## 2 Related Studies  
Recent research explores innovative methodologies to overcome the above-mentioned challenges. Sequence-based methods for malware detection use DL models to extract sequential features from API call sequences. API-MalDetect is a framework that utilizes API call sequences for malware detection on Windows systems [13]. The framework combines a CNN and BiGRU-based hybrid feature extractor with NLP-inspired encoding to understand semantic relationships in API calls. API-MalDetect achieves high accuracy and robustness against evolving malware variants, outperforming state-of-the-art methods in various evaluation metrics. Another paper [17] presents a DL framework that detects malware through intrinsic features derived from API sequences, such as software behavior, semantic information, and relationships between API calls. By employing Bi-LSTM and convolutional layers, the proposed model achieves improved accuracy and F1-scores over existing baselines. The study highlights the limitations of traditional methods, which often overlook the contextual semantics of API calls, and proposes a more comprehensive approach for dynamic malware detection. [PERSON] et al. [18] introduced a transfer and ensemble learning method using CNNs to classify malware in enterprise networks. Their approach effectively handles polymorphic malware with an impressive detection accuracy of 0.9936 on benchmark datasets by visualizing malware as grayscale images. Similarly, [PERSON] and [PERSON] [1] used a CNN-LSTM hybrid model to classify malware based on code sequences and API calls, demonstrating the effectiveness of combining sequential and convolutional approaches for high accuracy. [PERSON] et al. [19] introduced a novel DL-basedframework utilizing feature engineering techniques to reduce feature space significantly and achieved a 0.9601 detection accuracy using the Microsoft malware dataset. [PERSON] et al. [20] explored the potential of Automated Machine Learning (AutoML) for optimizing DL architectures, demonstrating superior performance in static and online malware detection compared to manually crafted models. Recent work in malware detection highlights the importance of integrating advanced analysis techniques and Graph Neural Networks (GNNs) against evolving threats. [PERSON] et al. [21] proposed a multimodal Windows malware detection method combining static features from PE headers and dynamic Control Flow Graphs (CFG) using a hybrid analysis and DL framework, achieving 0.9925 accuracy. [PERSON] et al. [22] introduced a semantics-preserving reinforcement learning attack targeting GNN-based malware detection system, highlighting vulnerabilities in existing defenses while preserving malware functionality through semantic manipulation of CFGs. [PERSON] et al. [23] explored the use of Graph Convolutional Networks (GCNs) and GATs for ransomware detection, utilizing API call graphs extracted from Cuckoo Sandbox reports, demonstrating the effectiveness of GCNs over GATs in analyzing structural and relational data in malware detection. These studies underline the critical role of GNNs and multimodal approaches in enhancing the accuracy and resilience of modern malware detection systems. [PERSON] et al. proposed a multimodal approach combining structural and semantic features using Class Set Call Graph (CSCG) and GAT. They achieved state-of-the-art accuracy in Android malware detection by combining graph-based and permission features [24]. In another study, [PERSON] et al. [2] presented a graph neural network-based framework called DawnGNN for Windows malware detection that utilize semantic embeddings derived from API documents to improve detection performance. Extending API behavior analysis, [PERSON] et al. proposed the MINES framework, which combines graph contrastive learning and CNNs to extract and fuse API presence and transition features, significantly improving malware classification [25].