A recent benchmark study of GANs models for securing mobile applications
Akram Chhaybi, Saiida Lazaar, Mohammed Hassine · 2023
Generative adversarial networks (GANs) are a technology that uses two neural networks against each other in a maximization game to generate new instances of data that look similar to actual data. The GANs was first proposed by Ian Goodfellow et al.[10]. From there, researchers have presented many models of GANs, such as Wasserstein GAN, conditional GAN, deep convolutional GAN, and many others models. Every model has its character and architecture, and there are some elements in common. One neural network is called the generator; it generates new data samples that have the same probability distribution as the distribution of training data. While the other is called the discriminator, its role is to determine whether the input data came from the training dataset or were created by the generator. In other words, it classifies if the generated data that was caused by the generator is real or fake. The GANs technology was used widely in image and video generation, but lately, this technique has broken into the mobile security world. Especially for android, since it is an open-source operation system, it provides hackers with considerable freedom to develop new types of attacks and malware. In this paper, we provide a comparison between the well-known GANs models: Wasserstein GAN, conditional GAN, and deep convolutional GAN. We explain their architectures, objectives functions, and critical situations in which they can be used for the security of mobile applications