## 1 Introduction  
In recent years, the cybersecurity landscape has undergone a significant evolution, driven by the exponential growth of cyber threats. This proliferation of malicious activities has underscored the urgent need for innovative approaches to detect and mitigate potential attacks [1]. The swift progress of technology, despite its many benefits, has made computer systems and networks more susceptible to a wide range of threats [2]. Malware, a term derived from \"malicious software\", represents one of the most significant challenges in this domain. By the first quarter of 2024, over 1.3 billion unique malware samples had been recorded, marking a steady increase from the approximately 1.23 billion documented by the end of 2021 [3]. This alarming trend highlights the critical importance of developing robust cybersecurity solutions to combat these ever-evolving threats [4]. Malware can be classified into various categories, including viruses, worms, trojans, spyware, ransomware, and logic bombs, each with distinct behavioral characteristics and attributes [5]. The potential impact of these malicious programs ranges from unauthorized access and service disruption to data theft and sometimes irreparable system damage [6; 7]. Understanding the infiltration methods of malware into computer systems requires knowledge of malware families, as members of the same family mostly exhibit comparable behaviors. Malware analysts and incident responders can enhance preventive measures against malware by identifying its family and also simplifying the overall analysis process. Malware analysis, an important aspect of cybersecurity, involves the systematic investigation and dissection of malicious software. This process provides valuable insights into the operational mechanisms, objectives, and potential impacts of these programs on computer systems, networks, and end-user devices [8]. By analyzing the tactics, techniques, and procedures (TTPs) employed by malicious actors, cybersecurity professionals can develop more effective countermeasures, including intrusion detection system (IDS) rules, antivirus signatures, and defensive strategies [2]. Malware analysis typically employs three primary methods: static, dynamic, and hybrid approaches. Static analysis examines the structure of the malware code without execution, while dynamic analysis observes the behavior of the malware by running it in a controlled environment. Hybrid techniques combine elements of both approaches [9]. One critical aspect of dynamic analysis is the examination of API (Application Programming Interface) calls made by the malware, as these represent its behavioral patterns and interactions with the operating system [10]. The Windows API, consisting of numerous functions within specific software libraries, plays a key role in facilitating interactions between applications and the operating system. Analyzing the patterns and sequences of API calls is essential for verifying the legitimacy of system actions and identifying potentially malicious behavior [11]. An application makes several API calls to perform tasks. For instance, to delete a file, the application will call the DeleteFileA Windows API [12; 13; 14]. Recently, deep learning algorithms have evolved as useful tools in the field of malware detection and classification. These algorithms, a subset of machine learning techniques, leverage artificial neural network architectures to discover and learn complex patterns from large, high-dimensional datasets [15; 16]. Deep learning models can automatically extract high-level features and learn from both labeled and unlabeled data, producing highly accurate results with low false-positive rates [17]. Although numerous studies have advanced deep learning for malware detection using API calls, the benefits of Generative Adversarial Networks (GANs) in this area remain largely unexplored.