Protographs: Graph-Based Approach to NetFlow Analysis. Jeff Janies RedJack FloCon 2011
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1 Protographs: Graph-Based Approach to NetFlow Analysis Jeff Janies RedJack FloCon 2011
2 Thesis Using social networks we can complement our existing volumetric analysis. Identify phenomenon we are missing because they are just not bandwidth heavy enough. Relate behaviors in novel ways. What is really the most important host in a collection a network?
3 Social Network Analysis Demonstrates relationships through Graphs Allows us to map out interconnections. Objective measure of social importance Who connects the groups together? Who can influence communication? 3
4 Protocol Graphs Protocol Graphs Social networks of host communications. (Who talked to whom) Undirected Graphs Vertices The hosts that communicated. Edges Connects between hosts that communicated. Analyze a specific phenomenon. Ex: BotNet, P2P, Established services
5 Protograph Tool Processes raw SiLK NetFlow data. Produces protocol graphs. Only uses IP information. Reports centrality of hosts. Centrality How integral a host is to the group. 5
6 Example NetFlow SIP DIP Sport Dport Flags Bytes Pkts Stime SAF /01/01T SAF /01/01T SAR /01/01T SAR /01/01T SAR /01/01T SAR /01/01T SAR /01/01T SAR /01/01T SAR /01/01T SAR /01/01T.. 6
7 NetFlow as a Protocol Graph That NetFlow Makes this graph. No Volume. No Direction. Just Connections. Centrality Connects many Connects to the rest of the graph. If either removed, the graph is no longer fully connected. 7
8 Centrality A measure of social importance. Betweenness How efficiently a vertex connects the graph. (protograph) Degree How many vertices are connected to the vertex. (SiLK rwuniq) Closeness How close a vertex is to other vertices. Eigenvector How important a vertex is.
9 Betweenness Which hosts provide the most shortest paths through the network? g ij Geodesic paths through host i and j. G ikj Geodesic paths through host k for i and j.
10 Interpretation The higher the centrality value the more "important a host is to the graph. Without a central node the graph will break down into unconnected groups. (The protocol is effected) Example: If we have all a sample of P2P traffic, centrality tells us which host to remove to cause the most damage to the overlay s QoS. Not necessarily which host is the most talkative. 10
11 Volume & Betweenness Spikes in centrality may exist without spikes in bandwidth. Centrality measures something not tied to volume. Sample data: One week long sample of TCP/IP traffic. Ephemeral port to ephemeral port. >1K bytes, >4 packets. Divided into intervals of 60, 30, and 15 minutes.
12 Volume measures
13 Betweenness Centrality
14 Betweenness Centrality Spike 1 Spike 2
15 Volume measures Spike 1 Spike 2
16 Spike 1 3 hosts have 4x the centrality measure of any host measured at any other time. all three part of same phenomenon. One host was a scan victim of two unrelated hosts. The only overlap in scan victims was this host. One scanned ~37,000 destinations on port 20,000. (usermin exploit) One SA scanned ~3,500 destinations. (various ports)
17 Spike 2 1 host has 3x the centrality of any other host measured at any other time. Contacts 20,000 hosts that connect a graph of 31,000 hosts. Active for 6 minutes and sent out 17 million packets. Scanner.
18 Second Data Sample Increased resolution to one minute intervals. One Week of TCP/IP ephemeral port to ephemeral port traffic: >120 bytes per direction. >3 packets. Contains at least a SYN and ACK flag in the OR of observed Flags.
19 Betweenness and Degree Comparing centralities gives richer understanding of hosts relationships. Examine hosts that have high Betweenness with modest Degree. Hosts that are important without being directly connected to many other hosts.
20 Volume Vs. Centralities
21 Only Betweenness Spikes Recorded each IP address max Degree and Betweenness values. Divided spikes, or exceedingly high Betweenness centralities into strata. High (>10,000) - All IP addresses also had comparatively high Degree centrality. Low (>1,000 and <10,000) - We investigated 11 IP addresses that had spikes in Betweenness without comparatively high Degree.
22 High Betweenness Low Degree 9 victims of vulnerability scans. Vulnerability scans requiring full connections. Scanner connects them to a lot of hosts. 1 contacted a host that contacted everything. It provides a service for a promiscuous host. 1 connected several of the hosts with high Degree and Betweenness centrality. Connecting segments of a P2P network. Easily identified high value asset to the P2P network.
23 Summary Social network analysis: Identifying components of a behavior. Complementary tool to volumetric measures. It does not consider direction or volume. Still a great deal of tuning required to make this into an actionable utility. 23
24 References Stephen P. Borgatti, Centrality and Network flow, Social Neworks, Vol. 27, No
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