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The growth of Smartphones has bridged the telephony/SMS and the IP worlds, and this has resulted in new opportunities for financially motivated attackers. For example, some malicious campaigns in the cellular network aimed at extracting money fraudulently can do so even without any malware. Detecting and mitigating the variety of attacks in cellular network is difficult because they do not necessarily have a fixed `signature', and new types of campaigns appear frequently. Further complicating matters, detecting a single malicious entity (a domain name, a phone number, or a short code) that is part of a malicious campaign, is usually not very effective, because the attacker simply moves to using another entity in its place. An effective strategy requires detecting all/most elements involved in the campaign at once. In this paper, we describe a system, based on ideas from anomaly detection and clustering, that aims to detect many different families of widespread malicious campaigns in cellular networks. The system reveals an entire campaign as a graph cluster which includes the various entities involved in the campaign and their relationship, such as malware download websites, C&C servers, spammers, etc. Using logs from both SMS and IP portions of the network for millions of users, we detect newly popular entities and cluster them to discover how they are related. By looking for cues of possible malicious behavior from any of the entities in a cluster, we attempt to ascertain whether a detected campaign might be malicious, providing valuable leads to a human analyst. Our system is live and generates daily clusters to human analysts. We provide detailed case studies of real, previously unseen families of malicious campaigns that this system has successfully brought to light.
Author(s):
Nathaniel Boggs
Columbia University
United States
Wei Wang
AT&T
United States
Suhas Mathur
AT&T
United States
Baris Coskun
AT&T
United States
Carol Pincock
AT&T
United States