Hybrid Deep Learning for Intrusion Detection in IoT Networks Using CNNs, Bigrus, Attention Mechanisms, and Advanced Binary Snake Optimizer (BSO) Techniques

As Internet of Things (IoT) networks are growing rapidly and being applied in a vast array of fields, they increasingly become targets for cyber-attacks needing reliable IDSs. This perspective paper presents a highly innovative hybrid IDS system based on Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Units (BiGRU), Attention Mechanisms, and Binary Snake Optimizer (BSO) for enhanced IoT environment intrusion detection. The CNNs then automatically take important features out of network traffic, while at the same time, BiGRUs capture past and future temporal dependencies about the traffic patterns. Attention Mechanism focuses on different parts of the input feature, enabling the model to detect subtle and evolving attacks. The BSO optimizes the hyperparameters of our model so that convergence time is reduced, improving efficiency for real-time detection. Accordingly, the experimental results have shown that the proposed system performed better with 99.86% accuracy, 99.69% precision, and 99.57% recall, especially compared to current IDS solutions. The ability of the system to classify emerging threats with great efficiency within dynamic IoT networks, along with its scalable and adaptable features, makes it a strong asset for securing IoT systems.