  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.
