Adversarial Attacks Against IoT Networks using Conditional GAN based Learning

During the last decade, the integration of artificial intelligence (AI) and the use of intrusion detection systems (IDSs) in the Internet of Things(IoT) networks have brought a new dimension to technological progress. Deep learning (DL) and machine learning (ML)-based IDS are vulnerable to adversarial perturbations. However, anomaly detection methods suffer from unbalanced and missing sample data, thus causing IDS training to be complicated. In this paper, we propose using conditional generative adversarial networks (cGANs) to enhance the training process by handling the unbalanced data and coping with the lack of specifics class samples, which may succeed in evading our Convolutional Neural Network-Long Short-Term Memory (CNNLSTM) based-IDS model. We evaluated our proposed IDS model before and after applying the adversarial training using the Bot-IoT dataset. Promising results showed that the accuracy of detecting Theft attacks could be increased by 40%. To the best of our knowledge, we are the first to suggest the combination of cGAN and CNNLSTM based-IDS system to enhance its performance.