Malicious PDF documents present a serious threat to various security
organizations that require modern threat intelligence platforms to effectively
analyze and characterize the identity and behavior of PDF malware.
State-of-the-art approaches use machine learning (ML) to learn features that
characterize PDF malware. However, ML models are often susceptible to evasion
attacks, in which an adversary obfuscates the malware code to avoid being
detected by an Antivirus. In this paper, we derive a simple yet effective
holistic approach to PDF malware detection that leverages signal and
statistical analysis of malware binaries. This includes combining orthogonal
feature space models from various static and dynamic malware detection methods
to enable generalized robustness when faced with code obfuscations. Using a
dataset of nearly 30,000 PDF files containing both malware and benign samples,
we show that our holistic approach maintains a high detection rate (99.92%) of
PDF malware and even detects new malicious files created by simple methods that
remove the obfuscation conducted by malware authors to hide their malware,
which are undetected by most antiviruses.

本文提出一种全面且简单的基于信号和统计分析的方法，以检测 PDF 恶意软件，采用分别从不同静态和动态恶意软件检测方法中选择的正交特征空间模型进行结合，以实现检测恶意代码混淆的鲁棒性。使用近 3 万个 PDF 文件数据集，我们证明该方法保持高检测率（99.92％），甚至检测到由恶意软件作者进行的混淆的新的恶意文件，并且是大多数防病毒软件无法检测到的。

HAPSSA: 使用信号和统计分析的全面 PDF 恶意软件检测方法

HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and  Statistical Analysis

Although state-of-the-art PDF malware classifiers can be trained with almost
perfect test accuracy (99%) and extremely low false positive rate (under 0.1%),
it has been shown that even a simple adversary can evade them. A practically
useful malware classifier must be robust against evasion attacks. However,
achieving such robustness is an extremely challenging task.
In this paper, we take the first steps towards training robust PDF malware
classifiers with verifiable robustness properties. For instance, a robustness
property can enforce that no matter how many pages from benign documents are
inserted into a PDF malware, the classifier must still classify it as
malicious. We demonstrate how the worst-case behavior of a malware classifier
with respect to specific robustness properties can be formally verified.
Furthermore, we find that training classifiers that satisfy formally verified
robustness properties can increase the evasion cost of unbounded (i.e., not
bounded by the robustness properties) attackers by eliminating simple evasion
attacks.
Specifically, we propose a new distance metric that operates on the PDF tree
structure and specify two classes of robustness properties including subtree
insertions and deletions. We utilize state-of-the-art verifiably robust
training method to build robust PDF malware classifiers. Our results show that,
we can achieve 92.27% average verified robust accuracy over three properties,
while maintaining 99.74% accuracy and 0.56% false positive rate. With simple
robustness properties, our robust model maintains 7% higher robust accuracy
than all the baseline models against unrestricted whitebox attacks. Moreover,
the state-of-the-art and new adaptive evolutionary attackers need up to 10
times larger $L_0$ feature distance and 21 times more PDF basic mutations
(e.g., inserting and deleting objects) to evade our robust model than the
baselines.

本文介绍了一种对 PDF 恶意软件进行训练的方法，通过使用新的距离度量和可验证的稳健性属性来训练分类器，并论证了其有效性和可靠性。