Abnormal Files Classification using Convolutional Neural Network CNN (American Journal of Engineering , Mechanics and Architecture (2993-2637), 2024)
As of late, turning Windows files into pictures and analyzing them with deep learning and machine learning have been regarded as state-of-the-art methods for identifying and classifying malware. This is mostly because deep learning model performance in image classification has recently experienced a boom in success, and image-based malware detection and classification is platform independent. Convolutional neural network (CNN) deep learning techniques are successfully used for image-based Windows malware classification, according to a review of the literature. Nevertheless, just a small percentage of the entire picture representation had the infection. Finding and identifying these impacted little areas is crucial to achieving a high degree of malware classification accuracy. This study locates and identifies the little contaminated spots in the overall picture by integrating a Data augmentation technique with a CNN. On a dataset of malware images, a thorough examination and analysis of the suggested technique were conducted. The effectiveness of the suggested Data augmentation-based CNN method was evaluated against many non-attention-CNN-based methods using different data splits from the benchmark malware picture training and testing dataset. The proposed CNN approach ensured computational efficiency and outperformed non-attention-based CNN methods in all the data-splits. Most notably, the majority of the techniques demonstrate consistent performance across all training and testing data splits, illuminating CNN's multi-headed attention. model’s generalizability to perform on the diverse datasets.