Optimized Hybrid Fuzzy NFSC Approach for Enhanced Scalable and Efficient Anomaly Detection in Secure IoT- Systems with Low-Latency

Document Type : Research Paper

Authors

1 Rajkot

2 Department of Computer Science and Engineering, Vidarbha Institute of Technology, Nagpur, India.

3 Department of Computer Engineering, Marwadi University, Rajkot. Gujarat, India

4 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh – 522302, India.

Abstract

The rapid growth and widespread integration of the Internet of Things (IoT) across various domains have enabled systems to autonomously send, receive, and analyze data. Despite its significant benefits for improving quality of life and service efficiency, IoT still faces considerable challenges related to security and privacy. An anomaly-based Intrusion Detection System (IDS) can provide security for IoT networks against various cyber-attacks. This study proposes an anomaly-based IDS to prevent cyber-attacks in IoT environments. In this research, we propose a novel optimized ensemble deep learning-based approach for anomaly-based IDS classification. The proposed approach includes several innovative aspects: initially, raw data are pre-processed, and essential features are extracted using CoffeeNet. The approach employs the Binary Bamboo Forest Growth Optimization Algorithm (BBFGOA) to reduce data characteristics and enhance anomaly detection effectiveness. We use the Optimized Fuzzy NFSC approach an ensemble of the Sharpened Cosine Similarity-based Neural Network (SCS-Net) and a rule-based Deep Fuzzy System with Nonlinear Fuzzy Feature Transform (NFFS-DFS)—to categorize network flows as benign or malicious across multiple categories. The Partial Reinforcement Optimization Algorithm (PRO) is used to tune the hyperparameters in the Fuzzy NFSC approach. Additionally, we employ a Conditional Tabular Generative Adversarial Network (CTGAN) to address the class imbalance issue. The proposed anomaly detection approach is evaluated and analyzed using four benchmark datasets to validate its effectiveness. The evaluation's results demonstrate that the approach enhances detection performance, with a 99.43% F1-score, 99.61% accuracy, 99.45% precision, and 99.38% recall. The findings also show that employing an oversampling technique improves minority sample identification rates, which raises overall accuracy. The suggested approach works better than current techniques since it takes less time to train and detect.

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Main Subjects

References

[1] A. Abusitta, G. H. S. de Carvalho, O. A. Wahab, T. Halabi, B. C. M. Fung, S. Al Mamoori, Deep learning-enabled
anomaly detection for IoT systems, Internet of Things, 21 (2023), 100656. https://doi.org/10.1016/j.iot.
2022.100656
[2] R. Alghamdi, M. Bellaiche, A cascaded federated deep learning based framework for detecting wormhole attacks in
IoT networks, Computers and Security, 125 (2023), 103014. https://doi.org/10.1016/j.cose.2022.103014
[3] Z. Alwaisi, T. Kumar, E. Harjula, S. Soderi, Securing constrained IoT systems: A lightweight machine learning
approach for anomaly detection and prevention, Internet of Things, 28 (2024), 101398. https://doi.org/10.
1016/j.iot.2024.101398
[4] H. Asgharzadeh, A. Ghaffari, M. Masdari, F. S. Gharehchopogh, Anomaly-based intrusion detection system in the
Internet of Things using a convolutional neural network and multi-objective enhanced Capuchin Search Algorithm,
Journal of Parallel and Distributed Computing, 175 (2023), 1-21. https://doi.org/10.1016/j.jpdc.2022.12.009
[5] T. Assy, Y. Mostafa, A. Abd El-khaleq, M. Mashaly, Anomaly-based intrusion detection system using onedimensional
convolutional neural network, Procedia Computer Science, 220 (2023), 78-85. https://doi.org/10.
1016/j.procs.2023.03.013
[6] S. Bacha, A. Aljuhani, K. B. Abdellafou, O. Taouali, N. Liouane, M. Alazab, Anomaly-based intrusion detection
system in IoT using kernel extreme learning machine, Journal of Ambient Intelligence and Humanized Computing,
15(1) (2024), 231-242. https://doi.org/10.1007/s12652-022-03887-w
[7] M. P. S. Bhatia, S. R. Sangwan, Soft computing for anomaly detection and prediction to mitigate IoTbased
real-time abuse, Personal and Ubiquitous Computing, 28(1) (2024), 123-133. https://doi.org/10.1007/
s00779-021-01567-8
[8] M. Bhavsar, K. Roy, J. Kelly, O. Olusola, Anomaly-based intrusion detection system for IoT application, Discover
Internet of things, 3(1) (2023), 5. https://doi.org/10.1007/s43926-023-00034-5
[9] I. D. Borlea, R. E. Precup, F. Dragan, A. B. Borlea, Centroid update approach to K-means clustering, Advances in
Electrical and Computer Engineering, 17(4) (2017). https://doi.org/10.4316/AECE.2017.04001
[10] P. Deng, Y. Huang, Edge-featured multi-hop attention graph neural network for intrusion detection system, Computers and Security, 148 (2025), 104132. https://doi.org/10.1016/j.cose.2024.104132
[11] A. Govindaram, Flbc-ids: A federated learning and blockchain-based intrusion detection system for secure iot
environments, Multimedia Tools and Applications, 84(17) (2025), 17229-17251. https://doi.org/10.1007/
s11042-024-19777-6
[12] N. Hamdi, Federated learning-based intrusion detection system for internet of things, International Journal of
Information Security, 22(6) (2023), 1937-1948. https://doi.org/10.1007/s10207-023-00727-6
[13] M. J. Idrissi, H. Alami, A. El Mahdaouy, A. El Mekki, S. Oualil, Z. Yartaoui, I. Berrada, Fed-anids: Federated
learning for anomaly-based network intrusion detection systems, Expert Systems with Applications, 234 (2023),
121000. https://doi.org/10.1016/j.eswa.2023.121000
[14] Z. Li, P. Wang, Z. Wang, D. C. Zhan, Flowgananomaly: Flow-based anomaly network intrusion detection with
adversarial learning, Chinese Journal of Electronics, 33(1) (2024), 58-71. https://doi.org/10.23919/cje.2022.
00.173
[15] M. H. L. Louk, B. A. Tama, Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly
intrusion detection system, Expert Systems with Applications, 213 (2023), 119030. https://doi.org/10.1016/j.
eswa.2022.119030
[16] L. Narengbam, S. Dey, Harris hawk optimization trained artificial neural network for anomaly based intrusion
detection system, Concurrency and Computation: Practice and Experience, 35(23) (2023), e7771. https://doi.
org/10.1002/cpe.7771
[17] T. Saba, A. Rehman, T. Sadad, H. Kolivand, S. A. Bahaj, Anomaly-based intrusion detection system for IoT
networks through deep learning model, Computers and Electrical Engineering, 99 (2022), 107810. https://doi.
org/10.1016/j.compeleceng.2022.107810
[18] L. Sharma, C. Sharma, S. Lal Roy, Anomaly based network intrusion detection for IoT attacks using deep learning
technique, Computers and Electrical Engineering, 107 (2023), 108626. https://doi.org/10.1016/j.compeleceng.
2023.108626
[19] M. Soltani, B. Ousat, M. J. Siavoshani, A. H. Jahangir, An adaptable deep learning-based intrusion detection system
to zero-day attacks, Journal of Information Security and Applications, 76 (2023), 103516. https://doi.org/10.
1016/j.jisa.2023.103516
[20] M. Vishwakarma, N. Kesswani, A new two-phase intrusion detection system with Na¨ıve Bayes machine learning for
data classification and elliptic envelop method for anomaly detection, Decision Analytics Journal, 7 (2023), 100233.
https://doi.org/10.1016/j.dajour.2023.100233
[21] J. Xiao, L. Yang, F. Zhong, H. Chen, X. Li, Robust anomaly-based intrusion detection system for in-vehicle
network by graph neural network framework, Applied Intelligence, 53(3) (2023), 3183-3206. https://doi.org/10.
1007/s10489-022-03412-8
[22] H. Xu, Z. Sun, Y. Cao, H. Bilal, A data-driven approach for intrusion and anomaly detection using automated
machine learning for the internet of things, Soft Computing, 27(19) (2023), 14469-14481. https://doi.org/10.
1007/s00500-023-09037-4
Iranian Journal of Fuzzy Systems
Volume 22, Issue 5
September and October 2025
Pages 181-202

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