Under the Shadow of Sunshine: Understanding and Detecting Bulletproof Hosting on Legitimate Service Provider Networks

BulletProof Hosting (BPH) services provide criminal actors with technical infrastructure that is resilient to complaints of illicit activities, which serves as a basic building block for streamlining numerous types of attacks. Anecdotal reports have highlighted an emerging trend of these BPH services reselling infrastructure from lower end service providers (hosting ISPs, cloud hosting, and CDNs) instead of from monolithic BPH providers. This has rendered many of the prior methods of detecting BPH less effective, since instead of the infrastructure being highly concentrated within a few malicious Autonomous Systems (ASes) it is now agile and dispersed across a larger set of providers that have a mixture of benign and malicious clients. In this paper, we present the first systematic study on this new trend of BPH services. By collecting and analyzing a large amount of data (25 snapshots of the entire Whois IPv4 address space, 1.5 TB of passive DNS data, and longitudinal data from several blacklist feeds), we are able to identify a set of new features that uniquely characterizes BPH on sub-allocations and that are costly to evade. Based upon these features, we train a classifier for detecting malicious sub-allocated network blocks, achieving a 98% recall and 1.5% false discovery rates according to our evaluation. Using a conservatively trained version of our classifier, we scan the whole IPv4 address space and detect 39K malicious network blocks. This allows us to perform a large-scale study of the BPH service ecosystem, which sheds light on this underground business strategy, including patterns of network blocks being recycled and malicious clients being migrated to different network blocks, in an effort to evade IP address based blacklisting. Our study highlights the trend of agile BPH services and points to potential methods of detecting and mitigating this emerging threat.

[1]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[2]  Stefano Zanero,et al.  BURN: baring unknown rogue networks , 2011, VizSec '11.

[3]  Joseph B. Kadane,et al.  Using uncleanliness to predict future botnet addresses , 2007, IMC '07.

[4]  Craig A. Shue,et al.  Abnormally Malicious Autonomous Systems and Their Internet Connectivity , 2012, IEEE/ACM Transactions on Networking.

[5]  Raheem A. Beyah,et al.  Lurking Malice in the Cloud: Understanding and Detecting Cloud Repository as a Malicious Service , 2016, CCS.

[6]  Paul Barford,et al.  Spatial-Temporal Characteristics of Internet Malicious Sources , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[7]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[8]  Nick Feamster,et al.  PREDATOR: Proactive Recognition and Elimination of Domain Abuse at Time-Of-Registration , 2016, CCS.

[9]  Roberto Perdisci,et al.  Early Detection of Malicious Flux Networks via Large-Scale Passive DNS Traffic Analysis , 2012, IEEE Transactions on Dependable and Secure Computing.

[10]  Z. Berkay Celik,et al.  Detection of Fast-Flux Networks using various DNS feature sets , 2013, 2013 IEEE Symposium on Computers and Communications (ISCC).

[11]  Leyla Bilge,et al.  Exposure: A Passive DNS Analysis Service to Detect and Report Malicious Domains , 2014, TSEC.

[12]  Craig A. Shue,et al.  Malicious Hubs: Detecting Abnormally Malicious Autonomous Systems , 2010, 2010 Proceedings IEEE INFOCOM.

[13]  Guang Cheng,et al.  Detecting domain-flux botnet based on DNS traffic features in managed network , 2016, Secur. Commun. Networks.

[14]  Fang Yu,et al.  Finding the Linchpins of the Dark Web: a Study on Topologically Dedicated Hosts on Malicious Web Infrastructures , 2013, 2013 IEEE Symposium on Security and Privacy.

[15]  Nick Feamster,et al.  ASwatch: An AS Reputation System to Expose Bulletproof Hosting ASes , 2015, Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication.

[16]  Kevin C. Almeroth,et al.  FIRE: FInding Rogue nEtworks , 2009, 2009 Annual Computer Security Applications Conference.

[17]  Mingyan Liu,et al.  On the Mismanagement and Maliciousness of Networks , 2014, NDSS.