Using fake text vectors to improve the sensitivity of minority class for macro malware detection. (October 2020)

• Record Type: Journal Article
• Authors: Mimura, Mamoru
• Is Part Of: Journal of information security and applications. Volume 54(2020)
• Publication Date: 2020-10
• Subjects: Machine learning -- Data augmentation -- VBA macro -- NLP -- Paragraph Vector -- Doc2vec -- SVM -- Random Forests -- MLP -- CNN; Computer security -- Periodicals; Information technology -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.jisa.2020.102600
• Languages: English
• ISSNs: 2214-2126

Abstract

Abstract: To detect new malware, machine learning approaches require many training samples. These training samples contribute to build an accurate model. To maintain the accuracy, collecting comprehensive samples continuously is very important. However, new malicious samples appear one after another, and thereby making it difficult. Hence, actual small training samples do not likely to represent the entire population adequately. Despite this gap between ideal and reality, few studies have addressed this practical problem in macro malware. To enhance small training samples, data augmentation is efficient in the field of image recognition. Data augmentation with Generative Adversarial Networks (GANs) is a reasonable approach for oversampling the minority class. A major difficulty of GANs is to generate fake samples that represent the context. This paper attempts to generate fake text vectors with Paragraph Vector to enhance small training samples. Paragraph Vector is a model to convert text into vectors, which represents the context and numerical distance. These features allow to directly vary each element of the vectors. Our method adds random noise to the vectors to generate fake text vectors which represent the context. This paper applies this technique to detect new malicious VBA (Visual Basic for Applications) macros to address the practical problem. This generic technique could be used for not only malware detection, but also any imbalanced and contextual data. To simulate small training samples, we reduce the malicious samples, and generate fake samples from the reduced ones. The experimental result shows that the fake samples enhance our model, and improve the detection rate.