僵尸网络 DGA 检测规避 (AML.CS0001)

一句话通俗理解

用「普通域名生成器」生成的域名骗过基于 CNN 的 DGA 检测器——恶意域名看起来毫无恶意特征。

维度	内容
案例类型	演练 / 安全研究
发生时间	2020-01-01
攻击者	Palo Alto Networks AI Research Team
受害目标	Palo Alto Networks ML-based DGA detection module

Palo Alto Networks 安全 AI 研究团队使用通用域名生成算法生成域名，成功绕过了基于卷积神经网络的僵尸网络 DGA 检测器。攻击表明恶意域名可以伪装成正常域名特征逃避检测。

以下攻击链映射来自 MITRE ATLAS 官方数据（AML.CS0001 的 employs 关系），按官方步骤编号排序；技术名称链接至本库对应技术文档，引用文字为官方对本次运用的描述。

搜索开放技术数据库 (AML.T0000)

DGA detection is a widely used technique to detect botnets in academia and industry. The research team searched for research papers related to DGA detection.

获取公开 AI 制品 (AML.T0002)

The researchers acquired a publicly available CNN-based DGA detection model and tested it against a well-known DGA generated domain name data sets, which includes ~50 million domain names from 64 botnet DGA families. The CNN-based DGA detection model shows more than 70% detection accuracy on 16 (~25%) botnet DGA families.

对抗性 AI 攻击 (AML.T0017.000)

The researchers developed a generic mutation technique that requires a minimal number of iterations.

黑盒优化 (AML.T0043.001)

The researchers used the mutation technique to generate evasive domain names.

验证攻击 (AML.T0042)

The experiment results show that the detection rate of all 16 botnet DGA families drop to less than 25% after only one string is inserted once to the DGA generated domain names.

规避 AI 模型 (AML.T0015)

The DGA generated domain names mutated with this technique successfully evade the target DGA Detection model, allowing an adversary to continue communication with their Command and Control servers.

以下为 MITRE ATLAS 官方案例收录的参考来源：

版本: ATLAS 2026.09 | 更新: 2026-10-24 | 模板: ATLAS 案例研究 v1.0