Kim, “Comprehensive security assessment of combined mtd techniques for the cloud,” in Proceedings of the 5th ACM Workshop on Moving Target Defense, 2018, pp. 11–20.
  71. H. Alavizadeh, J. B. Hong, D. S. Kim, and J. Jang-Jaccard, “Evaluating the effectiveness of shuffle and redundancy mtd techniques in the cloud,” Computers & Security, vol. 102, p. 102091, 2021.
  72. D. Ma, Z. Tang, X. Sun, L. Guo, L. Wang, and K. Chen, “Game theory approaches for evaluating the deception-based moving target defense,” in Proceedings of the 9th ACM Workshop on Moving Target Defense, 2022, pp. 67–77.
  73. S. Sengupta and S. Kambhampati, “Multi-agent reinforcement learning in bayesian stackelberg markov games for adaptive moving target defense,” arXiv preprint arXiv:2007.10457, 2020.
  74. K. Ferguson-Walter, S. Fugate, J. Mauger, and M. Major, “Game theory for adaptive defensive cyber deception,” in Proceedings of the 6th Annual Symposium on Hot Topics in the Science of Security, 2019, pp. 1–8.
  75. H. Li and Z. Zheng, “Optimal timing of moving target defense: A stackelberg game model,” in MILCOM 2019-2019 IEEE Military Communications Conference (MILCOM).   IEEE, 2019, pp. 1–6.
  76. M. A. R. Al Amin, S. Shetty, L. Njilla, D. K. Tosh, and C. Kamhoua, “Hidden markov model and cyber deception for the prevention of adversarial lateral movement,” IEEE Access, vol. 9, pp. 49 662–49 682, 2021.
  77. C. Lei, D.-H. Ma, and H.-Q. Zhang, “Optimal strategy selection for moving target defense based on markov game,” IEEE Access, vol. 5, pp. 156–169, 2017.
  78. J. Zheng and A. Siami Namin, “A markov decision process to determine optimal policies in moving target,” in Proceedings of the 2018 ACM SIGSAC conference on computer and communications security, 2018, pp. 2321–2323.
  79. X.-L. Xiong, L. Yang, and G.-S. Zhao, “Effectiveness evaluation model of moving target defense based on system attack surface,” IEEE Access, vol. 7, pp. 9998–10 014, 2019.
  80. A. Amich and B. Eshete, “Morphence: Moving target defense against adversarial examples,” in Annual Computer Security Applications Conference, 2021, pp. 61–75.
  81. X. Xu, H. Hu, Y. Liu, J. Tan, H. Zhang, and H. Song, “Moving target defense of routing randomization with deep reinforcement learning against eavesdropping attack,” Digital Communications and Networks, vol. 8, no. 3, pp. 373–387, 2022.
  82. Q. Song, Z. Yan, and R. Tan, “Deepmtd: Moving target defense for deep visual sensing against adversarial examples,” ACM Transactions on Sensor Networks (TOSN), vol. 18, no. 1, pp. 1–32, 2021.
  83. S. Yoon, J.-H. Cho, D. S. Kim, T. J. Moore, F. Free-Nelson, and H. Lim, “Desolater: Deep reinforcement learning-based resource allocation and moving target defense deployment framework,” IEEE Access, vol. 9, pp. 70 700–70 714, 2021.
  84. S. Kim, S. Yoon, J.-H. Cho, D. S. Kim, T. J. Moore, F. Free-Nelson, and H. Lim, “Divergence: deep reinforcement learning-based adaptive traffic inspection and moving target defense countermeasure framework,” IEEE Transactions on Network and Service Management, vol. 19, no. 4, pp. 4834–4846, 2022.
  85. S. Sengupta, T. Chakraborti, and S. Kambhampati, “Mtdeep: boosting the security of deep neural nets against adversarial attacks with moving target defense,” in Decision and Game Theory for Security: 10th International Conference, GameSec 2019, Stockholm, Sweden, October 30–November 1, 2019, Proceedings 10.   Springer, 2019, pp. 479–491.
  86. W. Xu, I. M. Jaimoukha, and F. Teng, “Robust moving target defence against false data injection attacks in power grids,” IEEE Transactions on Information Forensics and Security, vol. 18, pp. 29–40, 2022.
  87. M. A. Rahman, E. Al-Shaer, and R. B. Bobba, “Moving target defense for hardening the security of the power system state estimation,” in Proceedings of the First ACM Workshop on Moving Target Defense, 2014, pp. 59–68.
  88. O. Gomis-Bellmunt, J. Sau-Bassols, E. Prieto-Araujo, and M. Cheah-Mane, “Flexible converters for meshed hvdc grids: From flexible ac transmission systems (facts) to flexible dc grids,” IEEE Transactions on Power Delivery, vol. 35, no. 1, pp. 2–15, 2019.
  89. K. Rogers and T. J. Overbye, “Some applications of distributed flexible ac transmission system (d-facts) devices in power systems,” in 2008 40th North American Power Symposium.   IEEE, 2008, pp. 1–8.
  90. J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural networks, vol. 61, pp. 85–117, 2015.
  91. A. Mahi-Al-rashid, F. Hossain, A. Anwar, and S. Azam, “False data injection attack detection in smart grid using energy consumption forecasting,” Energies, vol. 15, no. 13, p. 4877, 2022.
  92. K. Sun, W. Qiu, W. Yao, S. You, H. Yin, and Y. Liu, “Frequency injection based hvdc attack-defense control via squeeze-excitation double cnn,” IEEE Transactions on Power Systems, vol. 36, no. 6, pp. 5305–5316, 2021.
  93. G. B. Gaggero, R. Caviglia, A. Armellin, M. Rossi, P. Girdinio, and M. Marchese, “Detecting cyberattacks on electrical storage systems through neural network based anomaly detection algorithm,” Sensors, vol. 22, no. 10, p. 3933, 2022.
  94. S.-Q. Liu, X. Lan, and P. C. Yuen, “Learning temporal similarity of remote photoplethysmography for fast 3d mask face presentation attack detection,” IEEE Transactions on Information Forensics and Security, vol. 17, pp. 3195–3210, 2022.
  95. M. Rani et al., “A review of intrusion detection system in cloud computing,” in Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India, 2019.
  96. K. Ariyapala, G. Do Hoang, N. A. Huynh, K. N. Wee, and M. Conti, “A host and network based intrusion detection for android smartphones,” in 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA).   IEEE, 2016, pp. 849–854.
  97. K. Gai, M. Qiu, Z. Ming, H. Zhao, and L. Qiu, “Spoofing-jamming attack strategy using optimal power distributions in wireless smart grid networks,” IEEE Transactions on Smart Grid, vol. 8, no. 5, pp. 2431–2439, 2017.
  98. Z. El Mrabet, N. Kaabouch, H. El Ghazi, and H. El Ghazi, “Cyber-security in smart grid: Survey and challenges,” Computers & Electrical Engineering, vol. 67, pp. 469–482, 2018.
  99. A. Mpitziopoulos, D. Gavalas, C. Konstantopoulos, and G. Pantziou, “A survey on jamming attacks and countermeasures in wsns,” IEEE communications surveys & tutorials, vol. 11, no. 4, pp. 42–56, 2009.
  100. Y. Meidan, M. Bohadana, Y. Mathov, Y. Mirsky, A. Shabtai, D. Breitenbacher, and Y. Elovici, “N-baiot—network-based detection of iot botnet attacks using deep autoencoders,” IEEE Pervasive Computing, vol. 17, no. 3, pp. 12–22, 2018.
  101. R. Huang, Y. Li, and X. Wang, “Attention-aware deep reinforcement learning for detecting false data injection attacks in smart grids,” International Journal of Electrical Power & Energy Systems, vol. 147, p. 108815, 2023.
  102. P. Risbud, N. Gatsis, and A. Taha, “Vulnerability analysis of smart grids to gps spoofing,” IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3535–3548, 2018.
  103. S. Wang, S. Zhu, and Y. Zhang, “Blockchain-based mutual authentication security protocol for distributed rfid systems,” in 2018 IEEE Symposium on Computers and Communications (ISCC).   IEEE, 2018, pp. 00 074–00 077.
  104. M. M. Najafabadi, T. M. Khoshgoftaar, C. Kemp, N. Seliya, and R. Zuech, “Machine learning for detecting brute force attacks at the network level,” in 2014 IEEE International Conference on Bioinformatics and Bioengineering.   IEEE, 2014, pp. 379–385.
  105. F. Salahdine and N. Kaabouch, “Social engineering attacks: A survey,” Future internet, vol. 11, no. 4, p. 89, 2019.
  106. Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,” IEEE transactions on neural networks, vol. 5, no. 2, pp. 157–166, 1994.
  107. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
  108. K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder–decoder for statistical machine translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).   Association for Computational Linguistics, 2014, p. 1724.
  109. Y. LeCun, K. Kavukcuoglu, and C. Farabet, “Convolutional networks and applications in vision,” in Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010, pp. 253–256.
  110. V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al., “Human-level control through deep reinforcement learning,” nature, vol. 518, no. 7540, pp. 529–533, 2015.
  111. S. Gupta, G. Singal, and D. Garg, “Deep reinforcement learning techniques in diversified domains: a survey,” Archives of Computational Methods in Engineering, vol. 28, no. 7, pp. 4715–4754, 2021.
  112. S. Levine, A. Kumar, G. Tucker, and J. Fu, “Offline reinforcement learning: Tutorial, review, and perspectives on open problems,” arXiv preprint arXiv:2005.01643, 2020.
  113. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
  114. T. Shao, Y. Guo, H. Chen, and Z. Hao, “Transformer-based neural network for answer selection in question answering,” IEEE Access, vol. 7, pp. 26 146–26 156, 2019.
  115. F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE transactions on neural networks, vol. 20, no. 1, pp. 61–80, 2008.
  116. S. Ali and Y. Li, “Learning multilevel auto-encoders for ddos attack detection in smart grid network,” IEEE Access, vol. 7, pp. 108 647–108 659, 2019.
  117. G. Andresini, A. Appice, N. Di Mauro, C. Loglisci, and D. Malerba, “Multi-channel deep feature learning for intrusion detection,” IEEE Access, vol. 8, pp. 53 346–53 359, 2020.
  118. H. Kye, M. Kim, and M. Kwon, “Hierarchical detection of network anomalies: A self-supervised learning approach,” IEEE Signal Processing Letters, vol. 29, pp. 1908–1912, 2022.
  119. Y. Song, S. Hyun, and Y.-G. Cheong, “Analysis of autoencoders for network intrusion detection,” Sensors, vol. 21, no. 13, p. 4294, 2021.
  120. D. An, Q. Yang, W. Liu, and Y. Zhang, “Defending against data integrity attacks in smart grid: A deep reinforcement learning-based approach,” IEEE Access, vol. 7, pp. 110 835–110 845, 2019.
  121. D. An, F. Zhang, Q. Yang, and C. Zhang, “Data integrity attack in dynamic state estimation of smart grid: Attack model and countermeasures,” IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 1631–1644, 2022.
  122. A. Selim, J. Zhao, F. Ding, F. Miao, and S.-Y. Park, “Deep reinforcement learning for distribution system cyber attack defense with ders,” in 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).   IEEE, 2023, pp. 1–5.
  123. Y. Li and J. Wu, “Low latency cyberattack detection in smart grids with deep reinforcement learning,” International Journal of Electrical Power & Energy Systems, vol. 142, p. 108265, 2022.
  124. A. J. Abianeh, Y. Wan, F. Ferdowsi, N. Mijatovic, and T. Dragičević, “Vulnerability identification and remediation of fdi attacks in islanded dc microgrids using multiagent reinforcement learning,” IEEE Transactions on Power Electronics, vol. 37, no. 6, pp. 6359–6370, 2021.
  125. H. Zhang, D. Yue, C. Dou, and G. P. Hancke, “Resilient optimal defensive strategy of micro-grids system via distributed deep reinforcement learning approach against fdi attack,” IEEE Transactions on Neural Networks and Learning Systems, 2022.
  126. Y. Wang, Z. Zhang, J. Ma, and Q. Jin, “Kfrnn: An effective false data injection attack detection in smart grid based on kalman filter and recurrent neural network,” IEEE Internet of Things Journal, vol. 9, no. 9, pp. 6893–6904, 2021.
  127. E. Naderi and A. Asrari, “Experimental validation of a remedial action via hardware-in-the-loop system against cyberattacks targeting a lab-scale pv/wind microgrid,” IEEE Transactions on Smart Grid, 2023.
  128. P. Ganesh, X. Lou, Y. Chen, R. Tan, D. K. Yau, D. Chen, and M. Winslett, “Learning-based simultaneous detection and characterization of time delay attack in cyber-physical systems,” IEEE Transactions on Smart Grid, vol. 12, no. 4, pp. 3581–3593, 2021.
  129. Q. He, P. Shah, and X. Zhao, “Resilient operation of dc microgrid against fdi attack: A gru based framework,” International Journal of Electrical Power & Energy Systems, vol. 145, p. 108586, 2023.
  130. P. Kumar, R. Kumar, A. Aljuhani, D. Javeed, A. Jolfaei, and A. N. Islam, “Digital twin-driven sdn for smart grid: A deep learning integrated blockchain for cybersecurity,” Solar Energy, vol. 263, p. 111921, 2023.
  131. M. E. Eddin, A. Albaseer, M. Abdallah, S. Bayhan, M. K. Qaraqe, S. Al-Kuwari, and H. Abu-Rub, “Fine-tuned rnn-based detector for electricity theft attacks in smart grid generation domain,” IEEE Open Journal of the Industrial Electronics Society, vol. 3, pp. 733–750, 2022.
  132. M. Ezeddin, A. Albaseer, M. Abdallah, S. Bayhan, M. Qaraqe, and S. Al-Kuwari, “Efficient deep learning based detector for electricity theft generation system attacks in smart grid,” in 2022 3rd International Conference on Smart Grid and Renewable Energy (SGRE).   IEEE, 2022, pp. 1–6.
  133. D. Yao, M. Wen, X. Liang, Z. Fu, K. Zhang, and B. Yang, “Energy theft detection with energy privacy preservation in the smart grid,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 7659–7669, 2019.
  134. E. U. Haq, C. Pei, R. Zhang, H. Jianjun, and F. Ahmad, “Electricity-theft detection for smart grid security using smart meter data: A deep-cnn based approach,” Energy Reports, vol. 9, pp. 634–643, 2023.
  135. K.-D. Lu, L. Zhou, and Z.-G. Wu, “Representation-learning-based cnn for intelligent attack localization and recovery of cyber-physical power systems,” IEEE Transactions on Neural Networks and Learning Systems, 2023.
  136. L. Wu, H. Shi, S. Fu, Y. Luo, and M. Xu, “p2detect: Electricity theft detection with privacy preservation for both data and model in smart grid,” IEEE Transactions on Smart Grid, vol. 14, no. 3, pp. 2301–2312, 2022.
  137. S. H. Haghshenas, M. A. Hasnat, and M. Naeini, “A temporal graph neural network for cyber attack detection and localization in smart grids,” in 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).   IEEE, 2023, pp. 1–5.
  138. O. Boyaci, A. Umunnakwe, A. Sahu, M. R. Narimani, M. Ismail, K. R. Davis, and E. Serpedin, “Graph neural networks based detection of stealth false data injection attacks in smart grids,” IEEE Systems Journal, vol. 16, no. 2, pp. 2946–2957, 2021.
  139. J. Ruan, G. Fan, Y. Zhu, G. Liang, J. Zhao, F. Wen, and Z. Y. Dong, “Super-resolution perception assisted spatiotemporal graph deep learning against false data injection attacks in smart grid,” IEEE Transactions on Smart Grid, 2023.
  140. A. Takiddin, R. Atat, M. Ismail, O. Boyaci, K. R. Davis, and E. Serpedin, “Generalized graph neural network-based detection of false data injection attacks in smart grids,” IEEE Transactions on Emerging Topics in Computational Intelligence, 2023.
  141. O. Boyaci, M. R. Narimani, K. Davis, and E. Serpedin, “Cyberattack detection in large-scale smart grids using chebyshev graph convolutional networks,” in 2022 9th International Conference on Electrical and Electronics Engineering (ICEEE).   IEEE, 2022, pp. 217–221.
  142. J. Mao, M. Zhang, and Q. Xu, “Cnn and lstm based data-driven cyberattack detection for grid-connected pv inverter,” in 2022 IEEE 17th International Conference on Control & Automation (ICCA).   IEEE, 2022, pp. 704–709.
  143. R. U. Madhure, R. Raman, and S. K. Singh, “Cnn-lstm based electricity theft detector in advanced metering infrastructure,” in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT).   IEEE, 2020, pp. 1–6.
  144. H.-X. Gao, S. Kuenzel, and X.-Y. Zhang, “A hybrid convlstm-based anomaly detection approach for combating energy theft,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–10, 2022.
  145. X. Xia, J. Lin, Q. Jia, X. Wang, C. Ma, J. Cui, and W. Liang, “Etd-convlstm: A deep learning approach for electricity theft detection in smart grids,” IEEE Transactions on Information Forensics and Security, 2023.
  146. K. Yu, L. Tan, S. Mumtaz, S. Al-Rubaye, A. Al-Dulaimi, A. K. Bashir, and F. A. Khan, “Securing critical infrastructures: deep-learning-based threat detection in iiot,” IEEE Communications Magazine, vol. 59, no. 10, pp. 76–82, 2021.
  147. Z. Chen, D. Chen, X. Zhang, Z. Yuan, and X. Cheng, “Learning graph structures with transformer for multivariate time-series anomaly detection in iot,” IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9179–9189, 2021.
  148. H. Wang and W. Li, “Ddostc: A transformer-based network attack detection hybrid mechanism in sdn,” Sensors, vol. 21, no. 15, p. 5047, 2021.
  149. Y. Li, X. Wei, Y. Li, Z. Dong, and M. Shahidehpour, “Detection of false data injection attacks in smart grid: A secure federated deep learning approach,” IEEE Transactions on Smart Grid, vol. 13, no. 6, pp. 4862–4872, 2022.
  150. Z. Wu, H. Zhang, P. Wang, and Z. Sun, “Rtids: A robust transformer-based approach for intrusion detection system,” IEEE Access, vol. 10, pp. 64 375–64 387, 2022.
  151. L. Xu, K. Xu, Y. Qin, Y. Li, X. Huang, Z. Lin, N. Ye, and X. Ji, “Tgan-ad: Transformer-based gan for anomaly detection of time series data,” Applied Sciences, vol. 12, no. 16, p. 8085, 2022.
  152. Y. Li, X. Peng, J. Zhang, Z. Li, and M. Wen, “Dct-gan: Dilated convolutional transformer-based gan for time series anomaly detection,” IEEE Transactions on Knowledge and Data Engineering, 2021.
  153. W. Wang, S. Jian, Y. Tan, Q. Wu, and C. Huang, “Robust unsupervised network intrusion detection with self-supervised masked context reconstruction,” Computers & Security, vol. 128, p. 103131, 2023.
  154. M. Ismail, M. F. Shaaban, M. Naidu, and E. Serpedin, “Deep learning detection of electricity theft cyber-attacks in renewable distributed generation,” IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 3428–3437, 2020.
  155. Y. Li, Q. Zhou, S. Li, and B. Li, “wadvmtd: A mitigation to white-box adversarial examples using heterogeneous models and moving target defense,” in 2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS).   IEEE, 2023, pp. 592–597.
  156. W. Xu, I. M. Jaimoukha, and F. Teng, “Physical verification of data-driven cyberattack detector in power system: An mtd approach,” in 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).   IEEE, 2022, pp. 1–5.
  157. W. Xu, M. Higgins, J. Wang, I. M. Jaimoukha, and F. Teng, “Blending data and physics against false data injection attack: An event-triggered moving target defence approach,” IEEE Transactions on Smart Grid, 2022.
  158. A. Divekar, M. Parekh, V. Savla, R. Mishra, and M. Shirole, “Benchmarking datasets for anomaly-based network intrusion detection: Kdd cup 99 alternatives,” in 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS).   IEEE, 2018, pp. 1–8.
  159. N. Moustafa and J. Slay, “Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set),” in 2015 military communications and information systems conference (MilCIS).   IEEE, 2015, pp. 1–6.
  160. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the kdd cup 99 data set,” in 2009 IEEE symposium on computational intelligence for security and defense applications.   Ieee, 2009, pp. 1–6.
  161. B. Ingre and A. Yadav, “Performance analysis of nsl-kdd dataset using ann,” in 2015 international conference on signal processing and communication engineering systems.   IEEE, 2015, pp. 92–96.
  162. L. Dhanabal and S. Shantharajah, “A study on nsl-kdd dataset for intrusion detection system based on classification algorithms,” International journal of advanced research in computer and communication engineering, vol. 4, no. 6, pp. 446–452, 2015.
  163. G. Meena and R. R. Choudhary, “A review paper on ids classification using kdd 99 and nsl kdd dataset in weka,” in 2017 International Conference on Computer, Communications and Electronics (Comptelix).   IEEE, 2017, pp. 553–558.
  164. T. Janarthanan and S. Zargari, “Feature selection in unsw-nb15 and kddcup’99 datasets,” in 2017 IEEE 26th international symposium on industrial electronics (ISIE).   IEEE, 2017, pp. 1881–1886.
  165. R. Panigrahi and S. Borah, “A detailed analysis of cicids2017 dataset for designing intrusion detection systems,” International Journal of Engineering & Technology, vol. 7, no. 3.24, pp. 479–482, 2018.
  166. I. Sharafaldin, A. H. Lashkari, S. Hakak, and A. A. Ghorbani, “Developing realistic distributed denial of service (ddos) attack dataset and taxonomy,” in 2019 International Carnahan Conference on Security Technology (ICCST).   IEEE, 2019, pp. 1–8.
  167. Z. Zheng, Y. Yang, X. Niu, H.-N. Dai, and Y. Zhou, “Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids,” IEEE Transactions on Industrial Informatics, vol. 14, no. 4, pp. 1606–1615, 2017.
  168. Irish Social Science Data Archive, “Commission for energy regulation (cer) smart metering project,” 2012, [Online]. Available. [Online]. Available: http://www.ucd.ie/issda/data/commissionforenergyregulationcer/
Citations (12)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets