Research Article
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Year 2025, Volume: 8 Issue: 3, 518 - 535, 30.09.2025

Hilal Yıldız , Musa Balta

Abstract

Project Number

2022-6-23-68

References

  • Özçelik, İbrahim, et al. Center energy: A secure testbed infrastructure proposal for electricity power grid. In: 2021 International Conference on Information Security and Cryptology (ISCTURKEY). IEEE, 2021. p. 149-154.
  • Rehmani, Mubashir Husain, et al. Software defined networks-based smart grid communication: A comprehensive survey. IEEE Communications Surveys\& Tutorials, 2019, 21.3: 2637-2670.
  • Demirci, Sedef; SAGIROGLU, Seref. Software-defined networking for improving security in smart grid systems. In: 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, 2018. p. 1021-1026.
  • Soares, Arthur AZ, et al. 3AS: Authentication, authorization, and accountability for sdn-based smart grids. IEEE Access, 2021, 9: 88621-88640.
  • Jung, Oliver, et al. Anomaly Detection in Smart Grids based on Software Defined Networks. In: SMARTGREENS. 2019. p. 157-164.
  • Dileep, G. J. R. E. A survey on smart grid technologies and applications. Renewable energy, 2020, 146: 2589-2625.
  • Al-Fuqaha, Ala, et al. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE communications surveys \& tutorials, 2015, 17.4: 2347-2376.
  • Roman, Rodrigo; NAJERA, Pablo; LOPEZ, Javier. Securing the internet of things. Computer, 2011, 44.9: 51-58.
  • Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer networks, 54(15), 2787-2805.
  • Wang, Minxiao; YANG, Ning; WENG, Ning. Securing a Smart Home with a Transformer-Based IoT Intrusion Detection System. Electronics, 2023, 12.9: 2100.
  • Alonazi, Wesam Abdulrhman, et al. SDN Architecture for Smart Homes Security with Machine Learning and Deep Learning. International Journal of Advanced Computer Science and Applications, 2022, 13.10.
  • Chen, Jian, et al. A multi-layer security scheme for mitigating smart grid vulnerability against faults and cyber-attacks. Applied Sciences, 2021, 11.21: 9972.
  • NIST (2018, 8 November). Update of the NIST Smart Grid Conceptual Model.
  • Marikyan, Davit; PAPAGIANNIDIS, Savvas; ALAMANOS, Eleftherios. A systematic review of the smart home literature: A user perspective. Technological Forecasting and Social Change, 2019, 138: 139-154.
  • Zaidan, A. A.; ZAIDAN, B. B. A review on intelligent process for smart home applications based on IoT: coherent taxonomy, motivation, open challenges, and recommendations. Artificial Intelligence Review, 2020, 53.1: 141-165.
  • Rondon, Luis Puche, et al. Survey on enterprise Internet-of-Things systems (E-IoT): A security perspective. Ad Hoc Networks, 2022, 125: 102728.
  • Ravinder, M.; KULKARNI, Vikram. Intrusion detection in smart meters data using machine learning algorithms: A research report. Frontiers in Energy Research, 2023, 11: 1147431.
  • Cao, Keyan, et al. An overview on edge computing research. IEEE access, 2020, 8: 85714-85728.
  • Danbatta, Salim Jibrin; VAROL, Asaf. Comparison of Zigbee, Z-Wave, Wi-Fi, and bluetooth wireless technologies used in home automation. In: 2019 7th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2019. p. 1-5.
  • Moustafa, Nour; SLAY, Jill. 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. p. 1-6.
  • Ring, Markus, et al. Flow-based benchmark data sets for intrusion detection. In: Proceedings of the 16th European conference on cyber warfare and security. ACPI. 2017. p. 361-369.
  • Sharafaldin, Iman; LASHKARI, Arash Habibi; GHORBANI, Ali A. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 2018, 1: 108-116.
  • MOUSTAFA, Nour. New generations of internet of things datasets for cybersecurity applications based machine learning: TON\_IoT datasets. In: Proceedings of the eResearch Australasia Conference, Brisbane, Australia. 2019. p. 21-25.
  • NSL-KDD dataset. https://www.unb.ca/cic/datasets/nsl.html
  • FAN, Cheng, et al. A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Frontiers in Energy Research, 2021, 9: 652801.
  • YU, Xinran; ERGAN, Semiha; DEDEMEN, Gokmen. A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption. Applied Energy, 2019, 253: 113497.
  • FAN, Cheng; XIAO, Fu; YAN, Chengchu. A framework for knowledge discovery in massive building automation data and its application in building diagnostics. Automation in Construction, 2015, 50: 81-90.
  • FAN, Cheng, et al. Temporal knowledge discovery in big BAS data for building energy management. Energy and Buildings, 2015, 109: 75-89.
  • HASAN, Mahmudul, et al. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things, 2019, 7: 100059.
  • GANGWAR, Amit Kumar; SHAIK, Abdul Gafoor. k-Nearest neighbour based approach for the protection of distribution network with renewable energy integration. Electric Power Systems Research, 2023, 220: 109301.
  • ROSE, Thomas, et al. A hybrid anomaly-based intrusion detection system to improve time complexity in the Internet of Energy environment. Journal of Parallel and Distributed Computing, 2020, 145: 124-139.
  • SHABAD, Prem Kumar Reddy; ALRASHIDE, Abdulmueen; MOHAMMED, Osama. Anomaly detection in smart grids using machine learning. In: IECON 2021–47th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2021. p. 1-8.
  • LI, Qiang, et al. Simultaneous detection for multiple anomaly data in internet of energy based on random forest. Applied Soft Computing, 2023, 134: 109993.
  • VIGOYA, Laura, et al. IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection. Electronics, 2021, 10.22: 2857.
  • ARIBISALA, Adedayo; KHAN, Mohammad S.; HUSARI, Ghaith. Feed-Forward Intrusion Detection and Classification on a Smart Grid Network. In: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2022. p. 0099-0105.
  • CHUANG, Po-Jen; LI, Si-Han. Network intrusion detection using hybrid machine learning. In: 2019 International Conference on Fuzzy Theory and Its Applications (iFUZZY). IEEE, 2019. p. 1-5.
  • SHI, Jibo, et al. A hybrid intrusion detection system based on machine learning under differential privacy protection. In: 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). IEEE, 2021. p. 1-6.
  • Balta, D. D., Kaç, S. B., Balta, M., Oğur, N. B., & Eken, S. (2025). Cybersecurity-aware log management system for critical water infrastructures. Applied Soft Computing, 169, 112613.
  • Breviglieri, P., Erdem, T., & Eken, S. (2021). Predicting smart grid stability with optimized deep models. SN Computer Science, 2, 1-12.
  • Singh, C., & Jain, A. K. (2024). A comprehensive survey on DDoS attacks detection & mitigation in SDN-IoT network. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 100543.
  • Chaganti, R., Suliman, W., Ravi, V., & Dua, A. (2023). Deep learning approach for SDN-enabled intrusion detection system in IoT networks. Information, 14(1), 41.

Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks

Year 2025, Volume: 8 Issue: 3, 518 - 535, 30.09.2025

Hilal Yıldız , Musa Balta

Abstract

The problems of traditional electricity grids have led to the emergence of smart grids. Unlike traditional energy systems, smart grids play an important role in the energy sector with their flexibility, programmability and reliability. However, the heterogeneous structure of smart grids consisting of different devices and protocols poses some problems in terms of complexity, service quality and security. In the literature, SDN (Software Defined Networks) paradigm is proposed as a solution to these problems. SDN and smart grid integration makes the energy sector more efficient, reliable and sustainable. On the other hand, smart meters used in the consumption area of smart grids provide instantaneous transmission of energy production and consumption data in homes to the center. With the support of IoT (Internet of Things) of these meters and components in the home area network (oven, IP camera, TV, etc.), the energy supply and demand balance can be managed more smoothly.
In this study, a software-defined and IoT-based smart home architecture is proposed to obtain real energy consumption data. The proposed architecture is developed and implemented on the Mininet simulator with python code. As a result of simulations run under different process and attack scenarios, energy consumption data sets were created. A comparison of the anomaly detection performances of machine learning algorithms on the data sets that are considered to contribute to the literature has been made. As a result of this comparison, it was observed that the success rate of the random forest algorithm was higher than the other algorithms with 90-95 percent.

Keywords

SDN Smart grid IoT network Smart home Energy consumption

Supporting Institution

Sakarya University, Scientific Research Projects Unit

Project Number

2022-6-23-68

References

  • Özçelik, İbrahim, et al. Center energy: A secure testbed infrastructure proposal for electricity power grid. In: 2021 International Conference on Information Security and Cryptology (ISCTURKEY). IEEE, 2021. p. 149-154.
  • Rehmani, Mubashir Husain, et al. Software defined networks-based smart grid communication: A comprehensive survey. IEEE Communications Surveys\& Tutorials, 2019, 21.3: 2637-2670.
  • Demirci, Sedef; SAGIROGLU, Seref. Software-defined networking for improving security in smart grid systems. In: 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, 2018. p. 1021-1026.
  • Soares, Arthur AZ, et al. 3AS: Authentication, authorization, and accountability for sdn-based smart grids. IEEE Access, 2021, 9: 88621-88640.
  • Jung, Oliver, et al. Anomaly Detection in Smart Grids based on Software Defined Networks. In: SMARTGREENS. 2019. p. 157-164.
  • Dileep, G. J. R. E. A survey on smart grid technologies and applications. Renewable energy, 2020, 146: 2589-2625.
  • Al-Fuqaha, Ala, et al. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE communications surveys \& tutorials, 2015, 17.4: 2347-2376.
  • Roman, Rodrigo; NAJERA, Pablo; LOPEZ, Javier. Securing the internet of things. Computer, 2011, 44.9: 51-58.
  • Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer networks, 54(15), 2787-2805.
  • Wang, Minxiao; YANG, Ning; WENG, Ning. Securing a Smart Home with a Transformer-Based IoT Intrusion Detection System. Electronics, 2023, 12.9: 2100.
  • Alonazi, Wesam Abdulrhman, et al. SDN Architecture for Smart Homes Security with Machine Learning and Deep Learning. International Journal of Advanced Computer Science and Applications, 2022, 13.10.
  • Chen, Jian, et al. A multi-layer security scheme for mitigating smart grid vulnerability against faults and cyber-attacks. Applied Sciences, 2021, 11.21: 9972.
  • NIST (2018, 8 November). Update of the NIST Smart Grid Conceptual Model.
  • Marikyan, Davit; PAPAGIANNIDIS, Savvas; ALAMANOS, Eleftherios. A systematic review of the smart home literature: A user perspective. Technological Forecasting and Social Change, 2019, 138: 139-154.
  • Zaidan, A. A.; ZAIDAN, B. B. A review on intelligent process for smart home applications based on IoT: coherent taxonomy, motivation, open challenges, and recommendations. Artificial Intelligence Review, 2020, 53.1: 141-165.
  • Rondon, Luis Puche, et al. Survey on enterprise Internet-of-Things systems (E-IoT): A security perspective. Ad Hoc Networks, 2022, 125: 102728.
  • Ravinder, M.; KULKARNI, Vikram. Intrusion detection in smart meters data using machine learning algorithms: A research report. Frontiers in Energy Research, 2023, 11: 1147431.
  • Cao, Keyan, et al. An overview on edge computing research. IEEE access, 2020, 8: 85714-85728.
  • Danbatta, Salim Jibrin; VAROL, Asaf. Comparison of Zigbee, Z-Wave, Wi-Fi, and bluetooth wireless technologies used in home automation. In: 2019 7th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2019. p. 1-5.
  • Moustafa, Nour; SLAY, Jill. 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. p. 1-6.
  • Ring, Markus, et al. Flow-based benchmark data sets for intrusion detection. In: Proceedings of the 16th European conference on cyber warfare and security. ACPI. 2017. p. 361-369.
  • Sharafaldin, Iman; LASHKARI, Arash Habibi; GHORBANI, Ali A. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 2018, 1: 108-116.
  • MOUSTAFA, Nour. New generations of internet of things datasets for cybersecurity applications based machine learning: TON\_IoT datasets. In: Proceedings of the eResearch Australasia Conference, Brisbane, Australia. 2019. p. 21-25.
  • NSL-KDD dataset. https://www.unb.ca/cic/datasets/nsl.html
  • FAN, Cheng, et al. A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Frontiers in Energy Research, 2021, 9: 652801.
  • YU, Xinran; ERGAN, Semiha; DEDEMEN, Gokmen. A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption. Applied Energy, 2019, 253: 113497.
  • FAN, Cheng; XIAO, Fu; YAN, Chengchu. A framework for knowledge discovery in massive building automation data and its application in building diagnostics. Automation in Construction, 2015, 50: 81-90.
  • FAN, Cheng, et al. Temporal knowledge discovery in big BAS data for building energy management. Energy and Buildings, 2015, 109: 75-89.
  • HASAN, Mahmudul, et al. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things, 2019, 7: 100059.
  • GANGWAR, Amit Kumar; SHAIK, Abdul Gafoor. k-Nearest neighbour based approach for the protection of distribution network with renewable energy integration. Electric Power Systems Research, 2023, 220: 109301.
  • ROSE, Thomas, et al. A hybrid anomaly-based intrusion detection system to improve time complexity in the Internet of Energy environment. Journal of Parallel and Distributed Computing, 2020, 145: 124-139.
  • SHABAD, Prem Kumar Reddy; ALRASHIDE, Abdulmueen; MOHAMMED, Osama. Anomaly detection in smart grids using machine learning. In: IECON 2021–47th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2021. p. 1-8.
  • LI, Qiang, et al. Simultaneous detection for multiple anomaly data in internet of energy based on random forest. Applied Soft Computing, 2023, 134: 109993.
  • VIGOYA, Laura, et al. IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection. Electronics, 2021, 10.22: 2857.
  • ARIBISALA, Adedayo; KHAN, Mohammad S.; HUSARI, Ghaith. Feed-Forward Intrusion Detection and Classification on a Smart Grid Network. In: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2022. p. 0099-0105.
  • CHUANG, Po-Jen; LI, Si-Han. Network intrusion detection using hybrid machine learning. In: 2019 International Conference on Fuzzy Theory and Its Applications (iFUZZY). IEEE, 2019. p. 1-5.
  • SHI, Jibo, et al. A hybrid intrusion detection system based on machine learning under differential privacy protection. In: 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). IEEE, 2021. p. 1-6.
  • Balta, D. D., Kaç, S. B., Balta, M., Oğur, N. B., & Eken, S. (2025). Cybersecurity-aware log management system for critical water infrastructures. Applied Soft Computing, 169, 112613.
  • Breviglieri, P., Erdem, T., & Eken, S. (2021). Predicting smart grid stability with optimized deep models. SN Computer Science, 2, 1-12.
  • Singh, C., & Jain, A. K. (2024). A comprehensive survey on DDoS attacks detection & mitigation in SDN-IoT network. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 100543.
  • Chaganti, R., Suliman, W., Ravi, V., & Dua, A. (2023). Deep learning approach for SDN-enabled intrusion detection system in IoT networks. Information, 14(1), 41.
There are 41 citations in total.

Details

Primary Language English
Subjects Computer Software, Software Engineering (Other)
Journal Section Research Article
Authors

Hilal Yıldız 0000-0001-6840-8545

Musa Balta 0000-0002-8711-6625

Project Number 2022-6-23-68
Early Pub Date September 29, 2025
Publication Date September 30, 2025
Submission Date February 17, 2025
Acceptance Date September 17, 2025
Published in Issue Year 2025 Volume: 8 Issue: 3

Cite

APA Yıldız, H., & Balta, M. (2025). Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks. Sakarya University Journal of Computer and Information Sciences, 8(3), 518-535.
AMA Yıldız H, Balta M. Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks. SAUCIS. September 2025;8(3):518-535.
Chicago Yıldız, Hilal, and Musa Balta. “Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks”. Sakarya University Journal of Computer and Information Sciences 8, no. 3 (September 2025): 518-35.
EndNote Yıldız H, Balta M (September 1, 2025) Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks. Sakarya University Journal of Computer and Information Sciences 8 3 518–535.
IEEE H. Yıldız and M. Balta, “Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks”, SAUCIS, vol. 8, no. 3, pp. 518–535, 2025.
ISNAD Yıldız, Hilal - Balta, Musa. “Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks”. Sakarya University Journal of Computer and Information Sciences 8/3 (September2025), 518-535.
JAMA Yıldız H, Balta M. Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks. SAUCIS. 2025;8:518–535.
MLA Yıldız, Hilal and Musa Balta. “Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 3, 2025, pp. 518-35.
Vancouver Yıldız H, Balta M. Comparison of Machine Learning Based Anomaly Detection for Energy Consumption Values in SDN-IoT Based Home Area Networks. SAUCIS. 2025;8(3):518-35.
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