Advanced Malware Classification Using Convolutional Neural Networks and Image Transformation Techniques

Malware seriously compromises cybersecurity and calls for sophisticated and effective categorization methods to find and lessen its effects. This research article offers a complete method based on image processing and deep learning to classify malware. ResNet18, VGG16, VGG19, AlexNet, and our custom-designed MalwareNet are among the several deep learning models we use to categorize malware photos from the nine different classes in the Big 2015 dataset-which shows a clear class imbalance. Using hyperband and bayesian optimization approaches, our strategy maximizes hyperparameters to provide the greatest performance. The hyperparameters for ResNet18, VGG16, VGG19, and AlexNet models comprise learning rate, kernel size, number of epochs, dropout rate, and dense layer size as well as particular parameters for MalwareNet including the number of neurons in each layer and the fully connected layer size. With MalwareNet obtaining especially remarkable results, the examination of our models shows great accuracy, precision, recall, and F1 scores. We examine the training accuracy, F1 score, and log loss measures in great detail; we then show the classification performance over all classes using confusion matrices. With notable increases in malware detection and categorization, the suggested model offers a strong and practical answer for uses in cybersecurity. Supported by diligent hyperparameter adjustment and performance evaluation, this work expands the field by providing a comprehensive comparison of many deep learning architectures and how effectively they classify malware. The results show how well deep learning models might improve malware detection systems and protect digital environments against hostile attacks.