FedBHAD: Energy-Efficient Federated Learning for Black Hole Attack Detection in RPL-Based Low-Power IoT Networks
Downloads
The internet of things is a network of connected devices that share and send information over the internet, frequently in resource-constrained situations. These are often built using the routing protocol for low-power and lossy networks (RPL), face significant security problems because of their limited computing power, and have energy constraints. The objective of this study is to design an efficient and lightweight mechanism for detecting black hole attacks on RPL-based internet of things networks. The proposed framework presents a distributed collaborative learning framework to reduce the processing load on central nodes while enhancing real-time threat detection. The novelty of the present work lies in integrating distributed learning with feature-based anomaly detection tailored for RPL environments, thereby improving IoT network security while reducing communication and energy overhead. A customized data retrieval algorithm is developed with the Cooja simulator’s configuration and extracts essential network parameters, including rank, expected transmission count, power consumption, forward count, and reception count. The analysis of this dataset allows the detection of black hole attacks. The research analysis indicates that the proposed framework achieves 99.6% detection accuracy, surpassing existing machine learning and deep learning techniques and offering enhanced security, reduced overhead, and lower computational needs.
Downloads
[1] Mathi, S., Akshaya, R., & Sreejith, K. (2022). An Internet of Things-based Efficient Solution for Smart Farming. Procedia Computer Science, 218, 2806–2819. doi:10.1016/j.procs.2023.01.252.
[2] Vidhya, S. S., Mathi, S., Ananthanarayanan, V., & Iyer, G. N. (2024). IP-RPL: An Intelligent Power-aware Routing Protocol for Next Generation Low Power Networks. IEEE Sensors Journal. doi:10.1109/JSEN.2024.3506816.
[3] Anjali, A. N., Sreelakshmi, C. B., & Remya Nair, T. (2023). Blackhole Attack Detection in Wireless Sensor Network Using Backpropagation Algorithm. 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023, 1–6. doi:10.1109/ICCCNT56998.2023.10307586.
[4] Mayzaud, A., Badonnel, R., & Chrisment, I. (2016). A taxonomy of attacks in RPL-based internet of things. International Journal of Network Security, 18(3), 459–473. doi:10.6633/IJNS.201605.18(3).07.
[5] Panda, N., Supriya, M. (2023). Blackhole Attack Prediction in Wireless Sensor Networks Using Support Vector Machine. Advances in Signal Processing, Embedded Systems and IoT, Lecture Notes in Electrical Engineering, Springer, Singapore. doi:10.1007/978-981-19-8865-3_30.
[6] Wakili, A., Bakkali, S., & Alaoui, A. E. H. (2024). Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors. Sensors International, 5, 100289. doi:10.1016/j.sintl.2024.100289.
[7] Krari, A., Hajami, A., Toubi, A., Errakha, K. (2025). A Deep Learning Approach to Strengthening IoT RPL Protocol Security Against Black Hole Attacks Advances in Intelligent Systems and Digital Applications, ISDA 2025, Lecture Notes in Networks and Systems, vol 1485. Springer, Cham, Switzerland. doi:10.1007/978-3-031-95326-2_4.
[8] Sharma, N., & Dhiman, P. (2025). A survey on IoT security: challenges and their solutions using machine learning and blockchain technology. Cluster Computing, 28(5), 313. doi:10.1007/s10586-025-05208-0.
[9] Raghavendra, T., Anand, M., Selvi, M., Thangaramya, K., Santhosh Kumar, S. V. N., & Kannan, A. (2022). An Intelligent RPL attack detection using Machine Learning-Based Intrusion Detection System for Internet of Things. Procedia Computer Science, 215, 61–70. doi:10.1016/j.procs.2022.12.007.
[10] Sharma, D. K., Dhurandher, S. K., Kumaram, S., Datta Gupta, K., & Sharma, P. K. (2022). Mitigation of black hole attacks in 6LoWPAN RPL-based Wireless sensor network for cyber physical systems. Computer Communications, 189, 182–192. doi:10.1016/j.comcom.2022.04.003.
[11] Airehrour, D., Gutierrez, J., & Ray, S. K. (2016). Securing RPL routing protocol from blackhole attacks using a trust-based mechanism. 2016 26th International Telecommunication Networks and Applications Conference (ITNAC), 115–120. doi:10.1109/atnac.2016.7878793.
[12] Karthikeyan, M., & Revathi, S. T. (2024). Approaches to Detecting Threats in Wireless Sensor Networks for Data Transmission Security. 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), 1–7. doi:10.1109/accai61061.2024.10601841.
[13] Shukla, M., Brijendra Kumar Joshi, & Singh, U. (2024). A Novel Machine Learning Algorithm for MANET Attack: Black Hole and Gray Hole. Wireless Personal Communications, 138(1), 41–66. doi:10.1007/s11277-024-11360-4.
[14] Shafi, S., & Venkata Ratnam, D. (2023). New Energy Aware MPR Selection for Securing OLSR Routing Scheme under Black Hole Attack: A Machine Learning Approach. Wireless Personal Communications, 132(3), 1917–1931. doi:10.1007/s11277-023-10690-z.
[15] Hussain, K., Xia, Y., Onaizah, A. N., Manzoor, T., & Jalil, K. (2022). Hybrid of WOA-ABC and proposed CNN for intrusion detection system in wireless sensor networks. Optik, 271, 170145. doi:10.1016/j.ijleo.2022.170145.
[16] Kurtkoti, M., Premananda, B.S., Vishwavardhan Reddy, K. (2022). Performance Analysis of Machine Learning Algorithms in Detecting and Mitigating Black and Gray Hole Attacks. Innovative Data Communication Technologies and Application. Lecture Notes on Data Engineering and Communications Technologies, Springer, Singapore. doi:10.1007/978-981-16-7167-8_69.
[17] Shahid, U., Zunnurain Hussain, M., Zulkifl Hasan, M., Haider, A., Ali, J., & Altaf, J. (2024). Hybrid Intrusion Detection System for RPL IoT Networks Using Machine Learning and Deep Learning. IEEE Access, 12, 113099–113112. doi:10.1109/ACCESS.2024.3442529.
[18] Garcia Ribera, E., Martinez Alvarez, B., Samuel, C., Ioulianou, P. P., & Vassilakis, V. G. (2022). An Intrusion Detection System for RPL-Based IoT Networks. Electronics (Switzerland), 11(23), 4041. doi:10.3390/electronics11234041.
[19] Choukri, W., Lamaazi, H., & Benamar, N. (2022). A Novel Deep Learning-based Framework for Blackhole Attack Detection in Unsecured RPL Networks. 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). doi:10.1109/3ict56508.2022.9990664.
[20] Abdiyeva-Aliyeva, G., Hematyar, M., & Bakan, S. (2021). Development of System for Detection and Prevention of Cyber Attacks Using Artifıcial Intelligence Methods. 2021 2nd Global Conference for Advancement in Technology (GCAT), 1–5. doi:10.1109/gcat52182.2021.9587584.
[21] Ahmadi, K., & Javidan, R. (2024). A novel RPL defense mechanism based on trust and deep learning for internet of things. Journal of Supercomputing, 80(12), 16979–17003. doi:10.1007/s11227-024-06118-5.
[22] Prajisha, C., & Vasudevan, A. R. (2022). An Intrusion Detection System for Blackhole Attack Detection and Isolation in RPL Based IoT Using ANN. Advanced Computing, 332–347. doi:10.1007/978-3-030-95502-1_26.
[23] Osman, M., He, J., Zhu, N., & Mokbal, F. M. M. (2024). An ensemble learning framework for the detection of RPL attacks in IoT networks based on the genetic feature selection approach. Ad Hoc Networks, 152, 103331. doi:10.1016/j.adhoc.2023.103331.
[24] Gurung, S., & Mankotia, V. (2024). ABGF-AODV protocol to prevent black-hole, gray-hole and flooding attacks in MANET. Telecommunication Systems, 86(4), 811–827. doi:10.1007/s11235-024-01154-1.
[25] Reshi, I. A., Sholla, S., & Najar, Z. A. (2024). Safeguarding IoT networks: Mitigating black hole attacks with an innovative defense algorithm. Journal of Engineering Research (Kuwait), 12(1), 133–139. doi:10.1016/j.jer.2024.01.014.
[26] Karimy, A. U., & Reddy, P. C. (2024). Analyzing Federated Learning as a novel approach for enhancing security and privacy in the Internet of Things (IoT). 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 1–7. doi:10.1109/icaect60202.2024.10468686.
[27] Belkheir, M., Rouissat, M., Mokaddem, A., Ziani, D., & Lorenz, P. (2025). Lightweight Novel Approach for Collaborative Packet-Based Mitigation of Blackhole Attacks in RPL-Based IoT. Journal of Network and Systems Management, 33(2), 34. doi:10.1007/s10922-025-09908-1.
[28] Yazdanypoor, M., Cirillo, S., & Solimando, G. (2024). Developing a Hybrid Detection Approach to Mitigating Black Hole and Gray Hole Attacks in Mobile Ad Hoc Networks. Applied Sciences (Switzerland), 14(17), 7982. doi:10.3390/app14177982.
[29] Abdallah, A. A., El Sayed Abdallah, M. S., Aslan, H., Azer, M. A., Cho, Y.-I., & Abdallah, M. S. (2024). Enhancing Mobile Ad Hoc Network Security: An Anomaly Detection Approach Using Support Vector Machine for Black-Hole Attack Detection. International Journal of Safety and Security Engineering, 14(4), 1015–1028. doi:10.18280/ijsse.140401.
[30] Mathi, S., Gudivada, R. L., Madala, L. C., Reddy, K. A., & Putta, J. (2025). RPLGuard_Dataset-v1-2025: RPL Dataset for Guarding against Attacks. IEEE Dataport. doi:10.21227/mhgp-zg08.
- This work (including HTML and PDF Files) is licensed under a Creative Commons Attribution 4.0 International License.
