DACTI: a Generation-Based Data Augmentation Method for Cyber Threat Intelligence
Jingbing Chen, Yali Gao, Xiaoyong Li · 2023
Methods of data augmentation are extensively utilized to boost the efficacy of machine learning, given their superior capability in promoting generalization performance. In this paper, we address the issue of data scarcity in the field of cyber threat intelligence and propose a method for generating high-quality synthetic data by training a language model on linearized labeled phrases. Subsequently, we design a relational classification model to classify relational entities related to cyber threat intelligence in the created synthetic data. We conduct extensive experiments on datasets of different sizes, and the experimental results show that our proposed method outperforms the baseline methods, especially when the amount of gold data provided is insufficient, and the$F_{1}$score of our model is improved by up to 1.94 points.