Malware Analysis and Detection Using Deep Learning

Abstract: Malware detection is still a major problem in the rapidly changing field of cybersecurity, requiring creative solutions. The creation and assessment of a specialized Convolutional Neural Network (CNN) intended for malware identification is the main goal of this research. The study trains and evaluates the effectiveness of the custom model using samples of malware from 15 different families included in the Malimg dataset. To benchmark the results, a comparison study is carried out using the wellestablished VGG16 model. Intricate features are extracted from the malware samples by the bespoke CNN model, improving the malware samples' detection and classification abilities. Performance measures including F1-score, recall, accuracy, and precision are employed in evaluation. The results show that the custom CNN model performs better than the VGG16 model in important metrics, indicating improved computational efficiency and accuracy. This suggests that customized CNN architectures can greatly enhance malware detection performance. This study concludes by highlighting the effectiveness of customized CNN models in improving malware detection and offering insightful information for further cybersecurity research. The findings demonstrate how sophisticated deep learning techniques may be used to create malware detection systems that are more reliable and effective.