The Internet of Things (IoT) technology is widely used in various areas. Due to design constraints, IoT devices have low power consumption at a low cost. However, with increased risks to information security, IoT devices are easy targets of cyberattacks. These attacks render infrastructure devices unable to provide efficient information exchange. At the same time, the establishment of a deep learning (DL) model requires a large amount of data and takes a lot of time. Therefore, in this study, we proposed a method based on generative adversarial networks (GANs) for the classification of IoT attacks. In the original data set of 3 million records, only 50 thousand records were taken to train the GAN model. The generator automatically generated similar data and the discriminator in training, and the classification accuracy reached 80%.
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IoT Attack Classification Based on Generative Adversarial Networks
Semantic Scholar · Computer Science · 2023
Abstract
The Internet of Things (IoT) technology is widely used in various areas. Due to design constraints, IoT devices have low power consumption at a low cost. However, with increased risks to information security, IoT devices are easy targets of cyberattacks. These attacks render infrastructure devices unable to provide efficient information exchange. At the same time, the establishment of a deep learning (DL) model requires a large amount of data and takes a lot of time. Therefore, in this study, we proposed a method based on generative adversarial networks (GANs) for the classification of IoT attacks. In the original data set of 3 million records, only 50 thousand records were taken to train the GAN model. The generator automatically generated similar data and the discriminator in training, and the classification accuracy reached 80%.