KEYWORDS: Visualization, Machine learning, Data modeling, Visual process modeling, Image classification, Performance modeling, Education and training, Binary data, RGB color model
The use of fileless technologies in malware continues to grow and fileless malware becomes more dangerous and difficult to detect. To address this challenge, we propose a novel visual method for classifying fileless malware based on few-shot learning. First, we built a fileless malware dataset, which is executed through a local virtual environment to collect malware memory dumps. Secondly, memory dumps are clipped and visualized. We developed a new memory dumps trimming method and a novel binary file visualization technique, which can remove redundant data from memory dumps, significantly compress the file size, and then represent the trimmed memory dumps as RGB images. Finally, we propose a few-shot learning framework, namely MMEL (MAML + Mean_subtraction + Euclidean_normalization + Label_Smothing), to improve the performance of the classification method. Experimental results show that our visualization technique and framework outperform other state-of-the-art few-shot learning methods.
Face morphing attack has become a severe threat to the current face recognition systems. Though there are some methods for detecting face morphing, the performance of these methods is susceptible to noise. Aiming to enhance the performance of resisting noise in face morphing detection, a noise robust convolutional neural network is proposed in this paper. The structure of the network is divided into two parts: facial image adaptive denoising and face morphing detection. Before the face morphing detection, the auto-encoders are first utilized to adaptively denoise the noised facial images, which can effectively reduce the influence of noise on face morphing detection. Then, the pre-trained VGG19 convolution neural network with powerful classification ability is used for face morphing detection with the generated noise-free facial images. Experimental results indicate that the proposed method can effectively reduce the noise influence on face morphing detection, and can achieve better performance compared with some existing methods.
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