HpfeedsHoneyGraph - Automated Attack Graph Construction for Hpfeeds Logs
11 Sep 2012 Julia Yuchin Cheng attack-graph d3-v2 gsoc gsoc-2012-d67
I cannot tell enough how fortunate I am to have Chris Horsley from Australia chapter on guiding me to achieve project goal. Without him, I definitely cannot finish my project.
Code is available at : https://github.com/yuchincheng/HpfeedsHoneyGraph
A large amount of honeypot logs from Hpfeeds result in difficulties in data analysis and interpretation. In order to solve this problem and make logs easy to explain, this project is to implement a Splunk APP, named as HpfeedsHoneyGraph, for constructing attack graph from multi-sources to provide a comprehensive attack scenario.
HpfeedsHoneyGraph collects honeypot logs from hpfeeds and indexes the logs. Pre-processing extracts useful fields, run automatically GeoIP for IP addresses and DNSLookup for hostname to get country information. When given a hostname, Fast-Flux module resolves IP addresses and continue to run passive DNS replication. Furthermore, we use force-based and node-line tree of D3.v2.js graph drawings to present three attack graphs.
Data Processing Flow
As described on last figure, we apply GeoIP, DNSLookup, fast-flux module and passive DNS replication to further process hpfeeds logs. Then, we can get multi-sources data to construct graph.
Fast-Flux Module: ffdomainip
ffdomainip includes pffdetect and BFK edv-consulting sub-modules. pffdetect is to resolve fast-fluxing IPs with domains, and BFK edv-consulting is to do passive DNS replication for fast-fluxing IPs extracted from pffdetect.
While malicious hostname extracted from Cuckoo Report, the first we want to know is “does malicious hostname belong to fast-flux domain? “. If the answer is YES, we hope we can explore all fast-fluxing IP addresses. For this purpose, we integrate pffdetect module into SPLUNK HpfeedsHoneyGraph App as an external command. Pffdetect is a python fast-flux domain detector. Given a domain, pffdetect can check if fast-flux domain and find all resolved IPs.
Following the pffdetect, we continue to do passive DNS replication of fast-flux IPs by BFK edv-consulting. Given a fast-flux IP address, query BFK database to display known A RRs for this IP address and CNAME RRs.
The following figure runs ffdomainip external command to explore fast-fluxing IPs and passive DNS replication.
Attack Graph 1 : Domains and IP addresses connected to malware
This forced-based attack graph shows the comprehensive malicious hostname and resolved IPs relationships connected to malware MD5.
- Central red dot: the circle means cuckoo reports.
- Green dot: Malware samples MD5
- Black dot: Malcious hostname extracting from cuckoo reports
- Dark red dot: Resolved IPs or fast-flux IPs by ffdomain external module
The most important we did in this graph is to make nodes unique. It helps us easily find the highly-connected node called it “weak point”. The weak point is the domains or IP addresses are connected to lots of malwares.
It is quite interesting when observing this graph. We found that different shapes of subgraph could present different stories.
Attack Graph 2 :Thug_hopping: Malicious websites interlinks
This graph is to display the interlinks from landing site, hopping site to malware downloading.
- Green dot: landing site detected by thug
- Black dot: hopping site (referer URL from thug.events)
- Dark blue dot: malware MD5
From observing the graph, some thoughts come in my mind. This graph could help study the complexity of malicious websites through interlink analysis.
Attack Graph 3 : By Country: Malicious Activities on selected country
This graph displays malicious activities related to selected country on specific time duration.
Malicious activities include (1)malicious IPs (sites): The IP address on selected country are used to host malicious pages or malwares. (2) malicious hostnames: Analyzed from malware samples using Cuckoo sandboxing. We also use node-link tree to show malicious activities for each IP address.