Security classifiers, designed to detect malicious content in computer
systems and communications, can underperform when provided with insufficient
training data. In the security domain, it is often easy to find samples of the
negative (benign) class, and challenging to find enough samples of the positive
(malicious) class to train an effective classifier. This study evaluates the
application of natural language text generators to fill this data gap in
multiple security-related text classification tasks. We describe a variety of
previously-unexamined language-model fine-tuning approaches for this purpose
and consider in particular the impact of disproportionate class-imbalances in
the training set. Across our evaluation using three state-of-the-art
classifiers designed for offensive language detection, review fraud detection,
and SMS spam detection, we find that models trained with GPT-3 data
augmentation strategies outperform both models trained without augmentation and
models trained using basic data augmentation strategies already in common
usage. In particular, we find substantial benefits for GPT-3 data augmentation
strategies in situations with severe limitations on known positive-class
samples.

本研究评估了自然语言文本生成器在多个与安全相关的文本分类任务中填补不足的数据差距方面的应用，并考虑了训练集中不均衡类别的影响。通过使用三种针对冒犯性语言检测、评论欺诈检测和短信垃圾检测的最新分类器进行评估，我们发现使用 GPT-3 数据增强策略训练的模型优于未使用增强和使用常见数据增强策略训练的模型；特别是在已知阳性样本严重不足的情况下，GPT-3 数据增强策略带来了显著的好处。

安全分类任务中的数据集扩增文本生成

Text generation for dataset augmentation in security classification  tasks

Many recent works have proposed methods to train classifiers with local
robustness properties, which can provably eliminate classes of evasion attacks
for most inputs, but not all inputs. Since data distribution shift is very
common in security applications, e.g., often observed for malware detection,
local robustness cannot guarantee that the property holds for unseen inputs at
the time of deploying the classifier. Therefore, it is more desirable to
enforce global robustness properties that hold for all inputs, which is
strictly stronger than local robustness.
In this paper, we present a framework and tools for training classifiers that
satisfy global robustness properties. We define new notions of global
robustness that are more suitable for security classifiers. We design a novel
booster-fixer training framework to enforce global robustness properties. We
structure our classifier as an ensemble of logic rules and design a new
verifier to verify the properties. In our training algorithm, the booster
increases the classifier's capacity, and the fixer enforces verified global
robustness properties following counterexample guided inductive synthesis.
We show that we can train classifiers to satisfy different global robustness
properties for three security datasets, and even multiple properties at the
same time, with modest impact on the classifier's performance. For example, we
train a Twitter spam account classifier to satisfy five global robustness
properties, with 5.4% decrease in true positive rate, and 0.1% increase in
false positive rate, compared to a baseline XGBoost model that doesn't satisfy
any property.

本文提出了一个框架和工具来训练满足全局鲁棒性的分类器，并通过逐步引导综合来强制验证全局鲁棒性属性，同时结构分类器作为逻辑规则的集合，并在三个安全数据集上展示了在对分类器性能的影响较小的情况下，训练分类器满足不同的全局鲁棒性属性。

使用验证的全局鲁棒性属性学习安全分类器

Learning Security Classifiers with Verified Global Robustness Properties

There are various costs for attackers to manipulate the features of security
classifiers. The costs are asymmetric across features and to the directions of
changes, which cannot be precisely captured by existing cost models based on
$L_p$-norm robustness. In this paper, we utilize such domain knowledge to
increase the attack cost of evading classifiers, specifically, tree ensemble
models that are widely used by security tasks. We propose a new cost modeling
method to capture the feature manipulation cost as constraint, and then we
integrate the cost-driven constraint into the node construction process to
train robust tree ensembles. During the training process, we use the constraint
to find data points that are likely to be perturbed given the feature
manipulation cost, and we use a new robust training algorithm to optimize the
quality of the trees. Our cost-aware training method can be applied to
different types of tree ensembles, including gradient boosted decision trees
and random forest models. Using Twitter spam detection as the case study, our
evaluation results show that we can increase the attack cost by 10.6X compared
to the baseline. Moreover, our robust training method using cost-driven
constraint can achieve higher accuracy, lower false positive rate, and stronger
cost-aware robustness than the state-of-the-art training method using
$L_\infty$-norm cost model. Our code is available at
this https URL

该论文提出了一种基于成本驱动的约束来训练鲁棒性决策树模型的成本建模方法，在 Twitter 垃圾邮件检测等场景中显示出比现有方法更高的准确性、更低的误报率和更强的成本感知鲁棒性。