Enhancing Internet of Things (IoT) Network Security: A Machine Learning-Driven Framework for Real-Time Intrusion Detection and Anomaly Classification

The rapid proliferation of IoT devices presents serious IoT network cybersecurity threats; hence, advanced IDSs are necessary. Signature and rule-based IDS mechanisms cannot address novel attacks, generate excessive alarms, and are computationally inefficient. Therefore, in response, in this paper, a machine learning IDS for IoT network real-time intrusion detection and anomaly categorization is proposed via black widow optimization (BWO) for optimal feature and hyperparameter selection. The IDS employs standard machine learning models, such as random forest and support vector machines (SVMs), and deep models, such as long short-term memory (LSTM), to address IoT environment nuances. The framework is evaluated on Bot-IoT and UNSW-NB15 datasets, such as various IoT-based attacks and normal traffic. The BWO algorithm maximizes feature reduction; for Bot-IoT, 57.1%; and for UNSW-NB15, 55.1%, while retaining better detection accuracy. Experimental evidence demonstrates the strength of the framework, where LSTM offers optimal detection accuracy (99.1%) and low false alarms (0.9%). The SVM model is computationally efficient and has a low training time (90 s), inference time (10 ms), spac