  In the modern era, malware is experiencing a significant increase in both its
variety and quantity, aligning with the widespread adoption of the digital
world. This surge in malware has emerged as a critical challenge in the realm
of cybersecurity, prompting numerous research endeavors and contributions to
address the issue. Machine learning algorithms have been leveraged for malware
detection due to their ability to uncover concealed patterns within vast
datasets. However, deep learning algorithms, characterized by their
multi-layered structure, surpass the limitations of traditional machine
learning approaches. By employing deep learning techniques such as CNN
(Convolutional Neural Network) and RNN (Recurrent Neural Network), this study
aims to classify and identify malware extracted from a dataset containing API
call sequences. The performance of these algorithms is compared with that of
conventional machine learning methods, including SVM (Support Vector Machine),
RF (Random Forest), KNN (K-Nearest Neighbors), XGB (Extreme Gradient Boosting),
and GBC (Gradient Boosting Classifier), all using the same dataset. The
outcomes of this research demonstrate that both deep learning and machine
learning algorithms achieve remarkably high levels of accuracy, reaching up to
99% in certain cases.
