## 2 Related Works  
The major obstacle in the early detection of ransomware involves obtaining adequate data in the pre-encryption stage when the attack is still being set up and has not yet been executed [1,2]. Addressing this data shortage is crucial, as a sufficient dataset is neededfor precise early detection. Data augmentation is often used in machine learning solutions and presents a promising way to tackle this scarcity of data, a problem commonly faced by malware and ransomware early detection systems. To our understanding, no existing studies specifically address data augmentation in the pre-encryption stage of ransomware attacks [10]. Another challenge stems from the ever-changing nature of ransomware, which complicates the relevancy of features used for detection models [14]. For example, an attack pattern seen in one ransomware variant at a specific time might be more relevant than the same pattern displayed by a different variant at another time. This indicates that the importance of features can vary depending on the ransomware variant and the timing of the attack. Despite this, current early detection methods often operate on the assumption that the importance of these features remains constant, leading to \"behavioral drift.\" This drift mostly results in detection systems becoming quickly outdated and less accurate over time. The Generative Adversarial Network (GAN) has been widely used as an important component of deep neural networks [15]. The GAN model has gained massive attention from researchers recently due to its prominent characteristics. It has two main rewards for machine learning based models: the generality and adversarial [16]. It can generate new samples that can be used to prevent overfitting and, thus, improve machine learning performance. Moreover, it can be used to generate adversarial samples that can be used to improve the discriminability of the model. GAN use alternative training to estimate the density function over a data distribution using the Minimax algorithm [17]. The Minimax game algorithm tries to minimize the maximum possible loss which results in multiple possibilities that can be used to generate new samples. In doing so, GAN projects the available simple distribution to a much more complex high-dimensional, real-world data distribution [18]. GAN trains two adversarial networks called the generator and the discriminator. The generator is trained to map noise samples to synthetic samples with the goal is to generate new adversarial samples that can mislead the discriminator. Meanwhile, the discriminator trains to distinguish the real data samples from synthesized samples that were generated using the generator. GAN creates the new samples by making small changes to the original samples so as to deceive the detection model gain benefit of the nonlinear characteristics of neural networks and thus constructs a model that produces incorrect classification results. Due to its prominent features, many researchers have applied the GAN algorithm to improve the classification performance of machine learning algorithms. [PERSON] and [PERSON] [15] proposed a malware detection model for Internet of Things (IoT) using the Generative Adversarial Network technique and Convolutional Neural Network (CNN). CNN was used to extract high-level features while GAN was used to generate new malware samples to mitigate the limitations of availability of insufficient malware samples in IoT. [PERSON] and [PERSON] [19] utilized GAN to develop a malware detection model-based adversarial example for the Android platform. Their proposed model called bi-objective GAN can generate evasive adversarial-example attacks able to fool the firewall and evade detection. [PERSON] and [PERSON] [20] used GAN to improve the classification accuracy of the malware detection by generating new samples that can mimic realistic-like malware samples as well as the realistic distribution of data.