Paper Title
Image-Based Malware Detection Using Deep Features of CNN Architecture

Malware instances have been widely employed for illegitimate purposes and new types of malware are discovered every day. Machine learning in security is one of the most important fields of research nowadays due to its performance and huge progress over the previous decade. In this research, we introduce a streamlined yet comprehensive malware detection framework fusing deep learning with conventional machine learning methodologies. Pre-trained Convolutional Neural Networks (CNNs), including Visual Geometry Group (VGG), Inception, and ResNet, are used to extract deep features from malware images. This feature extraction forms the basis for subsequent classification using a suite of machine learning models, including Decision Trees, Logistic Regression, Random Forest, and XGBoost. These models, chosen for their diverse strengths and proven classification efficacy, undergo rigorous testing to evaluate their performance. Further enhancing our approach, we incorporate advanced deep learning techniques, notably Long Short-Term Memory (LSTM) networks and additional CNN architectures, to delve deeper into the malware's sequential patterns and behavioral anomalies. This not only elevates detection accuracy but also ensures adaptability in the face of evolving cyber threats. Our manuscript outlines the development, implementation, and evaluation of these methods, positioning our approach as a significant advancement in the field of cybersecurity and malware detection. Keywords - CNN, Deep Learning, LSTM, Machine Learning, Malware Detection, Malware image.