AUTOPROBE

Zhaoyan Xu, Antonio Nappa, Robert Baykov, Guangliang Yang, Juan Caballero, Guofei Gu
2014 Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security - CCS '14  
Malware continues to be one of the major threats to Internet security. In the battle against cybercriminals, accurately identifying the underlying malicious server infrastructure (e.g., C&C servers for botnet command and control) is of vital importance. Most existing passive monitoring approaches cannot keep up with the highly dynamic, ever-evolving malware server infrastructure. As an effective complementary technique, active probing has recently attracted attention due to its high accuracy,
more » ... ficiency, and scalability (even to the Internet level). In this paper, we propose AUTOPROBE, a novel system to automatically generate effective and efficient fingerprints of remote malicious servers. AUTOPROBE addresses two fundamental limitations of existing active probing approaches: it supports pull-based C&C protocols, used by the majority of malware, and it generates fingerprints even in the common case when C&C servers are not alive during fingerprint generation. Using real-world malware samples we show that AUTOPROBE can successfully generate accurate C&C server fingerprints through novel applications of dynamic binary analysis techniques. By conducting Internet-scale active probing, we show that AUTOPROBE can successfully uncover hundreds of malicious servers on the Internet, many of them unknown to existing blacklists. We believe AUTOPROBE is a great complement to existing defenses, and can play a unique role in the battle against cybercriminals.
doi:10.1145/2660267.2660352 dblp:conf/ccs/XuNBYCG14 fatcat:pi4h5fk6kzb2pnaloosclwvxsu