###### Abstract  
The development of IoT technology has made various IoT applications and services widely used. Because IoT devices have weak information security protection capabilities, they are easy targets for cyber attacks. Therefore, this study proposes MLP-based IoT attack classification with data augmentation for GANs. In situations where the overall classification performance is satisfactory but the performance of a specific class is poor, GANs are employed as a data augmentation mechanism for that class to enhance its classification performance. The experimental results indicate that regardless of whether the training dataset is BoI-IoT or TON-IOT, the proposed method significantly improves the classification performance of classes with insufficient training data when using the data augmentation mechanism with GANs. Furthermore, the classification accuracy, precision,  
security risks becomes paramount to ensure the reliability and safety of interconnected devices and systems.  
Facing increasingly severe threats to the Internet of Things (IoT) security, the development of intrusion detection systems (IDS) has become an essential tool to assist in detecting cyberattacks. Traditional IDSs can effectively monitor network traffic and identify anomalous behavior. They record every packet during detection, and if a packet violates basic routing rules or exhibits malicious activity, they immediately alert the network administrator and automatically generate related reports. However, IDSs require manual updates of the attack patterns and the corresponding remedies. With ever-changing attack techniques, relying solely on traditional manual updates and processing may no longer be an effective solution. Furthermore, most packets are now encrypted to ensure data transmission security, which leads to higher maintenance costs if detection and maintenance need to be improved.  
Following the rapid development of artificial intelligence, deep learning applications have addressed the issues faced by traditional IDSs. These applications eliminate the need for manual packet monitoring or adding attack characteristics and solutions, significantly improving detection efficiency and accuracy. However, most deep learning methods still require large datasets and consume significant time. Additionally, if a category within the dataset has an insufficient number of entries, the learning model might struggle to classify it accurately.  
Therefore, this study proposes using a generative adversarial network (GAN) to classify IoT attacks. Through the continuous adversarial training of a generator and a discriminator, the generator generates data that are close, but not identical, to real data. In addition, a generator is used to augment unbalanced class data to increase the amount and diversity of original data. Subsequently, the augmented data are merged with the original data for training in order to enhance the classification accuracy of each class. Regardless of whether the training dataset is BoT-IoT or ToN-IoT, this method achieves accuracy, precision, recall, and F1 scores exceeding 90% in both overall classification and single-class classification. The rest of this study is organized as follows. In Section 2, we analyze related work proposed by other authors in detail, sort out the advantages and disadvantages of their proposed methods, and further clarify our research goals. In Section 3, we clearly explain our proposed IoT attack classification and data augmentation method using GAN. In Section 4, we demonstrate the proposed IoT attack classification mechanism, which provides a high overall classification accuracy, uses GANs for data augmentation, and improves the classification performance of a single class. In the last section, we conclude the study based on our proposed methods and summarize their performance breakthroughs while also presenting a brief overview of our future research.