Machine learning (ML)-based malware detection systems are becoming
increasingly important as malware threats increase and get more sophisticated.
PDF files are often used as vectors for phishing attacks because they are
widely regarded as trustworthy data resources, and are accessible across
different platforms. Therefore, researchers have developed many different PDF
malware detection methods. Performance in detecting PDF malware is greatly
influenced by feature selection. In this research, we propose a small features
set that don't require too much domain knowledge of the PDF file. We evaluate
proposed features with six different machine learning models. We report the
best accuracy of 99.75% when using Random Forest model. Our proposed feature
set, which consists of just 12 features, is one of the most conciseness in the
field of PDF malware detection. Despite its modest size, we obtain comparable
results to state-of-the-art that employ a much larger set of features.

机器学习在恶意软件检测系统中的应用日益重要，本研究提出了一种只使用 12 个特征的小特征集，通过六种不同的机器学习模型评估表明，在 PDF 恶意软件检测领域中具有最简洁的特征集之一，并能获得与大量特征集相当的准确性。

PDF 恶意软件检测的小型功能集

A Feature Set of Small Size for the PDF Malware Detection

In recent years, the field of document understanding has progressed a lot. A
significant part of this progress has been possible thanks to the use of
language models pretrained on large amounts of documents. However, pretraining
corpora used in the domain of document understanding are single domain,
monolingual, or nonpublic. Our goal in this paper is to propose an efficient
pipeline for creating a big-scale, diverse, multilingual corpus of PDF files
from all over the Internet using Common Crawl, as PDF files are the most
canonical types of documents as considered in document understanding. We
analysed extensively all of the steps of the pipeline and proposed a solution
which is a trade-off between data quality and processing time. We also share a
CCpdf corpus in a form or an index of PDF files along with a script for
downloading them, which produces a collection useful for language model
pretraining. The dataset and tools published with this paper offer researchers
the opportunity to develop even better multilingual language models.

提出一种高效管道，通过使用 Common Crawl 在因特网上创建大规模、多语种的 PDF 文件语料库，有助于语言模型的预训练。数据集和工具为研究者提供了开发更好的多语言语言模型的机会。

从 Web 爬虫数据构建高质量视觉丰富文档的语料库

CCpdf: Building a High Quality Corpus for Visually Rich Documents from  Web Crawl Data

The task of table structure recognition aims to recognize the internal
structure of a table, which is a key step to make machines understand tables.
Currently, there are lots of studies on this task for different file formats
such as ASCII text and HTML. It also attracts lots of attention to recognize
the table structures in PDF files. However, it is hard for the existing methods
to accurately recognize the structure of complicated tables in PDF files. The
complicated tables contain spanning cells which occupy at least two columns or
rows. To address the issue, we propose a novel graph neural network for
recognizing the table structure in PDF files, named GraphTSR. Specifically, it
takes table cells as input, and then recognizes the table structures by
predicting relations among cells. Moreover, to evaluate the task better, we
construct a large-scale table structure recognition dataset from scientific
papers, named SciTSR, which contains 15,000 tables from PDF files and their
corresponding structure labels. Extensive experiments demonstrate that our
proposed model is highly effective for complicated tables and outperforms
state-of-the-art baselines over a benchmark dataset and our new constructed
dataset.

本论文提出了一种名为 GraphTSR 的新型图神经网络，用于在 PDF 文件中识别表的结构，其通过预测单元之间的关系来识别表结构，并构建了一个大规模的表结构识别数据集 SciTSR，该模型对于复杂的表具有很高效性，并超过了基准数据集和新构建的数据集中的现有模型。