A Hybrid Deep Learning and Optimization-based Framework for Real-Time Cyber Threat Detection in Network and IoT Environments

The growing complexity and prevalence of cyberattacks in contemporary connected and Internet of Things (IoT) settings require intrusion detection frameworks that are corrective, receptive, and strong. Conventional security measures tend to fail in identifying non-traditional and novel attacks and have low false alarms. In this paper, the hybrid deep learning-based cybersecurity system called TriNet is introduced to detect cyber threats in diverse and heterogeneous network and IoT systems in real-time. The suggested framework combines Chien physics in formed neural networks, Pt-Net, and Generalized LaGrange and Neural Networks to simulate spatial, temporal, and dynamic behavioral patterns of cyber intrusion. Mean imputation, label encoding, and Min–Max normalization are used to preprocess network traffic and IoT telemetry data in the UNSW-NB15 and TON-IoT benchmark datasets. To improve the detection efficiency and complexity computations, a hybrid approach to feature selection that combines the use of the PUMA optimizer and Hippopotamus Optimization Algorithm is used. The predictions of the individual deep learning models are put together through an ensemble-based majority voting system to give the ultimate classification decision. It is shown that TriNet has a high accuracy, better detection rates, and lower false alarm rates than currently used methods of intrusion detection, making it suitable to be used in the next-generation cybersecurity.