Intrusion Detection for Blockchain-Based Internet of Things Using Gaussian Mixture–Fully Convolutional Variational Autoencoder Model

Abstract

The Internet of Things (IoT) is an evolving paradigm that has dramatically transformed the traditional style of living into a smart lifestyle. IoT devices have recently attained great attention due to their wide range of applications in various sectors, such as healthcare, smart home devices, smart industries, smart cities, and so forth. However, security is still a challenging issue in the IoT environment. Because of the disparate nature of IoT devices, it is hard to detect the different kinds of attacks available in IoT. Various existing works aim to provide a reliable intrusion detection system (IDS) technique. But they failed to work because of several security issues. Thus, the proposed study presents a blockchain-based deep learning model for IDS. Initially, the input data are preprocessed using min-max normalization, converting the raw input data into improved quality. In order to detect the presented attacks in the provided dataset, the proposed work introduced Gaussian mixture–fully convolutional variational autoencoder (GM-FCVAE) model. The implementation is performed in Python, and the performance of the proposed GM-FCVAE model is analyzed by evaluating several metrics. The proposed GM-FCVAE model is tested on three datasets and attained superior accuracy of 99.18%, 98.81%, and 98.4% with UNSW-NB15, CICIDS 2019, and N_BaIoT datasets, respectively. The comparison reveals that the proposed GM-FCVAE model obtained higher results than the other deep learning techniques. The outperformance shows the efficacy of the proposed study in identifying security attacks.