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

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