## 1 Introduction
Generative Adversarial Networks have emerged as a powerful tool in cybersecurity, serving many roles in both improving detection methodologies and simulating potential threats. In enhancing detection models, GANs can generate synthetic malware samples, thereby creating more robust and diverse training datasets [18]. This capability is particularly valuable in improving the detection of novel malware variants. Furthermore, GANs play a crucial role in adversarial training, simulating attacks on detection systems to identify and address weaknesses, ultimately leading to more resilient malware detection frameworks. Another significant application of GANs in this field is anomaly detection. By learning the distribution of normal API call patterns, GANs can effectively identify deviations that may indicate potential malware activity [19; 20]. This approach has shown particular promise in the detection of zero-day threats. Additionally, GANs can be employed to generate new malware variants for research purposes, aiding in proactive defense by anticipating future attack vectors. GANs offer unique advantages such as generating synthetic API call sequences to augment training datasets, enhancing system robustness through adversarial training, enabling unsupervised anomaly detection, adapting dynamically to evolving threats, extracting complex features, improving cross-family malware detection, and reducing false-positive rates by refining the distinction between legitimate and malicious behavior. When combined with algorithms like Gated Recurrent Units (GRUs), GANs can significantly improve detection accuracy while keeping computational overhead low [21]. This hybrid approach leverages GRU efficiency in processing sequential data and GAN generative capabilities, creating a powerful, resource-efficient malware detection system. Building on the potential of GANs in malware detection using API calls, this paper presents a hybrid deep learning model to tackle the proliferation of sophisticated malware. Our study combines the capabilities of GANs with advanced algorithms like GRUs to enhance the accuracy and computational efficiency of malware analysis based on API calls from portable executable (PE) files. The hybrid model aims to improve detection accuracy while maintaining low computational overhead. Our paper makes several key contributions to the field of malware detection for Windows environments:  
1. We conduct a comprehensive evaluation of different deep learning algorithms, including BiLSTM, BiGRU, and GRU-GAN, for the classification and detection of malware from API call sequences. 2. We propose a hybrid model that combines GRU and GAN algorithms for the accurate detection of malware from API call sequences. 3. We provide an in-depth analysis of model performance across diverse datasets to test the generalization capabilities of the models. 4. We benchmark the proposed hybrid model against existing models, focusing on both performance and computational overhead. The structure of this paper is as follows: Section 2 explores Windows API calls and deep learning algorithms and reviews related work on malware detection using API calls. Section 3 presents the proposed model architecture and methodology. In Section 4, we analyze the results, evaluate the performance of the proposed model, and assess its computational efficiency. We also benchmark our model against existing malware detection models. Finally, Section 5 concludes the paper and outlines potential directions for future research.