Monthly Archives: October 2014

Poor Man’s Risk Visualization II

Categorizing and clumping (aggregating) simple exposure data from the Shodan database can help communicate some risks that otherwise might have been missed.  Even with the loss of some accuracy (or maybe because of loss of accuracy), grouping some data into larger buckets can help communicate risk/exposure. For example, a couple of posts ago in Poor Man’s Industrial Control System Visualization, Shodan data was used to do a quick visual analysis of what ports and services are open on publicly available IP addresses for different organizations. Wordle was used to generate word clouds and show relative frequency of occurrence where ‘words’ where actually port/service numbers.

Trading-off some accuracy for comprehension

This is great for yourself or colleagues that are also fairly familiar with port numbers, the services that they represent, and what their relative frequencies might imply. However, often we’re trying to communicate these ideas to business people and/or senior management. Raw port numbers aren’t going to mean much to them. A way to address this is to pre-categorize the port numbers/services so that some of them clump together.

Yes, there is a loss of some accuracy with this approach — whenever we generalize or categorize, there is a loss of information.  However, when the domain-specific information makes it difficult or impossible to communicate to another that does not work in that domain (with some interesting parallels to the notion of channel capacity), it’s worth the accuracy loss so that something useful gets communicated. Similar to the earlier post of port/service numbers only, one organization has this ‘port number cloud’:


A fair amount of helpful quick-glance detail consumable by the IT or security professional, but not much help to the non-IT professional

Again, this might have some utility to an IT or security professional, but not much to anyone else. However, by aggregating some of the ports returned into categories and using descriptive words instead, something more understandable by business colleagues and/or management can be rendered:


For communicating risk/exposure, this is a little more readable & understandable to a broader audience, especially business colleagues & senior management

How you categorize is up to you. I’ll list my criteria below for these examples. It’s important not to get too caught up in the nuance of the categorization. There are a million ways to categorize and many ports/services serve a combination of functions. You get to make the cut on these categories to best illustrate the message that you are trying to get across. As long as you can show how you went about it, then you’re okay.


One way to categorize ports — choose a method that best helps you communicate your situation

The port number and ‘categorized’ clouds for a smaller organization with less variety are below.



A port number ‘cloud’ for a different (and smaller) organization with less variety in port/service types


The same port/service categorization as used above, but for the smaller organization, yields a very different looking word cloud

One challenge with the more clear approach is that your business colleagues or senior management might leap to a conclusion that you don’t want them too. For example, you will need to be prepared for the course of action that you have in mind. You might need to explain, for example, that though there are many web servers in your organization, your bigger concern might be exposure of telnet and ftp access, default passwords, or all of the above.

This descriptive language categorization approach can be a useful way to demonstrate port/service exposure in your organization, but it does not obviate the need for a mitigation plan.

Borrowing from search to characterize network risk

Most frequently occurring port is in outer ring, 2nd most is next ring in, ...

Most frequently occurring port is in outer ring, 2nd most is next ring in, …

Borrowing some ideas from document search techniques, data from the Shodan database can be used to characterize networks at a glance. In the last post, I used Shodan data for public IP spaces associated with different organizations and Wordle to create a quick and dirty word cloud visualization of exposure by port/service for that organization.

The word cloud idea works pretty well in communicating at a glance the top two or three ports/services most frequently seen for a given area of study (IP space).  I wanted to extend this a bit and compare organizations by a linear rank of the most frequently occurring services seen on that organization’s network.  So I wanted to capture both the most frequently occurring ports/services as well as the rank amongst those and then use those criteria to potentially compare different organizations (IP spaces).

Vector space model

I also wanted to experiment with visualizing this in a way that would give at a glance something of a ‘signature’.  Sooooo, here’s the idea: document search often uses this idea of a vector space model where documents are broken down into vectors.  The vector is a list of words representing all of the words that occur in that document.  The weight given to each word (or term or element) in the vector can be computed in a number of different ways, but one of the most popular is frequency with which that word occurs in that document (and sometimes with which it occurs in all of the documents combined).

A similar idea was used here, except that I used frequency with which ports/services appeared in an organization instead of words in a document. I looked at the top 5 ports/services that appeared.  I also experimented with the top 10 ports/services, but that got a little busy on the graphic and it also seemed that as I moved further down the ordered port list — 8th most frequent, 9th most frequent, etc — that these additional ports were adding less and less to the characterization of the network. Could be wrong, but it just seemed that way at the time.

I went through 12 organizations and collected the top 5 ports/services in each. Organizations varied between approximately 10,000 and 50,000 IP addresses. To have a basis for comparison of each organization, I used a list created by the ports returned from all of the organizations’ Top 5 ports.

Visualizing port rank ‘signatures’

A polar plot was created where each radial represents each port/service.  The rings of the plot represent the rank of that port — most frequently occurring, 2nd most frequently occurring, …, 5th most frequently occurring. I used a polar plot because I wanted something that might generate easily recognizable shapes or patterns. Another plot could have been used, but this one grabbed my eye the most.

Finally, to really get geeky, to measure similarity in some form, I computed the Euclidean distance between each possible vector pair. Two of the closest organizations of the 12 analyzed are (that is most similar port vectors):



2 of the most similar organizations by Euclidean distance — ports 21, 23, & 443 show up with the same rank & port 80 shows up with a rank difference of only 1. This makes them close.  (Euclidean distance of ~2.5)

Two of the furthest way of the 12 studied are these (least similar port vectors):



While port 80 aligns between the two (has the same rank) and port 22 is close in rank between the two, there is no alignment between ports 23, 3389, or 5900. This non-alignment, non-similar port rank, creates more distance between the two. (Euclidean distance of ~9.8)

Finally, this last one is some where in the middle (mean) of the pack:



A distance chosen from the middle of the sorted distance (mean). Euclidean distance is ~8.7. Because this median value is much closer to the most dissimilar, it seems to indicate a high degree of dissimilarity across the set studied (I think).

Overall, I liked the plots. I also liked the polar approach. I was hoping that I would see a little more of a ‘shape feel’, but I only studied 12 organizations.  I’d like to add more organizations to the study and see if additional patterns emerge. I also tried other distance measuring methods (Hamming, cosine, jaccard, Chebyshev, cityblock, etc) because they were readily available and easy to use with the scipy library that I was using, but none offered a noticeable uptick in utility over the plain Euclidean measure.

Cool questions from this to pursue might be:

1. For similar patterns between 2 or more organizations, can history of network development be inferred? Was a key person at both organizations at some point? Did one org copy another org?

2. Could the ranked port exposure lend itself to approximating risk for combined/multiprong cyber attack?

Again, if you’re doing similar work on network/IP space characterization and want to share, please contact me at ChuckBenson at this website’s domain for email.

Poor Man’s Industrial Control System Risk Visualization

The market is exploding with a variety of visualization tools to assist with ‘big data’ analysis in general and security and risk awareness analysis efforts in particular. Who the winner is or winners are in this arena is far from settled and it can be difficult to figure out where to start. While we analyze these different products and services and try some of our own approaches, it is good to keep in mind that there can also be some simple initial value-add in working with quick and easy, nontraditional (at least in this context), visualization

Even simple data visualization can be helpful

I’ve been working with some Shodan data for the past year or so. Shodan, created by John Matherly, is a service that scans several ports/services related to Industrial Control Systems (ICS) and, increasingly, Internet of Things sorts of devices and systems. The service records the results of these scans and puts them in a web accessible database. The results are available online or via a variety of export formats to include csv, json, and xml (though xml is deprecated). In his new site format, Matherly also makes some visualizations of his own available. For example, here’s one depicting ranked services for a particular subset of IP ranges that I was analyzing:

Builtin Shodan visualization -- Top operating systems in scan

One of the builtin Shodan visualizations — Top operating systems

Initially, I wanted to do some work with the text in the banners that Shodan returns, but I found that there was some even simpler stuff that I could do with port counts (number of times a particular port shows up in a subset of IP addresses) to start. For example, I downloaded the results from a Shodan scan, counted the occurrences for each port, ran a quick script to create a file of repeated ‘words’ (actually port numbers), and then dropped that into a text box on Wordle.

Inexpensive (free) data visualization tools

Wordle is probably the most popular web-based way of creating a word cloud. You just paste your text in here (repeated ports in our case):

Just cut & paste ports

Just cut & paste ports into Wordle

Click create and you’ve got a word cloud based on the number of ports/services in your IP range of interest. Sure you could look at this in a tabular report, but to me, there’s something about this that facilitates increased reflection regarding the exposure of the IP space that I am interested in analyzing.



VNC much? Who says telnet is out of style ?

[For some technical trivia, I did this by downloading the Shodan results into a json file, used python to import, parse, and upload to a MySQL database, and then ran queries from there. Also, Wordle uses Java so it didn’t play well with Chrome and I switched to Safari for Wordle.]

In addition to quickly eyeball-analyzing an IP space of interest, it can also make for interesting comparisons between related IP spaces. Below are two word clouds for organizations that have very similar missions and staff make up. You would, I did anyway, expect their relative ports counts and word clouds to be fairly similar. As the results below show, however, they may be very different.


Organization 1’s most frequently found ports/services


Organization 2’s most frequent ports/services — same mission and similar staffing as Org 1, but network (IP space) has some significant differences

Next steps are to explore a couple of other visualization ideas of using port counts to characterize IP spaces and then back to the banner text analysis. Hopefully, I’ll have a post on that up soon.

If you’re doing related work, I would be interested in hearing about what you’re exploring.