Malware Image Classification with Enhanced Feature Extraction Using VGG16 and Spatial Pyramid Pooling

In the present era, the rise in number and complexity of malware poses a major threat leading to the rise in the importance of smarter Malware detection systems to understand and detect modern malwares. Modern systems are often very slow and inefficient with outdated machine learning models and feature selection techniques. These inefficiencies are overcome by the use of a multiclass detection system with CNN application to automatically classify malware. The system represents malware binaries in image format and uses VGG16 based network architecture with Spatial Pyramid Pooling (SPP) and ReLU activation to efficiently retrieve multiscale features from images of variable dimensionality. The model also includes dropout regularization, categorical cross entropy and Adam optimizer for better performance, adaptability and generalization. This approach works with a well balanced dataset with extensive training and achieved an accuracy of 95.94% making it a reliable and scalable solution for real time threat detection in a dynamic environment.