Today, the security of many domains rely on the use of Machine Learning to
detect threats, identify vulnerabilities, and safeguard systems from attacks.
Recently, transformer architectures have improved the state-of-the-art
performance on a wide range of tasks such as malware detection and network
intrusion detection. But, before abandoning current approaches to transformers,
it is crucial to understand their properties and implications on cybersecurity
applications. In this paper, we evaluate the robustness of transformers to
adversarial samples for system defenders (i.e., resiliency to adversarial
perturbations generated on different types of architectures) and their
adversarial strength for system attackers (i.e., transferability of adversarial
samples generated by transformers to other target models). To that effect, we
first fine-tune a set of pre-trained transformer, Convolutional Neural Network
(CNN), and hybrid (an ensemble of transformer and CNN) models to solve
different downstream image-based tasks. Then, we use an attack algorithm to
craft 19,367 adversarial examples on each model for each task. The
transferability of these adversarial examples is measured by evaluating each
set on other models to determine which models offer more adversarial strength,
and consequently, more robustness against these attacks. We find that the
adversarial examples crafted on transformers offer the highest transferability
rate (i.e., 25.7% higher than the average) onto other models. Similarly,
adversarial examples crafted on other models have the lowest rate of
transferability (i.e., 56.7% lower than the average) onto transformers. Our
work emphasizes the importance of studying transformer architectures for
attacking and defending models in security domains, and suggests using them as
the primary architecture in transfer attack settings.

评估了变压器对系统防御者的恶意样本的强健性以及对系统攻击者的对抗性样本的可迁移性，发现变压器所生成的对抗样本具有最高的可迁移率，对其他模型具有更强的对抗性，强调了研究在安全领域中使用变压器架构进行攻击和防御的重要性，并建议将其作为转移攻击设置中的主要架构。

基于 Transformer 的对抗攻击在安全领域中的有效性

The Efficacy of Transformer-based Adversarial Attacks in Security  Domains

Unsupervised Deep Learning (DL) techniques have been widely used in various
security-related anomaly detection applications, owing to the great promise of
being able to detect unforeseen threats and superior performance provided by
Deep Neural Networks (DNN). However, the lack of interpretability creates key
barriers to the adoption of DL models in practice. Unfortunately, existing
interpretation approaches are proposed for supervised learning models and/or
non-security domains, which are unadaptable for unsupervised DL models and fail
to satisfy special requirements in security domains.
In this paper, we propose DeepAID, a general framework aiming to (1)
interpret DL-based anomaly detection systems in security domains, and (2)
improve the practicality of these systems based on the interpretations. We
first propose a novel interpretation method for unsupervised DNNs by
formulating and solving well-designed optimization problems with special
constraints for security domains. Then, we provide several applications based
on our Interpreter as well as a model-based extension Distiller to improve
security systems by solving domain-specific problems. We apply DeepAID over
three types of security-related anomaly detection systems and extensively
evaluate our Interpreter with representative prior works. Experimental results
show that DeepAID can provide high-quality interpretations for unsupervised DL
models while meeting the special requirements of security domains. We also
provide several use cases to show that DeepAID can help security operators to
understand model decisions, diagnose system mistakes, give feedback to models,
and reduce false positives.

DeepAID 是一个用于解释无监督深度学习模型在安全领域异常检测系统中的通用框架，提供了一种新颖的解释方法，可以满足安全领域的特定要求，并提供了模型扩展来解决特定的问题。实验证明 DeepAID 可以为无监督深度学习模型提供高质量解释，同时有助于降低误报率。