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DeepC2: AI-Powered Covert Command and Control on OSNs

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Information and Communications Security (ICICS 2022)

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Abstract

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.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61902396), the Youth Innovation Promotion Association CAS (No. 2019163), the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDC02040100), the Key Laboratory of Network Assessment Technology at Chinese Academy of Sciences, and Beijing Key Laboratory of Network Security and Protection Technology.

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Correspondence to Chaoge Liu or Xiang Cui .

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Appendices

A Easy Data Augmentation

Table 1 is the example of EDA with an original sentence from Twitter. Bold words represent parts that have changed from the original sentence. The newly generated sentences are not grammatically correct. Because there are many grammatically incorrect sentences on the Internet, sentences generated using EDA can also be accepted by Internet users and confused with normal content.

Table 1. Sentences generated by EDA
Fig. 11.
figure 11

Threshold for distance

B Threshold for Distance

A threshold is needed to determine whether two avatars share the same source. We use a trained model to calculate the distances on the validation set, which contains 115,887 pairs with label 0 and 347,657 pairs with label 1. We record the distances of every comparison, sort them by value and label, and count their frequencies to learn the boundary between the “same” avatars and different avatars. As shown in Fig. 11, the distances of all pairs with label 1 and only four pairs with label 0 are larger than 0.02, and the remaining pairs with label 0 are less than 0.02. It shows that 0.02 is a proper threshold for the determination. In real scenarios, attackers can choose a threshold less than 0.02, as the undistributed avatars and distances are within the authority of attackers.

C Enhancement

As proof of concept, the parameters in this work are conservative. There are ways to enhance the security of DeepC2.

In the model’s design, the vectors can be longer than 128, making analysis and collisions for avatars even more difficult. The threshold of distances can also be lower than 0.02, as the undistributed avatars and the distances are within the authority of attackers. They can balance efficiency and accuracy according to the needs. Additionally, more losses can be introduced during the processing of avatars, like compression, deformation, format conversion, etc., making it harder to recover the avatars.

For addressing, the attacker can select more topics. Attackers can publish commands on the topics, and the malware can choose one randomly to find attackers. Attackers can also use other fields in OSNs to convey customized content. For instance, attackers could comment on a tweet, and the malware would identify and obtain commands from attackers’ profiles. Other platforms, like Weibo and Tumblr, can also be utilized.

As stated before, attackers should maintain some accounts to publish different commands. To reduce the specious behaviors of accounts, attackers can maintain them by imitating normal users or social bots [7]. This work can be done manually or automatically [24]. When attackers need to publish a command, attackers can select one account and maintain other accounts as usual.

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Wang, Z. et al. (2022). DeepC2: AI-Powered Covert Command and Control on OSNs. In: Alcaraz, C., Chen, L., Li, S., Samarati, P. (eds) Information and Communications Security. ICICS 2022. Lecture Notes in Computer Science, vol 13407. Springer, Cham. https://doi.org/10.1007/978-3-031-15777-6_22

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  • DOI: https://doi.org/10.1007/978-3-031-15777-6_22

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