DATA AUGMENTATION AND TRANSFER LEARNING TO CLASSIFY MALWARE IMAGES IN A DEEP LEARNING CONTEXT

Abstract:

Malware detection and classification are critical tasks in the field of cybersecurity. With the growing sophistication of malware attacks, there is a need for robust and accurate methods to identify and classify malware samples. In recent years, deep learning techniques have proven to be highly effective in various image classification tasks. This study focuses on the application of data augmentation and transfer learning techniques to classify malware images in a deep learning context.

Data augmentation is a technique used to artificially increase the size of the training dataset by applying various transformations to the existing data. This approach helps to reduce overfitting and improve the generalization ability of the deep learning models. In the context of malware image classification, data augmentation can be used to generate additional samples by applying transformations such as rotation, scaling, flipping, and adding noise to the original images.

Transfer learning is another powerful technique that leverages pre-trained models on large image datasets to solve similar tasks with limited labeled data. In the case of malware image classification, transfer learning allows us to utilize the knowledge learned from large-scale image datasets, such as ImageNet, to improve the performance of the model in classifying malware samples.

This research proposes a deep learning framework for malware image classification that combines data augmentation and transfer learning. The proposed framework consists of a deep convolutional neural network (CNN) architecture, pre-trained on a large-scale image dataset, and fine-tuned on a labeled malware image dataset. Data augmentation techniques are applied during the training process to create a diverse and augmented training dataset.

Experimental evaluations are conducted on a benchmark dataset of malware images to assess the effectiveness of the proposed approach. The results demonstrate that the combination of data augmentation and transfer learning significantly improves the classification accuracy compared to using deep learning models without these techniques. The proposed framework achieves state-of-the-art performance in malware image classification, highlighting the potential of data augmentation and transfer learning in the field of cybersecurity.

In conclusion, this study presents a comprehensive approach for malware image classification using data augmentation and transfer learning in a deep learning context. The results indicate that these techniques can enhance the performance of deep learning models and enable accurate identification and classification of malware samples. The findings of this research contribute to the advancement of malware detection and provide insights for developing more robust cybersecurity systems.