Command and control (C&C) is important in an attack. It transfers commands
from the attacker to the malware in the compromised hosts. Currently, some
attackers use online social networks (OSNs) in C&C tasks. There are two main
problems in the C&C on OSNs. First, the process for the malware to find the
attacker is reversible. If the malware sample is analyzed by the defender, the
attacker would be exposed before publishing the commands. Second, the commands
in plain or encrypted form are regarded as abnormal contents by OSNs, which
would raise anomalies and trigger restrictions on the attacker. The defender
can limit the attacker once it is exposed. In this work, we propose DeepC2, an
AI-powered C&C on OSNs, to solve these problems. For the reversible
hard-coding, the malware finds the attacker using a neural network model. The
attacker's avatars are converted into a batch of feature vectors, and the
defender cannot recover the avatars in advance using the model and the feature
vectors. To solve the abnormal contents on OSNs, hash collision and text data
augmentation are used to embed commands into normal contents. The experiment on
Twitter shows that command-embedded tweets can be generated efficiently. The
malware can find the attacker covertly on OSNs. Security analysis shows it is
hard to recover the attacker's identifiers in advance.

本文提出了一种基于人工智能技术的 C&C 攻击方式 DeepC2，用于解决攻击者在在线社交网络上的两个主要问题：如何在不被防御者发现的情况下找到攻击者，以及如何在信息中隐藏命令，从而避免被社交网络检测到。实验表明，该方法可以让恶意软件在不被发现的情况下与攻击者进行通信。