Improved Resnet Model Based on Positive Traffic Flow for IoT Anomalous Traffic Detection

Abstract

The Internet of Things (IoT) has been highly appreciated by several nations and societies as a worldwide strategic developing sector. However, IoT security is seriously threatened by anomalous traffic in the IoT. Therefore, creating a detection model that can recognize such aberrant traffic is essential to ensuring the overall security of the IoT. We outline the main approaches that are used today to detect anomalous network traffic and suggest a Resnet detection model based on fused one-dimensional convolution (Conv1D) for this purpose. Our method combines one-dimensional convolution and a Resnet network to create a new network model. This network model improves the residual block by including Conv1D and Conv2D layers for two-dimensional convolution. This change enhances the model’s ability to identify aberrant traffic by enabling the network to extract feature information from one-dimensional linearity and two-dimensional space. The CIC IoT Dataset from the Canadian Institute for Cybersecurity Research was used to assess the effectiveness of the proposed enhanced residual network technique. The outcomes demonstrate that the algorithm performs better at identifying aberrant traffic in the IoT than the original residual neural network. The accuracy achieved can be as high as 99.9%.