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SANSFIRE18: War Stories on Using Automated Threat Intelligence for Defense

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Between limited resources and a lack of trained professionals on one hand and the increasing quantity and quality of attacks on the other, securing enterprises and responding to incidents has placed defenders on the losing end of a digital arms race. Even managing the amounts of threat data and open-source intelligence has become a challenge.

This talk will cover the possibilities and perils of integrating all the various sources of threat intelligence data to protect an organization. With all the various open-source and paid-source data, simply dumping it all into a firewall or DNS RPZ zone can be problematic. What to do about compromised websites or shared hosting environments? What about DGA domains that use full words and may collide with actual innocent websites? What about how to handle threat data that is lacking in context to make appropriate decisions on its validity and accuracy? This talk will present several case studies in how these problems can be tackled and how using multi-domain analysis can help reduce the risk and maximize the value of automated protection using these types of data.

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SANSFIRE18: War Stories on Using Automated Threat Intelligence for Defense

  1. 1. John Bambenek VP, Security Research and Intelligence, ThreatSTOP War Stories on Using Automated Threat Intelligence for Defense
  2. 2. About me • SANS ISC Handler • VP of Security Research and Intelligence at ThreatSTOP • Lecturer at the University of Illinois at Urbana-Champaign • Producer of open-source threat feeds • Involved in DNC, DCCC, et al investigations in 2016
  3. 3. The Problem – the “too much” issues • “1,000,000 unfilled cybersecurity jobs” • Too much work and not enough skilled people to do it. • Too much data, no clear prioritization. • Too much manual work to investigate and respond to incidents. • What’s Worth Responding To? What is the Intention of the Attacker? 3
  4. 4. The Problem in Numbers • Average dwell time during a breach: 4-5 months • Percentage of breaches were evidence was in logs: 80+% • These two data points mean that if a SOC knew what to look for and had the tools to respond quickly, a great deal of damage could be mitigated. 4
  5. 5. The Problem Illustrated (from Virustotal) 5
  6. 6. The Reality  There is a much smaller set of actual malware tools, many are used by multiple people.  Problem: How to use this data effectively?  How to manage large data sets to correlate behavior over time? 6
  7. 7. Pyramid of Pain
  8. 8. War Story #1 – Election Hacking • Brief overview of DNC, et al related hacks. • The private sector was “highly confident” of FSB/GRU attribution even before the news was released in the summer of 2016. • We have a long history of APT 28/29 history with a variety of TTPs and other info that allowed not just the responders, but those who verified the work of responders, to make determinations quickly. • And see what they were doing during the French Presidential Election, and some 2018 activity….
  9. 9. Stuff we know now
  10. 10. War Story #1 – Election Hacking • TTP – likes impersonating “vendors”/”partners”. • MIS Department in case of DNC • Using DomainTools Brand Monitor or Farsight Brand Sentry, you can proactively look for impersonation. • WHOIS details also provide clues. 10
  11. 11. WHOIS Registrant Intel • Often actors may re-use registrant information across different campaigns. There may be other indicators too. • Sometimes *even with WHOIS privacy protection* it may be possible to correlate domains and by extension the actor. • Most criminal prosecution in cybercrime is due to an OPSEC fail and the ability to map backwards in time of what the actor did to find that fail that exposes them. 11
  12. 12. War Story #1 – Election Hacking 12 Maltego graph from Motherboard: https://motherboard.vice.com/en_us/article/vvaxy8/evidence-linking- russian-hackers-fancy-bear-to-macron-phishing
  13. 13. War Story #1 – Election Hacking • Trend Micro was looking for domains with “en-marche” in the name and found 4. • En Marche! Said they fed fake information to the adversary. • Contrast with American response. • You COULD hack back here… but why? • There are dangers of deception though. 13
  14. 14. What we can do in 2018? • Because of the shear number of targets, any in-depth attempt to target political or election organizations will be “loud”. • If data is shared (IPs, domains, etc), AND you automatically block them, you can have a good layer of protection. • MS-ISAC, DHS AIS, other…
  15. 15. Malware Configs • Every malware has different configurable items. • Not every configuration item is necessarily valuable for intelligence purposes. Some items may have default values. • Free-form text fields provide interesting data that may be useful for correlation. • Mutex can be useful for correlating binaries to the same actor. • How to get to the identity of someone using Cobalt Strike to attack you? • KEY POINT: Non-operational data is still useful for intelligence purposes. 15
  16. 16. Where to get Malware • Everyone uses Virustotal • You can buy a malware feed… • Better is to mine your spam / e-mail for attacks. • This is the targeted malware no one can sell you. • Eliminate malware seen by VT (other sources), that is unique • Who are the repeat visitors? Advanced attackers need to go low and slow... 16
  17. 17. Sample DarkComet Data • Key: CampaignID Value: Guest16 • Key: Domains Value: 06059600929.ddns.net:1234 • Key: FTPHost Value: • Key: FTPKeyLogs Value: • Key: FTPPassword Value: • Key: FTPPort Value: • Key: FTPRoot Value: • Key: FTPSize Value: • Key: FTPUserName Value: • Key: FireWallBypass Value: 0 • Key: Gencode Value: 3yHVnheK6eDm • Key: Mutex Value: DC_MUTEX-W45NCJ6 • Key: OfflineKeylogger Value: 1 • Key: Password Value: • Key: Version Value: #KCMDDC51# 17
  18. 18. Sample njRat config • Key: Campaign ID Value: 1111111111111111111 • Key: Domain Value: apolo47.ddns.net • Key: Install Dir Value: UserProfile • Key: Install Flag Value: False • Key: Install Name Value: svchost.exe • Key: Network Separator Value: |'|'| • Key: Port Value: 1177 • Key: Registry Value Value: 5d5e3c1b562e3a75dc95740a35744ad0 • Key: version Value: 0.6.4 18
  19. 19. All the fields… • ActivateKeylogger,ActiveXKey,ActiveXStartup,AddToRegistry,AntiKillProcess,BypassUAC,CONNECTION _TIME,Campaign,ChangeCreationDate,ClearAccessControl,ClearZoneIdentifier,ConnectDelay,Custom RegKey,CustomRegName,CustomRegValue,DELAY_CONNECT,DELAY_INSTALL,Date,DebugMsg,Domain ,EnableDebugMode,EnableMessageBox,EncryptionKey,Error,ExeName,FTPDirectory,FTPHost,FTPInter val,FTPKeyLogs,FTPPassword,FTPPort,FTPRoot,FTPServer,FTPSize,FTPUser,FireWallBypass,FolderNam e,Gencode,GoogleChromePasswords,Group,HKCU,HKLM,HideFile,ID,INSTALL,INSTALL_TIME,Injection ,InstallDir,InstallDirectory,InstallFileName,InstallFlag,InstallFolder,InstallMessageBox,InstallMessageTi tle,InstallName,JAR_EXTENSION,JAR_FOLDER,JAR_NAME,JAR_REGISTRY,JRE_FOLDER,KeyloggerBacks pace=Delete,KeyloggerEnableFTP,KillAVG2012- 2013,MPort,MeltFile,MessageBoxButton,MessageBoxIcon,MsgBoxText,MsgBoxTitle,Mutex,NICKNAM E,NetworkSeparator,OS,OfflineKeylogger,Origin,P2PSpread,PLUGIN_EXTENSION,PLUGIN_FOLDER,Pas sword,Perms,Persistance,Port,PreventSystemSleep,PrimaryDNSServer,ProcessInjection,RECONNECTI ON_TIME,REGKeyHKCU,REGKeyHKLM,RegistryValue,RequestElevation,RestartDelay,RetryInterval,Run OnStartup,SECURITY_TIMES,ServerID,SetCriticalProcess,StartUpName,StartupPolicies,TI,TimeOut,US BSpread,UseCustomDNS,VBOX,VMWARE,Version,_raw,_time,adaware,ahnlab,baidu,bull,clam,comod o,compile_date,date_hour,date_mday,date_minute,date_month,date_second,date_wday,date_year, date_zone,escan,eventtype,fprot,fsecure,gdata,host,ikarus,immunet,imphash,index,k7,linecount,ma gic,malw,mc,mcshield,md5,nano,norman,norton,outpost,panda,product,proex,prohac,quickheal,rat_ name,resys,run_date,section_,section_.BSS,section_.DATA,section_.IDATA,section_.ITEXT,section_.R DATA,section_.RELOC,section_.RSRC,section_.TEXT,section_.TLS,section_AKMBCZMH,section_BSS,sec tion_CODE,section_DATA,section_ELTQHVWF,section_VDOJLYFM,section_YRKCHNMU,sha1,sha256,s ource,sourcetype,splunk_server,splunk_server_group,spybot,super,tag,tag::eventtype,taskmgr,times _submitted,timestamp,trend,uac,unique_sources,unthreat,vendor,vipre,windef,wire © Fidelis Cybersecurity. All rights reserved. 19
  20. 20. War Story #2 – Understanding Locky • Locky uses combination of static domains and a DGA for C2. • Has an affiliate program. • Seems to heavily favor necurs for delivery (but not exclusively) 20
  21. 21. War Story #2 – Understanding Locky 21
  22. 22. War Story #2 – Understanding Locky • We know there is a close relationship between necurs and Locky. (What about specific affiliates?) • We can see it’s likely Locky operator runs C2 infrastructure on behalf of affiliates. • This can inform prosecutorial decisions or potential “hack back” operatiors (i.e. stealing encryption keys) 22
  23. 23. Using DNS to Track the Adversary • Only certain ways you can contact a C2 server: • Static IP / Hostname Lists • Proxied C2s • Dynamic DNS • Fast Flux / Double Flux Networks • Domain Generation Algorithms • Tor / i2p hidden services 23
  24. 24. Domain Generation Algorithms  Usually a complex math algorithm to create pseudo-random but predictable domain names.  Now instead of a static list, you have a dynamic list of hundreds or thousands of domains and adversary only needs to have a couple registered at a time.  Can search for “friendly” registrars to avoid suspension. 24
  25. 25. Reverse Engineering DGAs  Many blog posts about reversing specific DGAs, Johannes Bader has the most online at his blog:  Johannesbader.ch  No real shortcuts except working through IDA/Debugger and reversing the function.  Look for functions that iterate many times.  There will be at least a function to generate the domains and a function to connect to all of them to find the C2.  As with all reverse engineering, be aware of obfuscation and decoy code meant to deceive you.
  26. 26. Types of DGAs  Almost all DGAs use some time of “Seed”.  Types:  Date-based  Static seed  Dynamic seed  Seed has to be globally consistent so all victims use the same one at the same time.
  27. 27. Feed generation on DGAs • sjuemopwhollev.co.uk,Domain used by Cryptolocker - Flashback DGA for 13 Aug 2015,2015-08-13 • meeeqyblgbussq.info,Domain used by Cryptolocker - Flashback DGA for 13 Aug 2015,2015-08-13 • ntjqyqhqwcwost.com,Domain used by Cryptolocker - Flashback DGA for 13 Aug 2015,2015-08-13, • nvtvqpjmstuvju.net,Domain used by Cryptolocker - Flashback DGA for 13 Aug 2015,2015-08-13 • olyiyhprjuwrsl.biz,Domain used by Cryptolocker - Flashback DGA for 13 Aug 2015,2015-08-13 • sillomslltbgyu.ru,Domain used by Cryptolocker - Flashback DGA for 13 Aug 2015,2015-08-13 • gmqjihgsfulcau.org,Domain used by Cryptolocker - Flashback DGA for 13 Aug 2015,2015-08-13, • From here you could easily feed this into RPZ or other technology to protect your organization.
  28. 28. DGA surveillance  Pre-generate all domains 2 days before to 2 days in future.  Pipe all those domains into adnshost using parallel to limit the number of lines.  Able to process over 700,000 domains inside 10 minutes (and I’m not done optimizing). • parallel -j4 --max-lines=3500 --pipe adnshost -a -f < $list-of-domains | fgrep -v nxdomain >> $outputfile
  29. 29. New Matsnu domains registered
  30. 30. What to do with this data? • With IP addresses, you can just block them at the firewall. • Inbound **AND** outbound traffic. • If you control DNS, you control the endpoint. Use a DNS Firewall! • Which means you can limit what the device can talk to in order to prevent exploitation or command-and-control. • DNS is on everything… even IoT devices!
  31. 31. What is a DNS Firewall? • Uses RPZ (Response Policy Zones) or the Microsoft equivalent. • Response Policy Zones are zone files you put into your DNS resolver that can block, redirect, or alert on specific queries. • Can flag on: • Specific hostname, domain, or TLD (i.e. www.google.com or *.ru) • The resolved IP address • The authoritative nameserver hostnames used • The authoritative nameserver IP addresses used
  32. 32. Block Bad Neighborhoods • There are many networks you can be pretty sure they are “always” safe (i.e. CDNs). • There are many networks you can treat as completely malicious (i.e. bullet proof hosters). • Some countries you may not have (or want) to talk to. • ITAR/OFAC • Why should your MRI machine talk to a Russian IP?
  33. 33. War Story #3 – Operation Tovar • One of the first modern successful ransomware attacks. • Was able to proactively monitor all new registrations for domains, mine registrant details, and ultimately get quicker to look at proxies. • This not only allowed us to grind to get to an indictment of Evgeniy Bogachev, but also to retrieve the private encryption keys so people could get their files back. • Was able to do a bulk takedown and shut the whole system down. 33
  34. 34. Tracking Malware Functions • We have tools to correlate IP addresses, domains, registration information, malware families, malware configs… • What about specific functions or portions of code? • The more we can correlate, the more we can get visibility into how code is shared, developed, and the ecosystem behind it. 34
  35. 35. FIRST IDA Plugin • Developed by Cisco Talos: https://github.com/vrtadmin/FIRST- plugin-ida • In essence, ties a database into IDA so you can search for functions that exist elsewhere to find code level relationships. • Presentation: https://www.botconf.eu/wp- content/uploads/2016/11/PR11-Function-Identification-and- Recovery-Signature-Tool-Villegas.pdf 35
  36. 36. FIRST IDA Plugin 36
  37. 37. War Story #4 - Wannacry • We all know Wannacry, worm-based ransomware using disclosed exploits (Thanks NSA!). • Very quickly we noticed that the payment infrastructure was not sound (and neither was NotPetya) • What’s the point of cryptographic ransomware if you aren’t getting paid? (Made only about $100k USD) 37
  38. 38. War Story #4 - Wannacry 38 From Costin Raiu twitter, 40 byte code reuse from Lazarus backdoor
  39. 39. War Story #4 - Wannacry • 40 bytes of code were identical to a Lazarus Group (DPRK) backdoor used in 2015. • Found by “spot checking” and memory. • This is not ideal • Not found anywhere else. • Inconclusive but suggests DPRK (since proven). • We NEED to figure out a way to make this a database search problem, not a tribal lore in analyst’s mind problem. 39
  40. 40. Last Key Point • Ending this talk with WannaCry and NotPetya was intentional. • Most of the techniques here are useful for crime. • Increasingly, however, APT is using crime tools as “obfuscation”. • WannaCry and NotPetya (if we’re right) are precursors to future APT attacks using criminals tools. • What if our research leads to a kinetic response? • We need to get the above right to disambiguate their intentions and to find investigate leads and potential weaknesses (hack back?) 40
  41. 41. Solution • Lots of us are all working on the same problems independently, we need to be working together more and sharing data. • Sharing data isn’t to contribute more to “admiring the problem”. Need to block stuff. • Back to Pyramid of Pain, block as much as you can as low as you can to focus limited people/resources on ”what’s left”. 41
  42. 42. Questions? John Bambenek / @bambenek jbambenek@threatstop.com

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