Design of advanced intrusion detection system for multimodal fuzzy-based convolutional neural networks (mFCNN)

Abstract

The Internet of Things (IoT) develops a smart autonomous system capable of controlling and facilitating numerous human intervention activities such as inventory, electricity, traffic, and health management. It primarily operates by interconnecting networks and objects in various locations. Communication is carried out between devices to accomplish diverse missions in industrial, household, and healthcare applications. The involvement of the IoT in these applications renders the devices susceptible to cyber-attacks. In this work, an intelligent methodology was proposed to defend the devices against various security-related threats using modified deep learning algorithms. The intrusion detection system was designed using a hybrid model comprising multimodal fuzzy-based convolutional neural networks (mFCNN) in association with bidirectional long-short term memory (Bi-LSTM) and the enhanced code element embedding (ECEE) method. Here, the input for training is derived from real images, videos, and audio information retrieved from various sensors. Initially, the ECEE in each input frame is converted into suitable vectors. These vectors are then mapped to corresponding fixed-length vectors for embedding to achieve the most compressed representation. The Bi-LSTM is utilized for extracting relevant information related to spatial features, thus providing an effective intrusion detection mechanism. The mFCNN extracts the most critical temporal features by classifying contextual inputs using the captured videos and images. The experimental results demonstrate that the proposed hybrid model yields better results compared to existing methodologies, with the accuracy of protection benchmarked at 99.91%, which is higher than other baseline methods.