An Intelligent Cybersecurity Framework for IoT Environments Using a Multi-Stage Heuristic Algorithm Fused with a Spatiotemporal Attention Mechanism

With the increasing risk of cyberattacks, cybersecurity has now become a major area of the Internet of Things (IoT). Cyberattacks on IoT devices can result in serious concerns, including unauthorized access, disruption of critical services, and data breaches. IoT security aims to decrease the risk for enterprises and users by ensuring the safety of IoT resources and user privacy. Novel cybersecurity tools and technologies have enabled significant improvements in IoT security management. Recently, Artificial Intelligence (AI) has been applied to identify cyber threats, rapidly analyze millions of events, and detect multiple threats. Deep Learning (DL) has proven effective and offers several advantages for addressing IoT cybersecurity challenges. This study proposes an Optimized Dimensionality Reduction with an Attention Mechanism for Cyberattack Detection using Metaheuristic Optimization Algorithms (ODRAMCD-MOA). First, a min–max normalization model is used in the data preprocessing phase. Next, the Fruit Fly Optimization Algorithm (FOA) is employed for Feature Selection (FS). Subsequently, a hybrid of a Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM) model with a Spatiotemporal Attention (STA) mechanism (CNN-BiLSTM-STA) is utilized for cybersecurity classification. Finally, the hyperparameters of the CNN-BiLSTM-STA method are optimized using the Augmented Red Panda Optimizer (ARPO). Comparative analysis shows that the ODRAMCD-MOA methodology achieves accuracy values of 99.79% and 99.03% on the ToN-IoT and BoT-IoT datasets, respectively, outperforming existing models.