Image analysis approach based on VAE-GAN for IoT malware detection in urban environments

The Internet of Things (IoT) has revolutionised technology within intelligent urban environments; however, this has concurrently given rise to security and privacy risks, including the proliferation of various types of malware, which can lead to detrimental consequences. This paper presents a GAN-inspired approach for the classification of malware imagery, employing an autoencoder (AE) as the synthetic data generator and leveraging transfer learning for the discriminator. This framework is designed to identify various malware threats that target IoT networks through the use of RGB images collected directly from malware samples. The generator is specifically constructed for effective data reconstruction, incorporating different AE architectures and denoising techniques, while the discriminator utilises a pre-trained convolutional neural network (CNN)-based model to maximise performance. Furthermore, to address data imbalance in the multi-label classification task, we introduced a self-adjustive oversampling technique to augment the sample volume from minority classes. The proposed method was evaluated on several multi-label malware-based imagery datasets to assess its robustness. Comparative performance analysis was conducted using well-established image classification models, including VGG19, MobileNet, and Xception, which were integrated into the discriminator model as a pre-trained block. The results demonstrate that the variational AE-GAN is highly implementable and scalable for the malware classification task, exhibiting commendable detection performance and generalisability.