HIMALAYAS: Hierarchical Machine Learning Stack for Fine-Grained Analysis of Malware Domain Groups

Team: Vinod Yegneswaran , Shalini Ghosh (SRI International), Arindam Banerjee (University of Minnesota), Guofei Gu (Texas A&M University)

The domain name system (DNS) protocol plays a significant role in operation of the Internet by enabling the bi-directional association of domain names with IP addresses. It is also increasingly abused by malware, particularly botnets, by use of: (1) automated domain generation algorithms for rendezvous with a command-and-control (C&C) server, (2) DNS fast flux as a way to hide the location of malicious servers, and (3) DNS as a carrier channel for C&C communications. This project explores the development of a scalable, hierarchical machine-learning stack, called HIMALAYAS, which specializes in algorithms for automatically mining DNS data for malware activity. In particular, we are interested in isolating both ordered and unordered sets of malware domain groups whose access patterns are temporally and logically correlated.

HIMALAYAS performs a task of increasing complexity at each level - starting from scalable clustering and feature selection at lower levels, to more advanced malware domain subsequence identification algorithms at higher levels. It has multiple benefits, including speed, accuracy, interpretability, and ability to use domain knowledge, which makes it very well suited for malware analysis and related tasks. The analysis by HIMALAYAS should accelerate the identification and takedown of malware domains on the Internet and improve services such as Google SafeSearch.

The machine-learning stack developed as part of the HIMALAYAS project has broader application to many important data mining problems, e.g., in financial data analysis, and mining user patterns from web access logs. The project provides opportunities for students to participate in the development and transition of the technology.

Relevant Publications

Hongyu Gao, Vinod Yegneswaran, Jian Jiang, Yan Chen, Phillip Porras, Shalini Ghosh and Haixin Duan. Reexamining DNS from a Global Recursive Resolver Perspective. Proceedings of IEEE Transactions on Networking, 2014.

Jialong Zhang, Jayant Notani and Guofei Gu. Characterizing Google Hacking: A First Large-Scale Quantitative Study. Proceedings of Securecomm, 2014.

Huahua Wang and Arindam Banerjee. Bregman Alternating Direction Method of Multipliers . Proceedings of NIPS, 2014.

Hongyu Gao, Vinod Yegneswaran, Yan Chen, Phillip Porras, Shalini Ghosh, Jian Jiang and Haixin Duan. An Empirical Reexamination of Global DNS Behavior . Proceedings of ACM SIGCOMM, August 2013.

This project is funded by a grant from the National Science Foundation. Award Number CNS-1314956. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.