A deep learning based technique for the classification of malware images

摘要: Because of the fast expansion of the internet and technology, a slew of developing malware and attack techniques has evolved. As a result, researchers concentrated their efforts on machine learning and deep learning techniques to detect malware. Many organizations have been developing new algorithms and products to secure people from these scams. On the other hand, Malware kinds have been expanding substantially in recent years. The anti-virus companies have been discovering millions of new malware variants every year. Therefore, new intelligent malware detection methods must be solved as soon as possible to halt this rise. Malware is becoming more prevalent, more diverse, and more sophisticated. Deep learning in malware detection through images has recently been demonstrated to be highly effective. We also employed an Image-based Malware dataset [Malimg] and used the different deep learning algorithms, CNN, Caps-Net, VGG16, ResNet, and InceptionV3, for malware detection. The dataset images were transported through the pre-processing pipeline and into the deep learning pipeline, where they were used to train deep learning models in the right way. As part of the model training process, all images were resized to be the same size and proportions. A factor of 1/255 was then applied to the images, resulting in a conversion from RGB value to grayscale, which restored the original RGB values to their correct positions. Later, the dataset was segmented into two groups, train, and test. The VGG16, ResNet50, and InceptionV3 models detected the malware images. A combination of the Adam optimizer and the cross …