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Zeus Financial Malware Samaneh Tajalizadehkhoob Hadi Asghari - PowerPoint PPT Presentation

Sep 21, 2023 •360 likes •679 views

Why Them? Extracting Intelligence about Target Selection from Zeus Financial Malware Samaneh Tajalizadehkhoob Hadi Asghari Carlos Gan Michel van Eeten Economics of Cybersecurity Group, Delft University of Technology Outline 1. Problem

  1. Why Them? Extracting Intelligence about Target Selection from Zeus Financial Malware Samaneh Tajalizadehkhoob Hadi Asghari Carlos Gañán Michel van Eeten Economics of Cybersecurity Group, Delft University of Technology

  2. Outline 1. Problem of online banking fraud 2. Zeus malware Capturing attackers’ instructions from infected machines 3. 4. Extracting intelligence from the instructions (targets, inject code) 5. Who is being targeted? 6. Do bigger targets attract more attacks? 7. How fast do attackers explore new targets? 8. Did the sudden availability of Zeus source code increase attacks? 9. How does inject code evolve? 10. Conclusion 2 Online Banking Fraud and Target Selection by Cybercriminals

  3. Online banking fraud • Fraud statistics for the Single European Payment area are around € 800 million (European Central Bank, 2014) • Different banks with different properties are targeted around the world • No clear patterns have been found till now • Little information is published about the targeted domains • Even when the information exists, it is incomplete and under/over counted 3 Online Banking Fraud and Target Selection by Cybercriminals

  4. Outline 1. Problem of online banking fraud 2. Zeus malware Capturing attackers’ instructions from infected machines 3. 4. Extracting intelligence from the instructions (targets, inject code) 5. Who is being targeted? 6. Do bigger targets attract more attacks? 7. How fast do attackers explore new targets? 8. Did the sudden availability of Zeus source code increase attacks? 9. How does attack code evolve? 10. Conclusion 4 Online Banking Fraud and Target Selection by Cybercriminals

  5. Targeted bank MiiB Bot Zeus C&C Bot Zeus C&C MiiB Bot Zeus Bot C&C 5 Online Banking Fraud and Target Selection by Cybercriminals

  6. Targeted bank MiiB Bot C&C Bot C&C MiiB Bot C&C Bot 11,000 config files targeting (2009 - 2013) 6 Online Banking Fraud and Target Selection by Cybercriminals

  7. 7 Online Banking Fraud and Target Selection by Cybercriminals

  8. Outline 1. Problem of online banking fraud 2. Zeus malware Capturing attackers’ instructions from infected machines 3. 4. Extracting intelligence from the instructions (targets, inject code) 5. Who is being targeted? 6. Do bigger targets attract more attacks? 7. How fast do attackers explore new targets? 8. Did the sudden availability of Zeus source code increase attacks? 9. How does attack code evolve? 10. Conclusion 8 Online Banking Fraud and Target Selection by Cybercriminals

  9. Targeted domains • Between January 2009 and March 2013, 2,131 unique botnets were in operation (based on different encrypted command and control channels) • These botnets targeted 2,412 unique domains – via 14,870 unique URLs • Located in 92 countries • Over 74% of the targets are financial service providers 9 Online Banking Fraud and Target Selection by Cybercriminals

  10. Attack persistence Briefly attacked Always attacked 10 Online Banking Fraud and Target Selection by Cybercriminals

  11. Outline 1. Problem of online banking fraud 2. Zeus malware Capturing attackers’ instructions from infected machines 3. 4. Extracting intelligence from the instructions (targets, inject code) 5. Who is being targeted? 6. Do bigger targets attract more attacks? 7. How fast do attackers explore new targets? 8. Did the sudden availability of Zeus source code increase attacks? 9. How does attack code evolve? 10. Conclusion 11 Online Banking Fraud and Target Selection by Cybercriminals

  12. Is target popularity related to its size? • Minor, but significant relationship between the size of a domain (measured by Alexa ranking) and the persistence of attacks 12 Online Banking Fraud and Target Selection by Cybercriminals

  13. Is target popularity related to its size? • United States: out of around 6,500 financial institutions with online presence, only 175 have been targeted • Almost all of the larger banks (48 of the top 50) are attacked • Size acts as a threshold for being attacked; it does not predict attack intensity 13 Online Banking Fraud and Target Selection by Cybercriminals

  14. Outline 1. Problem of online banking fraud 2. Zeus malware Capturing attackers’ instructions from infected machines 3. 4. Extracting intelligence from the instructions (targets, inject code) 5. Who is being targeted? 6. Do bigger targets attract more attacks? 7. How fast do attackers explore new targets? 8. Did the sudden availability of Zeus source code increase attacks? 9. How does attack code evolve? 10. Conclusion 14 Online Banking Fraud and Target Selection by Cybercriminals 14

  15. Trial of new targets • Average of 601attacked domains per month by Zeus malware • Average of 112 of these are new domains each month • There is a relatively stable ceiling in the peaks of overall attacked domains, as well as in the trial and error for new targets 15 Online Banking Fraud and Target Selection by Cybercriminals

  16. Trial of new targets • Seeking new targets across a larger area • In 2012, 17 new countries were targeted, but 18 countries from the previous years were no longer being attacked 16 Online Banking Fraud and Target Selection by Cybercriminals

  17. Outline 1. Problem of online banking fraud 2. Zeus malware Capturing attackers’ instructions from infected machines 3. 4. Extracting intelligence from the instructions (targets, inject code) 5. Who is being targeted? 6. Do bigger targets attract more attacks? 7. How fast do attackers explore new targets? 8. Did the sudden availability of Zeus source code increase attacks? 9. How does attack code evolve? 10. Conclusion 17 Online Banking Fraud and Target Selection by Cybercriminals

  18. Number of active botnets 18 Online Banking Fraud and Target Selection by Cybercriminals

  19. Outline 1. Problem of online banking fraud 2. Zeus malware Capturing attackers’ instructions from infected machines 3. 4. Extracting intelligence from the instructions (targets, inject code) 5. Who is being targeted? 6. Do bigger targets attract more attacks? 7. How fast do attackers explore new targets? 8. Did the sudden availability of Zeus source code increase attacks? 9. How does attack code evolve? 10. Conclusion 19 Online Banking Fraud and Target Selection by Cybercriminals

  20. Inject code development over time • 1.1m target URLs with ‘inject’ codes • On average, each inject code is repeated 27 times; 43% repeated over 1,000 times, and just 1% appears once! • Across all Zeus botnets and attackers, code similarity is over 90% from one attack to the next. 97% per URL per botnet • This suggests sharing, stealing or selling code across attackers 20 Online Banking Fraud and Target Selection by Cybercriminals

  21. Outline 1. Problem of online banking fraud 2. Zeus malware Capturing attackers’ instructions from infected machines 3. 4. Extracting intelligence from the instructions (targets, inject code) 5. Who is being targeted? 6. Do bigger targets attract more attacks? 7. How fast do attackers explore new targets? 8. Did the sudden availability of Zeus source code increase attacks? 9. How does attack code evolve? 10. Conclusion 21 Online Banking Fraud and Target Selection by Cybercriminals

  22. Conclusions • Although Zeus inject code was highly reused and Zeus source code became openly available, the criminal market of Zeus-based attacks did not expand as theory and experts predicted • Mitigating financial fraud might be more effective by allocating resources away from fighting freely available attacker resources 22 Online Banking Fraud and Target Selection by Cybercriminals

  23. Questions? 23 Online Banking Fraud and Target Selection by Cybercriminals

  24. Backup 24 Online Banking Fraud and Target Selection by Cybercriminals 24

  25. Inject Code Size vs. Repetition 25 Online Banking Fraud and Target Selection by Cybercriminals

  26. Summary • Not every Financial Service Provider is equally popular among criminals • Size is a threshold for getting attacked, but does not predict the intensity • Attack persistence varies widely. Half the domains are targeted briefly, mostly likely in search of new targets • Attack (and defense!) is less dynamic than often presumed 26 Online Banking Fraud and Target Selection by Cybercriminals

  27. Summary • The underground market for bots and malware may have lower economic entry barriers for criminals and reduced costs in the value chain of attacks, but it has not increased attack volume (number of botnets) or the number of targets • Attack ceiling suggests other bottlenecks in the criminal value chain, such as in cash out operations and mule recruitment • Defense should focus on these bottlenecks, not only on reducing abundant attacker resources (i.e., bots, malware and injects) 27 Online Banking Fraud and Target Selection by Cybercriminals

  28. Next steps • Map security properties of attacked services (e.g., authentication mechanism) • Study interaction among attack and defense (e.g., deterrence, waterbed effect?) • Statistically model factors that determine fraud levels in countries • Identify most cost-effective countermeasures 28 Online Banking Fraud and Target Selection by Cybercriminals

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