An introduction to honeyclient technologies

1. An introduction to honeyclient technologies Christian Seifert Angelo Dell'Aera

2. Speakers Christian Seifert • Full Member of the Honeynet Project since 2007 • PhD from Victoria University of Wellington, NZ • Research Software Engineer @ Microsoft Bing Angelo Dell'Aera • Full Memberof the Honeynet Project since 2009 • Senior Threat Analyst @ Security Reply (7 years) • Information Security Independent Researcher @ Antifork Research (13 years)

3. Agenda • Introduction • Honeyclient technologies • Low-Interaction (PhoneyC) • High-Interaction (Capture-HPC) • Malware Distribution Networks • Challenges and Future Work

4. New trends, new tools • In the last years more and more attacks against client systems • The end user as the weakest link of the security chain • New tools are required to learn more about such client-side attacks

5. New trends, new tools • The browser is the most popular client system deployed on every user system • A lot of vulnerabilities are daily identified and (almost always) reported in the most used browsers • The browser is currently the preferred way to own an host

6. Honeyclients • What we need is something which seems like a real browser the same way as a classical honeypot system seems like a real vulnerable server • A real system (high-interaction) • Or an emulated one (low-interaction)? Queuer Visitor Analysis Engine

7. Low-interaction strengths and weaknesses + Different browser versions (“personalities”) + Different ActiveX and plugins modules (even different versions) + Much more safer + More scalable - Easy to detect

8. PhoneyC - Brief History • A pure Python low-interaction honeyclient • First version developed by Jose Nazario • Great improvements during GSoC 2009 • And the history continues...

9. PhoneyC – DOM Emulation • “The Document Object Model is a platform- and language-neutral interface that will allow programs and scripts to dynamically access and update the content, structure and style of documents. The document can be further processed and the results of that processing can be incorporated back into the presented page.” (W3C definition) • Huge improvements during GSoC 2009 • Python object __getattr__ and __setattr__ methods

10. PhoneyC - Browser Personalities • Currently supported personalities: • Internet Explorer 6.0 (Windows XP) • Internet Explorer 6.1 (Windows XP) • Internet Explorer 7.0 (Windows XP) • Internet Explorer 8.0 (Windows XP) • Internet Explorer 6.0 (Windows 2000) • Internet Explorer 8.0 (Windows 2000) • Easy to add new personalities

11. PhoneyC - Javascript Engine • Based on SpiderMonkey, the Mozilla implementation of the Javascript engine • HoneyJS: a bridge between Python and SpiderMonkey which wraps a subset of its APIs • HoneyJS based on python-spidermonkey

12. PhoneyC - Vulnerability Modules • Python-based vulnerability modules • Core browser functionalities • Browser plugins • (Mock) ActiveX controls

13. PhoneyC - Shellcode detection and emulation • HoneyJS “The shellcode manipulation and the spraying of the fillblock involve assignments.The shellcode will be detected immediately on its assignment if we are able to interrupt spidermonkey at the interpretion of certain bytecodes related to an assignment and check its arguments and values for shellcodes” • Libemu integration (shellcode detection, execution and profiling)

14. A new and more reliable DOM (Document Object Model) emulation Replacing Spidermonkey with Google V8 Mixed static/dynamic analysis for detecting potential attacks PhoneyC - Future Improvements

15. Request Response Request Client Honeypots Attack Malicious Server Benign Server High-interaction Client Honeypot • Real system • Observe effects of attack No state changes detected New file appeared in start up folder

16. High-interaction strengths and weaknesses + No emulation necessary + Accurate classification (extremely low false positive rate) + Ability to detect zero-day attacks + More difficult to evade - Miss attacks - “Dangerous” - More computationally expensive

17. Capture-HPC (v2.5) - Functionality • Platform Independence * • Flexibility around client application • Forensically ready • Records information at kernel level • Collects modified files (e.g. malware) • Collects network traffic (pcap) • Maintained by the New Zealand Honeynet Project Chapter

18. Malware Distribution Networks

19. Malware Distribution NetworksOverview • Set of web servers (network) controlled by a group of cyber criminals to distribute malware efficiently • Specialized structures that support specialized roles of the cyber criminal • Malware distribution networks allow for campaigns and temp renting out components of the distribution network

20. Malware Distribution Networks Source: Microsoft Security Intelligence Threat Report (http://www.microsoft.com/sir)

21. Malware Distribution Network

22. Exploit Servers 12.8% of exploit servers responsible for 84.1% of drive-by-download pages Source: Microsoft Security Intelligence Threat Report (http://www.microsoft.com/sir)

23. Challenges and Future Work

24. Malware Distribution Network

25. Malware Distribution NetworksFast-Flux • LP infected with script that contacts twitter to obtain popular topics (e.g. japan) • From popular query from last week, script constructs host name (e.g. “j” + date) • Next day, the same LP will contact twitter to obtain popular topics (e.g. tunesia) • Now, it will construct different host name (e.g. “t” + date) • Attacker registers hostname a few days in advance LP1 LP2 R1 R2 ES1 ES2

26. Evasion Techniques • Technology Differences (Browser vs Honeyclient) • Human vs Machine Interaction • Decrease visibility

27. The Threats Availability Crashes Integrity Drive-by-pharming Network floods/ Puppetnets Drive-by-Downloads Web spam/ junk pages Social Engineering Hosting of malware Popup floods Cross-X attacks Confidentiality Cookie, history, file, and clipboard stealing Network scanners Phishing

28. Jose Nazario, “PhoneyC: A virtual client honeypot”, LEET 2009 The Honeynet Project, KYE: Malicious Web Servers, http://www.honeynet.org/papers Junjie Zhang, Jack Stokes, Christian Seifert and Wenke Lee, ARROW: Generating Signatures to Detect Drive-By Downloads, in proceedings of www conference, Hyderabad, India, 2011 Microsoft, Security Intelligence Threat Report, http://www.microsoft.com/sir References

29. Thanks for the attention Questions? Christian Seifert <christian.seifert@honeynet.org> Angelo Dell'Aera <angelo.dellaera@honeynet.org> http://code.google.com/p/phoneyc/ https://projects.honeynet.org/capture-hpc