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Malware Repository Overview

Wenke Lee David Dagon Georgia Institute of Technology. Malware Repository Overview. Overview. How malware is collected and shared now Malfease’s service-oriented repository

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Malware Repository Overview

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  1. Wenke Lee David Dagon Georgia Institute of Technology Malware Repository Overview

  2. Overview • How malware is collected and shared now • Malfease’s service-oriented repository • Support for malware analysis, e.g., signature generation, and evaluation of intrusion/anomaly detection/prevention systems, etc. • Automated unpacking

  3. Current Practices • Numerous private, semi-public malware collections • Need “trust” to join • “Too much sharing” often seen as competitive disadvantage • Analysis not shared • Incomplete collections: reflect sensor bias • Darknet-based collection • IRC surveillance • Honeypot-based collection

  4. Shortcomings • Malware authors know and exploit weaknesses in data collection • Illuminating sensors • “Mapping Internet Sensors with Probe Response Attacks”, Bethencourt, et al., Usenix 2005 • Automated victims updates • E.g., via botnets

  5. Solution:Service-Oriented Repository • Malfease uses hub-and-spoke model • Hub is central collection of malware • Spokes are analysis partners • Hub: • Malware, indexing, search • Static analysis: header extraction, icons, libraries • Metainfo: longitudinal AV scan results • Spoke: • E.g., dynamic analysis, unpacking, signatures, etc.

  6. Malware Repo Requirements • Malware repos should not: • Help illuminate sensors • Serve as a malware distribution site • Malware repo should: • Help automate analysis of malware flood • Coordinate different analysts (RE gurus, Snort rule writers, etc.)

  7. Approaches • Repository allows upload of samples • Downloads restricted to classes of users • Repository provides binaries and analysis • Automated unpacking • Win32 PE Header analysis • Longitudinal detection data • What did the AV tool know, and when did it know it? • Malware similarity analysis, family tree • Etc.

  8. Overview

  9. Repository User Classes • Unknown users • Scripts, random users, even bots • Humans • CAPTCHA-verified • Authenticated Users • Known trusted contributors

  10. Repository Access Control • Unknown users • Upload; view aggregate statistics • Humans • Upload; download analysis of their samples • Authenticated Users • Upload; download all; access analysis

  11. Basic User View

  12. Analysis Page for Sample

  13. Static Analysis Example

  14. Static Analysis Example Note search ability

  15. Dynamic Analysis Unpacked binary Available for Download, Along with asm version

  16. Malware: Why Pack? • Reduced malware size • Obfuscation transformation • Opaque binaries prevent pattern analysis • Invalid PE32 headers complicate RE • Increases response time • Unpacking often requires specialized skill sets

  17. Polyunpack: Work Flow

  18. Unpacking Heuristic

  19. Unpacking Example

  20. Results • Improved AV detection 10-40% improved AV detection on “old” stuff 5.2K Samples Claimed VX AV Scan 6K very old Samples Unpacking 0.8K Claimed “OK” 42 are now claimed VX AV ReScan

  21. Plan for Cyber-TA • Evaluation of various signature generation schemes • Development of new schemes • Development of signature ensemble scheme - automatically combine the attributes of signatures from different generation schemes • Evaluation of intrusion/anomaly detection systems • E.g., automatically generating mimicry/blending attacks based on malware

  22. Conclusion • Service-oriented repository • Support research in malware analysis and intrusion/anomaly detection/prevention • See malfease.oarci.net for details • Credits • David Dagon • Paul Vixie • Paul Royal • Mitch Halpin

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