Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

A Novel Intrusion Detection System to Enhance Internet of Things Security, Based on the Integration of Evolutionary Approach, Blockchain, and Federated Learning

Document Type : Persian Original Article

Authors
Mahdi Bazargani 1
Nafiseh Fareghzadeh 2
Mohammad Hossein Najafi 1
1 Department of Computer Engineering, Za.C., Islamic Azad University, Zanjan, Iran
2 Department of Computer Engineering, Khod.c, Islamic Azad University, Zanjan, Iran
10.22034/jscit.2026.531689.2133
Abstract
In this research, a novel and combinational intrusion detection system has been developed to enhance security in the Internet of Things (IoT) scope, which is designed based on the combination of blockchain, federated learning, and crow evolutionary algorithms. In addition to enhancing security, the proposed approach also addresses the challenges of data privacy and proposes a federated learning-based framework for anomaly detection that is superior to non-federated learning methods. In the operational process of the proposed system, data is first divided into independent blocks and, by using a combination of federated learning and blockchain approaches, samples are extracted for model training and recognition tasks. The proposed system is evaluated using supervised and unsupervised deep learning models, and the performance of the models in enhancing IoT network security is evaluated and compared. Also, the robustness of the proposed approach against a diverse range of attack vectors is carefully tested, and different federated learning algorithms are replaced with counterparts to reduce the computational load and optimize communication costs. Simulation results presented that the integration of dynamic thresholding method with federated learning model provides very high accuracy in classifying network traffic windows as normal or abnormal for detecting DDoS attacks. Based on the evaluation results, the proposed system has achieved an accuracy of 98.7%, a true positive rate of 0.987, a false positive rate of 0.026, and a recall of 0.987, which shows superior performance compared to the related approaches.
Keywords
Intrusion Detection
Security
IoT
Blockchain
Federated Learning
Crow Evolutionary Algorithm
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Journal of Soft Computing and Information Technology
Volume 15, Issue 3
Autumn - October
Autumn 2026
Pages 36-51