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<p>The rapid proliferation of the Internet of Things (IoT) across diverse domains has intensified cybersecurity concerns, particularly within lightweight communication protocols such as Message Queuing Telemetry Transport (MQTT), whose publish–subscribe architecture makes it a frequent target for cyberattacks. Motivated by the growing limitations of conventional intrusion detection systems (IDSs) in handling dynamic IoT environments, this study develops an efficient model that enhances detection accuracy while minimizing inference time. A hybrid intrusion detection framework that integrates the Adaptive Neuro-Fuzzy Inference System (ANFIS) with the Sailfish Optimization Algorithm (SOA) is proposed to achieve this objective. The proposed system is validated using uni-flow and bi-flow versions of the MQTT-IoT-IDS2020 dataset after dimensionality reduction via Principal Component Analysis (PCA). Experimental results demonstrate that the ANFIS–SOA model outperforms traditional classifiers, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), and Naïve Bayes Classifier (NBC), achieving an accuracy of 99.7% and a mean squared error (MSE) of 0.0125 on the bi-flow dataset, while also demonstrating superior precision, recall, F1-score, and faster inference time. The findings confirm that the ANFIS–SOA framework advances IDS design by combining adaptive neuro-fuzzy reasoning with metaheuristic optimization, offering a transparent, accurate, and practical solution for securing MQTT-based IoT communications.</p>

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Intrusion Detection on the Internet of Things Using the Adaptive Neural Fuzzy Inference System Trained by the Sailfish Optimization Algorithm

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Maryam Malekabadi,
  • Monireh Houshmand,
  • Majid Haghparast,
  • Somayyeh Jafarali Jassbi,
  • Akram Reza

摘要

The rapid proliferation of the Internet of Things (IoT) across diverse domains has intensified cybersecurity concerns, particularly within lightweight communication protocols such as Message Queuing Telemetry Transport (MQTT), whose publish–subscribe architecture makes it a frequent target for cyberattacks. Motivated by the growing limitations of conventional intrusion detection systems (IDSs) in handling dynamic IoT environments, this study develops an efficient model that enhances detection accuracy while minimizing inference time. A hybrid intrusion detection framework that integrates the Adaptive Neuro-Fuzzy Inference System (ANFIS) with the Sailfish Optimization Algorithm (SOA) is proposed to achieve this objective. The proposed system is validated using uni-flow and bi-flow versions of the MQTT-IoT-IDS2020 dataset after dimensionality reduction via Principal Component Analysis (PCA). Experimental results demonstrate that the ANFIS–SOA model outperforms traditional classifiers, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), and Naïve Bayes Classifier (NBC), achieving an accuracy of 99.7% and a mean squared error (MSE) of 0.0125 on the bi-flow dataset, while also demonstrating superior precision, recall, F1-score, and faster inference time. The findings confirm that the ANFIS–SOA framework advances IDS design by combining adaptive neuro-fuzzy reasoning with metaheuristic optimization, offering a transparent, accurate, and practical solution for securing MQTT-based IoT communications.