Malicious Topologies of IPv4
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The document discusses the use of internet-scale data science in cybersecurity, emphasizing the importance of improved security data and machine learning for effective threat detection. It covers the tools and methodologies being used, such as Rapid7's Sonar for internet-wide surveys and various data types like SSL certificates and IP address information. Additionally, it addresses the patterns of malicious activity in IPv4 hierarchies and presents strategies for enhancing security through better data practices.
![Quick Bios
Suchin Gururangan
@ssgrn
suchin.co
Data Scientist
Bob Rudis
@hrbrmstr
rud.is
[Master]Chief Data Scientist](https://image.slidesharecdn.com/rapid7-ipv4-srcbos-2016-160519155405/85/Malicious-Topologies-of-IPv4-2-320.jpg)
Malicious Topologies of IPv4
- 1. Malicious Topologies of IPv4 and Other Adventures in Internet-Scale Data Science Suchin Gururangan Bob Rudis Rapid7 Data Science
- 2. Quick Bios Suchin Gururangan @ssgrn suchin.co Data Scientist Bob Rudis @hrbrmstr rud.is [Master]Chief Data Scientist
- 3. Delivering Security Data & Analytics that revolutionize the practice of cyber security 37% Fortune 1000 5,100+ Customers 800+ Employees 99 Countries Threat Exposure Management Incident Detection and Response Security Advisory Services Rapid7
- 4. R7 Data Science + Labs We have an incredible team of Red teamers Data Scientists Developers
- 5. WIRED IBM Watson Brings AI Wonders to Cybersecurity Why Machine Learning Is Our Last Hope for Cybersecurity Fortune Datanami MIT builds AI system that can detect 85% of cyberattacks Business Insider Machine learning is fueling a cyber arms race
- 6. Machine learning algorithms are only as good as the data they are trained on.
- 7. Location Identification Facial Recognition Object Recognition
- 8. ModelPixels
- 11. Most security data is inconsistent short-lived adversarial biased lacking ground truth
- 12. Let's get better security data.
- 13. Data science is not just machine learning.
- 14. The place for security data science? Separate signal from noise Finding and visualizing trends Cluster hosts into groups filtering visualization organization
- 15. Internet-Scale
- 23. See trends and develop context for micro-scale attacks More data! Why Internet-Scale Data Science?
- 25. Sonar Internet-wide surveys across entire public IPV4 and a wide variety of services and protocols. Started in Nov 2013 by HD Moore
- 26. Sonar Data 443/TCP SSL Certificates 80/TCP HTTP Get/IP vhosts Reverse DNS Forward DNS UDP Probes (uPnP, IPMI, NetBIOS, etc.) POP, IMAP, SMTP
- 27. 443/TCP SSL Certs (weekly scans) ~25M SSL Certs, ~ 55GB in < 4 hours 80/TCP HTTP Get Requests (bi-weekly scans) Reverse DNS (bi-weekly scans) ~60-65M Web servers, ~1.7 TB in < 10 hours ~1.1B Records, ~50 GB < 24 hours What's out there?
- 28. Heisenberg Low-interaction, cloud-based RDP honeypots deployed across the world Over 334 days, recorded: 221203 different password attempts from 5076 distinct IP addresses across 119 different countries
- 30. Other data • BGP Archives • Blacklists: CleanMX, phishtank, malwaredomains
- 31. Malicious Topology of IPV4 • How to apply Internet-scale structure to phishing attacks? • How can this structure help us identify malicious areas of the Internet?
- 32. IPV4 Hierarchy IP Subnet AS 127.0.0.1 127.0.0/24 AS10
- 33. source: xkcd
- 37. AS Fragmentation 20.1.0/23 20.1.0/2320.1.2/23 20.1.2/24 20.1.2/24 20.1.0/2420.1.0/24 Tree Depth Fragmentation = 1 - Tree Depth / # Nodes Subnet Tree
- 38. Malicious AS Topology Size 80-95th percentile in IPv4 Fragmentation 10-20% higher Malicious Topology Benign Topology - subnet Composition 50-60% XX-small subnets
- 39. Subnet Category ARIN Fee Subnet Prefix XX-Small $500.00 < /22 X-Small $1,000.00 /22 - /20 Small $2,000.00 /20 - /18 Medium $4,000.00 /18 - /16 Large $8,000.00 /16 - /14 X-Large $16,000.00 /14 - /12 XX-Large $32,000.00 > /12
- 40. • Very few ASes host a disproportionate amount of malicious activity • Smaller subnets and ASes are becoming more ubiquitous in IPv4 • Malicious ASes are likely large and deeply fragmented Recap
- 41. With Internet-Scale security data... • We develop more informed context and bounds on local malicious activity • We make effective security ML more of a future possibility
- 42. How can I get started? • sonar.labs.rapid7.com and scans.io • blacklists: CleanMX, Phishtank, Malwaredomains • BGP archives - routeviews project • Heisenberg data coming soon