# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 2 Related Works  
In recent scientific literature, many publications utilize GANs for detecting and fighting cybersecurity threats. [PERSON] et al. [13] proposed a Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) model for generating synthetic network traffic data that can realistically simulate patterns of zero-day attacks. Experimental testing using the NSL-KDD dataset [14] showed that the WGAN-GP model could improve the detection accuracy for both binary classification and multi-classification (2.3% and 2% improvement, respectively). The model helped the classification models in identifying even subtle signatures of zero-day attacks. A GAN-based model for detecting zero-day malware in Android mobile devices was introduced by [PERSON] and [PERSON] [15]. This model was capable of producing previously unknown viruses and threats, which were used for the training process. Both the sigmoid and ReLU activation functions were employed, and the Frechet Inception Distance (FID) and Inception Score (IS) scores were used for evaluating the performance of the model. The FID metric is commonly used for assessing the performance of GANs [16] and measures the similarity of the training set distribution and the generated samples' distribution. The IS evaluates the diversity and quality of the images generated by a GAN model [17]. The model proposed by the authors reached an IS score of 7.65 and FID score of 2.34, indicating that its generated results were very realistic and of high quality. [PERSON] et al. [18] presented a GAN-based malware training and augmentation framework. The so-called PlausMal-GAN framework was capable of generating high-quality zero-day malware images of high diversity. For the classification of malware images, both real and generated malware data were utilized. Furthermore, the framework was tested with four different GAN models, namely the Least Squares Generative Adversarial Network (LSGAN), WGAN-GP, Deep Convolutional Generative Adversarial Network (DCGAN), and Evolutionary Generative Adversarial Network (E-GAN), yielding classification results of up to 98.74% accuracy. [PERSON] [19] proposed a GAN-based framework for binary and multi-class classification of Intrusion Detection. To address the class imbalance issue of many publicly available datasets for NIDS, the authors utilized GAN hyperparameter optimization followed by the chi-square method for feature selection. Experimental testing on the UNSW-NB15 dataset indicated high accuracy (98.14% and 87.44%) and F1-score (98.14% and 86.79%) for binary and multi-class classification, respectively. GANs are also employed in adversarial attacks on IoT and Internet of Vehicles (IoV) applications. [PERSON] et al. [20] proposed an Intrusion Detection System (IDS) and utilized Conditional Generative Adversarial Networks (CGANs) to improve the training process, which often suffers from missing or unbalanced data. More specifically, the IDS model was CNNLSTM-based. These kinds of models combine the strengths of CNNs and Long Short-Term Memory (LSTM) networks. The IDS model was evaluated both before and after applying cGANs. This combination yielded better overall accuracy, precision, and F1-scores in different attack types (e.g., Denial-of-Service-DoS, Distributed Denial-of-Service-DDoS, Info Theft, Info Gathering) and increased the theft attack detection accuracy by 40%. [PERSON] et al. [21] proposed a Semi-supervised GAN model (SGAN) for detecting botnets in Internet of Things (IoT) environments. This model aimed to overcome the challenge of many supervised models, which, due to unlabeled network traffic, sometimes cannot directly categorize botnets that are responsible for a specific attack. The SGAN achieved a binary classification accuracy of 99.89% and a multi-classification accuracy of 59%. These results were better than the results achieved by an Artificial Neural Network (ANN) model and a CNN model when tested on the same dataset.