Machine Learning-Based Intrusion Detection and Prevention System for IoT Smart Metering Networks: Challenges and Solutions
Keywords:
Internet of Things (IoT), Smart Metering, Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Machine Learning, Cybersecurity, Anomaly Detection, Edge Computing, Network Security, Smart GridAbstract
The Internet of Things (IoT) has revolutionized industries by enabling automation, real time data exchange, and smart decision making. However, its increased connectivity introduces cybersecurity threats, particularly in smart metering networks, which play a crucial role in monitoring and optimizing energy consumption. This paper explores the challenges associated with securing IoT-based smart metering networks and proposes a Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS) for safeguarding edge devices. The study reviews various intrusion detection approaches, highlighting the strengths and limitations of both signature based and anomaly-based detection techniques. The integration of ML-based IDPS in IoT smart metering networks significantly improves security by enhancing anomaly detection accuracy and reducing false positives. Advanced models like SVM, CNN, and CNN LSTM effectively detect threats such as DoS, spoofing, and abnormal usage patterns across network layers. These systems also support real-time monitoring and adapt to evolving attacks, increasing system resilience. However, most models are tested on outdated datasets and overlook deployment challenges on resource-constrained devices. Therefore, lightweight and scalable solutions are needed to ensure effective, energy-efficient protection in real-world smart metering environments.
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Alaiz-Moreton, H., Aveleira-Mata, J., Ondicol-Garcia, J., Muñoz-Castañeda, A. L., García, I. and Benavides, C. (2019). Multiclass classification procedure for detecting attacks on MQTT-IoT protocol. Complexity, 2019, 1-11.
Alhabib, M. H. and Ali, Q. I. (2023). Internet of autonomous vehicles communication infrastructure: A short review. Diagnostyka, 24.
Ali, Q. I. (2012). Design and implementation of an embedded intrusion detection system for wireless applications. IET Information Security, 6(3), 171-182.
Ali, Q. I. (2018). GVANET project: An efficient deployment of a self-powered, reliable and secured VANET infrastructure. IET Wireless Sensor Systems, 8(6), 313322. https://doi.org/10.1049/iet-wss.2018.5112
Ali, Q. I. (2022). Realization of a robust fog-based green VANET infrastructure. IEEE Systems journal, 17(2), 2465-2476.
Ali, Q. I. and Jalal, J. K. (2014). Practical design of solar-powered IEEE 802.11 backhaul wireless repeater. Paper presented at the 2014 6th International Conference on Multimedia, Computer Graphics and Broadcasting.
Andrysiak, T., Saganowski, Ł., and Kiedrowski, P. (2017). Anomaly detection in smart metering infrastructure with the use of time series analysis. Journal of Sensors, 2017, 1-15.
Awotunde, J. B., Chakraborty, C. and Adeniyi, A. E. (2021). Intrusion detection in industrial internet of things network-based on deep learning model with rule based feature selection. Wireless communications and mobile computing, 2021, 1-17. International STEM Journal, Volume 6 No.1, June 2025, 40-57
Aydogan, E., Yilmaz, S., Sen, S., Butun, I., Forsström, S. and Gidlund, M. (2019). A central intrusion detection system for rpl-based industrial internet of things. Paper presented at the 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS).
Beckel, C., Sadamori, L., Staake, T. and Santini, S. (2014). Revealing household characteristics from smart meter data. Energy, 78, 397-410.
Bergmann, N. W. and Robinson, P. J. (2012). Server-based internet of things architecture. Paper presented at the 2012 IEEE Consumer Communications and Networking Conference (CCNC).
Bhattacharjee, S., Thakur, A., Silvestri, S. and Das, S. K. (2017). Statistical security incident forensics against data falsification in smart grid advanced metering infrastructure. Paper presented at the Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy.
Chahla, C., Snoussi, H., Merghem, L. and Esseghir, M. (2019). A Novel Approach for Anomaly Detection in Power Consumption Data. Paper presented at the Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2019).
Čolaković, A. and Hadžialić, M. (2018). Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues. Computer Networks, 144, 17-39.
Derhab, A., Guerroumi, M., Gumaei, A., Maglaras, L., Ferrag, M. A., Mukherjee, M. and Khan, F. A. (2019). Blockchain and random subspace learning-based IDS for SDN-enabled industrial IoT security. Sensors, 19(14), 3119-3142.
Eskandari, M., Janjua, Z. H., Vecchio, M. and Antonelli, F. (2020). Passban IDS: An intelligent anomaly-based intrusion detection system for IoT edge devices. IEEE Internet of Things Journal, 7(8), 6882-6897.
Fu, Y., Yan, Z., Cao, J., Koné, O. and Cao, X. (2017). An automata based intrusion detection method for internet of things. Mobile Information Systems, 2017, 113.
García, J., Zamora, E. and Sossa, H. (2018). Supervised and unsupervised neural networks: Experimental study for anomaly detection in electrical consumption. Paper presented at the Mexican International Conference on Artificial
Intelligence.
Gubbi, J., Buyya, R., Marusic, S. and Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660.
Himeur, Y., Alsalemi, A., Bensaali, F. and Amira, A. (2020). A novel approach for detecting anomalous energy consumption based on micro-moments and deep neural networks. Cognitive Computation, 12(6), 1381-1401. International STEM Journal, Volume 6 No.1, June 2025, 40-57
Huma, Z. E., Latif, S., Ahmad, J., Idrees, Z., Ibrar, A., Zou, Z. and Baothman, F. (2021). A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things. IEEE Access, 9, 55595-55605.
Ibrahim, Q. (2011). Design & Implementation of High Speed Network Devices Using SRL16 Reconfigurable Content Addressable Memory (RCAM). Int. Arab. J. e Technol., 2(2), 72-81.
Ibrahim, Q. and Lazim, S. (2011). Applying an Efficient Searching Algorithm for Intrusion Detection on Ubicom Network Processor. Int. Arab. J. e Technol., 2(2), 82-90.
Ibrahim, Q. and Lazim, S. (2021). An insight review of internet of Things (IoT) protocols, standards, platforms, applications and security issues. International Journal of Sensors Wireless Communications and Control, 11(6), 627-648.
Jindal, A., Dua, A., Kaur, K., Singh, M., Kumar, N. and Mishra, S. (2016). Decision tree and SVM-based data analytics for theft detection in smart grid. IEEE Transactions on Industrial Informatics, 12(3), 1005-1016.
Jokar, P., Arianpoo, N. and Leung, V. C. (2016). Electricity Theft Detection in AMI Using Customers’ Consumption Patterns. IEEE Transactions on Smart Grid, 1(7), 216-226.
Jokar, P. and Leung, V. C. (2016). Intrusion detection and prevention for ZigBee- based home area networks in smart grids. IEEE Transactions on Smart Grid, 9(3), 1800-1811.
Khan, M. A., Khan, M. A., Jan, S. U., Ahmad, J., Jamal, S. S., Shah, A. A. and Buchanan, W. J. (2021). A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT. Sensors, 21(21), 7016-7040.
Korba, A. A., Tamani, N. and Ghamri-Doudane, Y. (2020). Anomaly-based framework for detecting power overloading cyberattacks in smart grid AMI. Computers & Security, 96, 101896-101935.
Latif, S., Zou, Z., Idrees, Z. and Ahmad, J. (2020). A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network. IEEE Access, 8, 89337-89350.
Lazim Qaddoori, S. and Ali, Q. I. (2023). An embedded and intelligent anomaly power consumption detection system based on smart metering. IET Wireless Sensor Systems, 13(2), 75-90.
Li, Y., Qiu, R. and Jing, S. (2018). Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid. PloS one, 13(2), e0192216-e0192231. International STEM Journal, Volume 6 No.1, June 2025, 40-57
Liu, X. and Nielsen, P. S. (2018). Scalable prediction-based online anomaly detection for smart meter data. Information Systems, 77, 34-47.
Madhawa, S., Balakrishnan, P. and Arumugam, U. (2019). Roll forward validation based decision tree classification for detecting data integrity attacks in industrial internet of things. Journal of Intelligent & Fuzzy Systems, 36(3), 2355-2366.
Maharani, M. P., Daely, P. T., Lee, J. M. and Kim, D.-S. (2020). Attack detection in fog layer for iiot based on machine learning approach. Paper presented at the 2020 International Conference on Information and Communication Technology
Convergence (ICTC).
Matharu, G. S., Upadhyay, P. and Chaudhary, L. (2014). The internet of things: Challenges & security issues. Paper presented at the 2014 International Conference on Emerging Technologies (ICET).
Nguyen, X.-H., Nguyen, X.-D., Huynh, H.-H. and Le, K.-H. (2022). Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways. Sensors, 22(2), 432-449.
Oprea, S.-V., Bâra, A., Puican, F. C. and Radu, I. C. (2021). Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption. Sustainability, 13(19), 10963-10982.
Putchala, M. K. (2017). Deep learning approach for intrusion detection system (ids) in the internet of things (iot) network using gated recurrent neural networks (gru).
Qaddoori, S. L. and Ali, Q. I. (2021). An in-depth characterization of intrusion detection systems (IDS). Journal of Modern Technology & Engineering, 6(2).
Qaddoori, S. L. and Ali, Q. I. (2023). An efficient security model for industrial internet of things (IIoT) system based on machine learning principles. Al-Rafidain Engineering Journal (AREJ), 28(1), 329-340.
Qaddoori, S. L. and Ali, Q. I. (2023). An embedded intrusion detection and prevention system for home area networks in advanced metering infrastructure. IET Information Security, 17(3), 315-334.
Qaddoori, S. L., Fathi, I. a., Hammoudy, M. A. and Ali, Q. I. (2023). Advancing Public Health Monitoring through Secure and Efficient Wearable Technology. International Journal of Safety & Security Engineering, 13(6).
Qiao, H., Blech, J. O. and Chen, H. (2020). A Machine learning based intrusion detection approach for industrial networks. Paper presented at the 2020 IEEE International Conference on Industrial Technology (ICIT).
Raja, K., Karthikeyan, K., Abilash, B., Dev, K. and Raja, G. (2021). Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System. Soft computing, Springer, Research Square, 2021, 1-32. doi:10.21203/rs.3.rs997888/v1
Rehman, H. U., Asif, M. and Ahmad, M. (2017). Future applications and research challenges of IOT. Paper presented at the 2017 International conference on information and communication technologies (ICICT).
Sahu, A., Tippanaboyana, H. K., Hefton, L. and Goulart, A. (2017). Detection of rogue nodes in AMI networks. Paper presented at the 2017 19th International Conference on Intelligent System Application to Power Systems (ISAP).
Sedjelmaci, H. and Senouci, S. M. (2016). Smart grid security: A new approach to detect intruders in a smart grid neighborhood area network. Paper presented at the 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM).
Selim, G. E. I., Hemdan, E., Shehata, A. M. and El-Fishawy, N. A. (2021). Anomaly events classification and detection system in critical industrial internet of things infrastructure using machine learning algorithms. Multimedia Tools and
Applications, 80(8), 12619-12640.
Shalaginov, A., Semeniuta, O. and Alazab, M. (2019). MEML: Resource-aware MQTT- based machine learning for network attacks detection on IoT edge devices. Paper presented at the Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion.
Song, Z., Lazarescu, M. T., Tomasi, R., Lavagno, L. and Spirito, M. A. (2014). High- level internet of things applications development using wireless sensor networks Internet of Things (pp. 75-109): Springer.
Sun, C.-C., Cardenas, D. J. S., Hahn, A. and Liu, C.-C. (2020). Intrusion detection for cybersecurity of smart meters. IEEE Transactions on Smart Grid, 12(1), 612-622.
Ullah, A., Javaid, N., Samuel, O., Imran, M. and Shoaib, M. (2020). CNN and GRU based deep neural network for electricity theft detection to secure smart grid. Paper presented at the 2020 International Wireless Communications and Mobile Computing (IWCMC).
Vaiyapuri, T., Sbai, Z., Alaskar, H. and Alaseem, N. A. (2021). Deep Learning Approaches for Intrusion Detection in IIoT Networks–Opportunities and Future Directions. International Journal of Advanced Computer Science and
Applications (IJACSA), 12(4), 86-92.
Vijayanand, R., Devaraj, D. and Kannapiran, B. (2017). Support vector machine based intrusion detection system with reduced input features for advanced metering infrastructure of smart grid. Paper presented at the 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS).
Vijayanand, R., Devaraj, D. and Kannapiran, B. (2019). A novel deep learning based intrusion detection system for smart meter communication network. Paper presented at the 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS).
Xiang, M., Rao, H., Tan, T., Wang, Z. and Ma, Y. (2019). Abnormal behaviour analysis algorithm for electricity consumption based on density clustering. The Journal of Engineering, 2019(10), 7250-7255.
Yao, H., Gao, P., Zhang, P., Wang, J., Jiang, C. and Lu, L. (2019). Hybrid intrusion detection system for edge-based IIoT relying on machine-learning-aided detection. IEEE Network, 33(5), 75-81.
Yao, R., Wang, N., Liu, Z., Chen, P. and Sheng, X. (2021). Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN- LSTM-Based Approach. Sensors, 21(2), 626-642.
Zhang, J., Zhang, H., Ding, S. and Zhang, X. (2021). Power Consumption Predicting and Anomaly Detection Based on Transformer and K-Means. Frontiers in Energy Research, 9, 681-688.
