Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks
Tóm tắt
Anomaly detection in Wireless Sensor Networks (WSNs) is critical for their reliable and secure operation. Optimizing resource efficiency is crucial for reducing energy consumption. Two new algorithms developed for anomaly detection in WSNs—Ensemble Federated Learning (EFL) with Cloud Integration and Online Anomaly Detection with Energy-Efficient Techniques (OAD-EE) with Cloud-based Model Aggregation. EFL with Cloud Integration uses ensemble methods and federated learning to enhance detection accuracy and data privacy. OAD-EE with Cloud-based Model Aggregation uses online learning and energy-efficient techniques to conserve energy on resource-constrained sensor nodes. By combining EFL and OAD-EE, a comprehensive and efficient framework for anomaly detection in WSNs can be created. Experimental results show that EFL with Cloud Integration achieves the highest detection accuracy, while OAD-EE with Cloud-based Model Aggregation has the lowest energy consumption and fastest detection time among all algorithms, making it suitable for real-time applications. The unified algorithm contributes to the system's overall efficiency, scalability, and real-time response. By integrating cloud computing, this algorithm opens new avenues for advanced WSN applications. These promising approaches for anomaly detection in resource constrained and large-scale WSNs are beneficial for industrial applications.
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Tài liệu tham khảo
Liu Y, Garg S, Nie J, Zhang Y, Xiong Z, Kang J, Hossain MS (2020) Deep anomaly detection for time-series data in industrial IoT: A communication-efficient on-device federated learning approach. IEEE Internet Things J 8(8):6348–6358. https://doi.org/10.1109/JIOT.2020.3011726
Mahesh B (2020) Machine learning algorithms-a review. International Journal of Science and Research (IJSR) 9(1):381–386
Gebremariam GG, Panda J, Indu S (2023) Blockchain-based secure localization against malicious nodes in IoT-based wireless sensor networks using federated learning. Wirel Commun Mob Comput 2023:1–27. https://doi.org/10.1155/2023/8068038
Deng B, Chen X, Chen X, Pei S, Wan and SK Goudos Trusted Edge Computing System Based on Intelligent Risk Detection for Smart IoT, in IEEE Transactions on Industrial Informatics https://doi.org/10.1109/TII.2023.3245681
Lu J, Pan B, Seid AM, Li B, Hu G and Wan S Truthful Incentive Mechanism Design via Internalizing Externalities and LP Relaxation for Vertical Federated Learning, in IEEE Transactions on Computational Social Systems https://doi.org/10.1109/TCSS.2022.3227270.
Lin Y et al (2023) DRL-Based Adaptive Sharding for Blockchain-Based Federated Learning. IEEE Trans Commun 71(10):5992–6004. https://doi.org/10.1109/TCOMM.2023.3288591
Xie M, Han S, Tian B, Parvin S (2011) Anomaly detection in wireless sensor networks: A survey. J Netw Comput Appl 34(4):1302–1325. https://doi.org/10.1016/j.jnca.2011.03.004
Dwivedi RK, Rai AK, & Kumar R (2020) A study on machine learning based anomaly detection approaches in wireless sensor network. In Data Science and Engineering (Confluence) 10th International Conference on Cloud Computing, 2020 (pp. 194–199). IEEE Publications. https://doi.org/10.1109/Confluence47617.2020.9058311
Cauteruccio F, Fortino G, Guerrieri A, Liotta A, Mocanu DC, Perra C, Terracina G, Torres Vega MT (2019) Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance. Information Fusion 52:13–30. https://doi.org/10.1016/j.inffus.2018.11.010
Stephanie V, Khalil I, Atiquzzaman M, & Yi X (2022) Trustworthy privacy-preserving hierarchical ensemble and federated learning in healthcare 4.0 with blockchain. IEEE Transactions on Industrial Informatics, 19(7), 7936–7945. https://doi.org/10.1109/TII.2022.3214998
Huang Z, Wu Y, Tempini N, Lin H, Yin H (2022) An energy-efficient and trustworthy unsupervised anomaly detection framework (EATU) for IIoT. ACM Transactions on Sensor Networks 18(4):1–18. https://doi.org/10.1145/3543855
Hu W, Liao Y, Vemuri R (2003) Robust support vector machines for anomaly detection in computer security 168–174
Singh VK, & Govindarasu M (2018) Decision tree based anomaly detection for remedial action scheme in smart grid using pmu data. In IEEE Power and Energy Society General Meeting (PESGM). IEEE Publications, 2018, (1–5). https://doi.org/10.1109/PESGM.2018.8586159
Biswas P, Samanta T (2021) Anomaly detection using ensemble random forest in wireless sensor network. Int J Inf Technol 13(5):2043–2052. https://doi.org/10.1007/s41870-021-00717-8
Lorenz J, Silva MI, Aparício D, Ascensão JT, & Bizarro P (2020). Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity. In Proceedings of the First ACM International Conference on AI in Finance (pp. 1–8). https://doi.org/10.1145/3383455.3422549
Mothukuri V, Khare P, Parizi RM, Pouriyeh S, Dehghantanha A, Srivastava G (2021) Federated-learning-based anomaly detection for IoT security attacks. IEEE Internet Things J 9(4):2545–2554. https://doi.org/10.1109/JIOT.2021.3077803
Ifzarne S, Tabbaa H, Hafidi I, & Lamghari N (2021) Anomaly detection using machine learning techniques in wireless sensor networks. In Journal of Physics: Conference Series (Vol. 1743, No. 1, p. 012021). IOP Publishing, 1743(1). https://doi.org/10.1088/1742-6596/1743/1/012021
Kaur A, Pal SK, Singh AP (2018) Hybridization of K-means and firefly algorithm for intrusion detection system. International Journal of System Assurance Engineering and Management 9(4):901–910. https://doi.org/10.1007/s13198-017-0683-8
Emy Dorfman LE, Leite JCL, Giugliani R, Riegel M (2015) Microarray-based comparative genomic hybridization analysis in neonates with congenital anomalies: Detection of chromosomal imbalances. Jornal de Pediatria 91(1):59–67. https://doi.org/10.1016/j.jped.2014.05.007
Li J (2008) Distributed estimation in resource-constrained wireless sensor networks. Georgia Institute of Technology. http://hdl.handle.net/1853/26633
Barr JR, Shaw P, Abu-Khzam FN, & Chen J (2019) Combinatorial text classification: The effect of multi-parameterized correlation clustering. In First International Conference on Graph Computing (GC), 2019 (pp. 29–36). IEEE Publications. https://doi.org/10.1109/GC46384.2019.00013
Khan RA, Mohammadani KH, Soomro AA, Hussain J, Khan S, Arain TH et al (2018) An energy efficient routing protocol for wireless body area sensor networks. Wireless Pers Commun 99. https://doi.org/10.1007/s11277-018-5285-5
Ferreira AC, Vilaça MA, Oliveira LB, Habib E, Wong HC, Loureiro AA (2005) On the security of cluster-based communication protocols for wireless sensor networks 449–458. https://doi.org/10.1007/978-3-540-31956-6_53
Ali H, Tariq UU, Hussain M, Lu L, Panneerselvam J, Zhai X (2021) ARSH-FATI: A Novel Metaheuristic for Cluster Head Selection in Wireless Sensor Networks. IEEE Syst J 15(2):2386–2397. https://doi.org/10.1109/JSYST.2020.2986811
Ali H, Tariq UU, Hardy J, Zhai X, Lu L, Zheng Y, Bensaali F, Amira A, Fatema K, Antonopoulos N (2021) A survey on system level energy optimisation for MPSoCs in IoT and consumer electronics. Computer Science Review 41:100416
OReilly, C., Gluhak, A., Imran, M. A., & Rajasegarar, S. (2014) Anomaly detection in wireless sensor networks in a non-stationary environment. IEEE Communications Surveys and Tutorials 16(3):1413–1432. https://doi.org/10.1109/SURV.2013.112813.00168
Haque SA, Rahman M, Aziz SM (2015) Sensor anomaly detection in wireless sensor networks for healthcare. Sensors 15(4):8764–8786. https://doi.org/10.3390/s150408764
Rajasegarar S, Leckie C, Palaniswami M (2008) Anomaly detection in wireless sensor networks. IEEE Wirel Commun 15(4):34–40. https://doi.org/10.1109/MWC.2008.4599219
Schneble W, Thamilarasu G (2019) Attack detection using federated learning in medical cyber-physical systems
Ghimire B, Rawat DB (2022) Recent advances on federated learning for cybersecurity and cybersecurity for federated learning for internet of things. IEEE Internet Things J 9(11):8229–8249. https://doi.org/10.1109/JIOT.2022.3150363
Chen Z, Lv N, Liu P, Fang Y, Chen K, Pan W (2020) Intrusion detection for wireless edge networks based on federated learning. IEEE Access 8:217463–217472. https://doi.org/10.1109/ACCESS.2020.3041793
Ihekoronye VU, Nwakanma CI, Kim DS, Lee JM (2023) DATA-FedAVG: delay-aware truncated accuracy-based federated averaging for intrusion detection in UAV network. J Kor Inst Commun Inform Sci 48:648–668. https://doi.org/10.7840/kics.2023.48.6.648
Yang X, Chen Y, Qian X, Li T, Lv X (2021) BCEAD: a blockchain-empowered ensemble anomaly detection for wireless sensor network via isolation forest. Security and Communication Networks 2021:1–10
Trinh V-V, Tran KP, Huong T (2017) Data driven hyperparameter optimization of one-class support vector machines for anomaly detection in wireless sensor networks 6–10. https://doi.org/10.1109/ATC.2017.8167642
Haque A, Chowdhury NU, Soliman H, Hossen MS, Fatima T, Ahmed I (2023) Wireless sensor networks anomaly detection using machine learning: a survey. https://doi.org/10.48550/arXiv.2303.08823