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Distributed Sensor Data Contextualization for Threat Intelligence Analysis


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As organizations operationalize diverse network sensors of various types, from passive sensors to DNS sinkholes to honeypots, there are many opportunities to combine this data for increased contextual awareness for network defense and threat intelligence analysis. In this presentation, we discuss our experiences by analyzing data collected from distributed honeypot sensors, p0f, snort/suricata, and botnet sinkholes as well as enrichments from PDNS and malware sandboxing. We talk through how we can answer the following questions in an automated fashion: What is the profile of the attacking system? Is the host scanning/attacking my network an infected workstation, an ephemeral scanning/exploitation box, or a compromised web server? If it is a compromised server, what are some possible vulnerabilities exploited by the attacker? What vulnerabilities (CVEs) has this attacker been seen exploiting in the wild and what tools do they drop? Is this attack part of a distributed campaign or is it limited to my network?

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Distributed Sensor Data Contextualization for Threat Intelligence Analysis

  1. 1. Distributed Sensor Data Contextualization for Threat Intelligence Analysis Jason Trost January 12, 2016
  2. 2. whoami Jason Trost •VP of Threat Research @ ThreatStream •Previously at Sandia, DoD, Booz Allen, Endgame Inc. •Background in Big Data Analytics, Security Research, and Machine Learning •Big advocate and contributor to open source: • Modern Honey Network, BinaryPig, Honeynet Project • Apache Accumulo, Apache Storm, Elasticsearch •3rd time participating at FloCon (2013, 2015, 2016)
  3. 3. ThreatStream •Cyber Security company founded in 2013 and venture backed by Google Ventures, Paladin Capital Group, Institutional Venture Partners, and General Catalyst Partners. •SaaS based enterprise security software that provides actionable threat intelligence to large enterprises and government agencies. •Our customers hail from the financial services, healthcare, retail, energy, and technology sectors.
  4. 4. Agenda •Background •Sensors •Enrichment •Contextualization •Wrap up
  5. 5. Background • Huge proliferation of new and old network sensors • IDS, Passive Inventory Systems, Malware Sandboxes • Honeypots, DNS Sinkholes, Endpoint agents • Netflow, Packet logging, etc • Many useful data enrichment sources • Passive DNS (PDNS), Whois, IP Geolocation • Large Malware Metadata Repositories • Network Telescopes / Distributed Sensors / Honeypots • Port scan and Web crawl data repositories • Internal IT Management, Security, and IR Systems • Vulnerability Databases • Huge talent shortage in Security, lots of need to make existing analysts better and reduce bar for new analysts • Lots of opportunities for combining these data sets, interpreting them, and contextualizing events for threat researchers and SOC analysts • Data overload if not leveraged carefully • This research started with Honeypots, expanded to other events …
  6. 6. Enrichment •Datasets that are useful for joining with events •Both local and external datasets can be useful •Very useful as features for machine learning models •Examples: • Whois • Passive DNS • Active probing data repositories (port scan, traceroute, web crawl) • Malware Metadata Repositories • Threat Intelligence Knowledgebase • Rollups, Analytics, Facts from your sensors (e.g. netflow, IDS) • Internal IT management, Security, and IR Systems
  7. 7. Contextualization •Gather details and related information to make an event or an indicator more actionable •Guide the analyst towards best practices •Help analysts work faster/better •Encode expert knowledge in the analytics and presentation •Building blocks for more automation, decision support, and features for classifiers
  8. 8. Sensor Combinations
  9. 9. Honeypots •Software systems designed to mimic vulnerable servers and desktops •Used as bait to deceive, slow down, or detect hackers, malware, or misbehaving users •Designed to capture data for research, forensics, and threat intelligence •Also useful as sinkhole servers when paired with DNS RPZ
  10. 10. Why Honeypots • Cheapest way to generate threat intelligence feeds around malicious IP addresses at scale • Internal deployment • Behind the firewall • Low noise IDS sensors • Can be used in conjunction with DNS RPZ as sinkhole webserver • Local External deployment • Who is attacking me? • Outside the firewall and on your IP space • Global External deployment • Rented Servers, Cloud Servers, etc. • Who is attacking everyone? • Global Trends
  11. 11. Modern Honey Network (MHN) • Open source platform for managing honeypots, collecting and analyzing their data • • Makes it very easy to deploy new honeypots and get data flowing • Leverages some existing open source tools • hpfeeds • nmemosyne • honeymap • MongoDB • Dionaea, Amun, Conpot, Glastopf • Wordpot, Kippo, Elastichoney, Shockpot • Snort, Suricata, p0f
  12. 12. Beyond Honeypot Sensors •Malware Sandboxes •Sinkholes •Endpoint Security Products •Intrusion Detection Systems •Protocol Analyzers/Decoders •Passive Device Inventory/Fingerprinting
  13. 13. Malware Sandbox •Dynamic Execution of Malware to gather IOCs, record execution traces, look for malicious activity •Deploy IDS on Malware Sandbox (Detonate files or URLs) • Signatures Identify some types of C2 network traffic • Identify Exploit Kit traffic (CVE tagger) • Identify sinkhole IPs passively • Extract indicators, CVEs, Context, make associations • Any future event regarding these IOCs on your network should be enriched with this context
  14. 14. Sinkholes • High interaction systems that mimic real services and C2 protocols where possible. Used to identify compromised systems • Conceptually similar to honeypots, but you drive traffic to them through RPZ • Use IDS to analyze sinkhole traffic • tag traffic where possible with C2 protocols • Deploy with p0f to gather host metadata (operating system, uptime, service banners) • Local Deployment • Use RPZ to sinkhole known malicious / suspicious domains • Malware C2 • Dynamic DNS domains • Exploit kit domains • Identify internal compromised systems • External Deployment • Register expired malicious domains or seize them • Identify infected systems across the globe
  15. 15. Automated Incident Response Collection • Starting Point: Policy Violation, Network IDS Alert, Honeypot Sensor Event, DNS Sinkhole hit, Indicator Match in SIEM, etc. • Automatically collect host based data, esp related to the network event • Logged in users • Running processes • DNS cache • Open network connections • Persistence checks • Prefetch files • Diff the collected data against the previous collection or a “gold image” • Prepare context for analyst
  16. 16. Enrichments
  17. 17. Enrichments: Whois • Domain registration data • Query the whois system on-demand (heavily rate limited), query 3rd party providers (pay-per-query), or buy bulk database for offline queries/mining • Who registered this domain? • Was this domain registered with a free email provider? • Was this domain registered with a disposable email provider? • Privacy protected? • Is this domain likely sinkholed? • Registration data congruent?
  18. 18. Enrichments: Internal IT, Security, and IR Systems • Identity Information • Asset Data • Specific Device • Owner • Device Characteristics • Software Inventory • Asset Discovery Data • Governance Risk and Compliance (GRC) Systems • Related Incident Response Tickets
  19. 19. Enrichments: Passive DNS (PDNS) • What other domains resolved to this IP? • What other IPs did this domain resolve to? • Is this domain sinkholed? • Is this a parking IP? • Is this domain resolving to an IP using DHCP? • Fast flux domain? • Often useful to combine with Whois • Common registrant across most domains resolving to single IP? -> Sinkholed • Nameserver name contains “sinkhole”, “abused”, “seized”? -> Sinkholed • Diverse registrants, common registrar? -> Parking IP (or Shared Hosting) • Diverse registrants, uncommon registrar? -> Shared Hosting IP
  20. 20. Enrichments: Active Probing Data • Internet scale Port scan, Web crawl, traceroute Repositories • Build your own or leverage 3rd parties • Host profile • Web server? • Embedded Device? • IOT Device? • Router? • Workstation? • C2 Panel? • Vulnerabilities? • Many can be determined unobtrusively • Signature Database needed • Sinkhole? • X-Sinkhole header • SSL Cert Metadata
  21. 21. Contextualization
  22. 22. Honeypot Attacker Profile? • p0f events? • OS? • Linux or Windows or other? • Uptime? • short (less than 1 day)? • long (weeks or more)? • MTU? • Cable? • DSL? • VPN/tunneled? • Query PDNS for the IP, filter for recent resolutions • Large number of diverse domains?  could be a web server • Query Port scan repository • recent port 80/443 open? • Query threat intelligence knowledge database • TOR? • I2P? • Commercial VPN? • Open or Commercial proxy? Infected windows workstation? • home / work Compromised webserver? • shared hosting? • dedicated? Ephemeral scanning/exploitation server? Long running scanning server (Shodan, Censys, ZoomEye, TOR nodes)?
  23. 23. Compromised System – How? • Attacker using a compromised system? • Compromised web server? • Port scan/Web crawl DB: port 80/443/8080 open? • Query PDNS: lots of recent domains, could be shared hosting • Compromised mail server? Query PDNS • Port scan/Web crawl DB: port 25/110/995/143/993 open? • domains with mail*, smtp*, pop* subdomains? • Uptime measurement from p0f? • days/weeks/months? • How did they get in? Query port scan/web crawl data repository • Wordpress / Joomla / Drupal? • Cpanel / Webmin / Vestacp / Ispconfig / Virtualmin / Ajenti? • SSH brute force? • IOT device?
  24. 24. Campaign Scope? •Is this IP attacking just me? •Are they attacking my vertical? •Are they attacking everyone? •Distributed Honeypots or sensors (or data sharing) are key here • Query external global deployment • Query external local deployment • Combine Events and summarize • first seen / last seen / number of sensors hit / ports involved • histogram of activity • Summary of exploits used, tools dropped & related C2s
  25. 25. Attacker Toolkit • Deploying Honeypots with IDS can assist here • Snort/Suricata are really useful for adding more context • CVE Tagging – roughly 1/3 of the Emerging Threat Snort Rules have CVEs • Classify traffic, fingerprint of attack tools? • Honeypots should collect exploit payloads and commands attempted • Windows and Linux Malware Sandboxing • Execute these commands/scripts (often times wget + execute) • Save all payloads • Extract host and network IOCs • Maintain relationship to original attacker IP • Query toolsets in VirusTotal
  26. 26. Gotchas • False positives • Adversarial manipulation • Whitelists • Lots of dead ends, pointing these out to analysts is important • Rate limiting of enrichments
  27. 27. Conclusion •Huge proliferation of network sensors and enrichment datasets •Combining this data is useful •Lots of opportunity to make security analysts better/faster • pre-gather context for user • point out gotchas/dead ends • guide analyst to best practices
  28. 28. Contact Jason Trost • @jason_trost • jason [dot] trost [AT] threatstream [dot] com •
  29. 29. Questions