## 2 Materials and Methods
### Related Work  
In related work, [PERSON] et al. [2] proposed a hybrid machine and deep learning model for malware detection from API call sequences. It uses logistic regression and RNN models on a large dataset (API-Call-Sequences) of over 40,000 malicious and 1000 benign API call sequences. The hybrid model achieved 84% accuracy on the balanced dataset and 98% on the imbalanced dataset, outperforming baselines. However, computational requirements, including training and testing time and the memory usage of the models, were not discussed. Similarly, in [4], the authors used machine learning and deep learning algorithms for malware detection and classification, comparing their performance using datasets like API-Call-Sequences and APIMDS. The system employs a two-step approach: a Malware Detection algorithm identifies malware and a Malware Classification algorithm categorizes it based on API call sequences. Algorithms tested included Random Forest, CatBoost, XGBoost, ExtraTrees, TabNet, Bi-LSTM, and BiGRU. The BiGRU algorithm showed superior performance in Recall, AUC-ROC, F1 score, and accuracy for malware detection, while the ExtraTrees algorithm excelled in malware classification. BiGRU was particularly effective in detecting malicious files, whereas ExtraTrees better discriminated between malware types. [PERSON] et al. [6] introduced the VirusShare and VirusSample datasets for benchmarking malware classification models using static and dynamic analysis to extract API calls. They preprocess the data by removing families with fewer than 100 samples and balancing it with an upper limit of 300 samples per family. The study benchmarks models like SVM, XGBoost, and CANINE, comparing performance on imbalanced and balanced datasets. While providing insights, the datasets may lack diversity, impacting generalizability. The paper also does not detail the computational resources, such as the training and testing time of the models. In [11], a behavioral malware detection method using Deep Graph Convolutional Neural Networks (DGCNNs) is proposed, involving an eight-step process. The approach analyzes Portable Executable (PE) files in a Cuckoo Sandbox, extracts API call sequences, and creates behavioral graphs for binary classification. Using the API-Call-Sequences dataset, the study finds that LSTM outperforms two DGCNN models in detecting malware. While the AUC metric highlights the models' effectiveness, PCA visualization shows distinct classification behaviors among different architectures. The study notes a need for additional regularization techniques to address observed overfitting after ten epochs. Authors in [14] employed Long Short-Term Memory (LSTM) for malware classification by analyzing Windows API call sequences. They used the Mal-API-2019 dataset of 7107 malware samples. Treating malware detection as a text classification problem, their LSTM-based model achieved an accuracy between 83.5% and 98.5%, with an overall accuracy of up to 98.50%. The study reported minimal model complexity, and the method was robust against class imbalance. However, the model recorded a relatively high training time with no testing time and memory usage recorded. Further, [PERSON] et al. [29] proposed API-MalDetect, a deep learning-based framework for detecting malware in Windows systems. It employs an NLP-based encoder for API calls and a hybrid feature extractor combining CNNs and BiGRU to process API call sequences. API-MalDetect aims to detect new malware attacks and reduce bias during training and testing. The authors reveal that experimental results outperform existing techniques in accuracy, precision, recall, F1 score, and AUC on benchmark datasets. The framework also highlights key API calls for malware identification. Nevertheless, the memory usage of the models on the three datasets tested was not presented. In [30], the authors developed a malware detection system by employing Bidirectional Long Short-Term Memory (BiLSTM) to analyze API calls.