A new version of AfterGlow is ready. Version 1.6.5 has a couple of improvements:
1. If you have an input file which only has two columns, AfterGlow now automatically switches to a two-node mode. You don’t have to use the (-t) switch explicitly anymore in this case! (I know, it’s about time I added this)
2. Very minor change, but something that kept annoying me over time is the default edge length. It was set to 3 initially and now it’s reduced to 1.5, which makes fro a bit more compact graphs. You can still change this with the -e switch on the command line
3. The major change is about adding edge label though. Here is a quick example:
This assumes that the third column of your data contains the label for the data. In the example below, the port numbers:
When you run afterglow, use the -t switch to have it render only two nodes, but given the configuration above, we are using the third column as the edge label. The output will look like this:
As you can see, we have twice the same edge defined in the data with two different labels (port 53 and 80). If you want to have the graph show both edges, you add the following configuration in the configuration file:
Which then results in the following graph:
Note that the duplicating of edges only works with GDF files (-k). The edge labels work in DOT and GDF files, not in GraphSON output.
If you have been interested and been following event interchange formats or logging standards, you know of CEF and CEE. Problem is that we lost funding for CEE, which doesn’t mean that CEE is dead! In fact, I updated the field dictionary to accommodate some more use-cases and data sources. The one currently published by CEE is horrible. Don’t use it. Use my new version!
Whether you are using CEE or any other logging standard for your message formatting, you will need a naming schema; a set of field names. In CEE we call that a field dictionary.
The problem with the currently published field dictionary of CEE is that it’s inconsistent, has duplicate field names, and is missing a bunch of field names that you commonly need. I updated and cleaned up the dictionary (see below or download it here.) Please email me with any feedback / updates / additions! This is by no means complete, but it’s a good next iteration to keep improving on! If you know and use CEF, you can use this new dictionary with it. The problem with CEF is that it has to use ArcSight’s very limited field schema. And you have to overload a bunch of fields. So, try using this schema instead!
I was emailing with my friend Jose Nazario the other day and realized that we never really published anything decent on the event taxonomy either. That’s going to be my next task to gather whatever I can find in notes and such to put together an updated version of the taxonomy with my latest thinking; which has emerged quite a bit in the last 12 years that I have been building event taxonomies (starting with the ArcSight categorization schema, Splunk’s Common Information Model, and then designing the CEE taxonomy). Stay tuned for that.
For reference purposes. Here are some spin-offs from CEE which have field dictionaries as well:
As I outlined in my previous blog post on How to clean up network traffic logs, I have been working with the VAST 2013 traffic logs. Today I am going to show you can load the traffic logs into Impala (with a parquet table) for very quick querying.
First off, Impala is a real-time search engine for Hadoop (i.e., Hive/HDFS). So, scalable, distributed, etc. In the following I am assuming that you have Impala installed already. If not, I recommend you use the Cloudera Manager to do so. It’s pretty straight forward.
First we have to load the data into Impala, which is a two step process. We are using external tables, meaning that the data will live in files on HDFS. What we have to do is getting the data into HDFS first and then loading it into Impala:
Now we have a table called ‘logs’ that contains all of our data. We told Impala that the data is comma separated and told it where the data files are. That’s already it. What I did on my installation is leveraging the columnar data format of Impala to speed queries up. A lot of analytic queries don’t really suit the row-oriented manner of databases. Columnar orientation is much more suited. Therefore we are creating a Parquet-based table:
create table pq_logs like logs stored as parquetfile;
insert overwrite table pq_logs select * from logs;
The second command is going to take a bit as it loads all the data into the new Parquet table. You can now issues queries against the pq_logs table and you will get the benefits of a columnar data store:
select distinct firstseendestpor from pq_logs where morefragment=1;
Have a look at my previous blog entry for some more queries against this data.
I have spent some significant time with the VAST 2013 Challenge. I have been part of the program committee for a couple of years now and have seen many challenge submissions. Both good and bad. What I noticed with most submissions is that they a) didn’t really understand network data, and b) they didn’t clean the data correctly. If you wanna follow along my analysis, the data is here: Week 1 – Network Flows (~500MB)
Just because it says port 0 in there doesn’t mean it’s port 0! Check out field 5, which says OTHER. That’s the transport protocol. It’s not TCP or UDP, so the port is meaningless. Most likely this is ICMP traffic!
On to another problem with the data. Some of the sources and destinations are turned around in the traffic. This happens with network flow collectors. Look at these two records:
The first one is totally legitimate. The source port is 9130, the destination 80. The second record, however, has the source and destination turned around. Port 14545 is not a valid destination port and the collector just turned the information around.
The challenge is on you now to find which records are inverted and then you have to flip them back around. Here is what I did in order to find the ones that were turned around (Note, I am only using the first week of data for MiniChallenge1!):
What I am looking for here are the top destination ports. My theory being that most valid ports will show up quite a lot. This gave me a first candidate list of ports. I am looking for two things here. First, the frequency of the ports and second whether I recognize the ports as being valid. Based on the frequency I would put the ports down to port 3389 on my candidate list. But because all the following ones are well known ports, I will include them down to port 21. So the first list is:
I’ll drop 0 from this due to the comment earlier!
Next up, let’s see what the top source ports are that are showing up.
See that? Port 80 is the top source port showing up. Definitely a sign of a source/destination confusion. There are a bunch of others from our previous candidate list showing up here as well. All records where we have to turn source and destination around. But likely we are still missing some ports here.
Well, let’s see what other source ports remain:
select firstseensrcport, count(*) c from pq_logs2 group by firstseensrcport
having firstseensrcport<1024 and firstseensrcport not in (0,123,138,137,80,25,53,21)
order by c desc limit 10
| firstseensrcport | c |
| 62559 | 579953 |
| 62560 | 453727 |
| 51358 | 366650 |
| 51357 | 342682 |
| 45032 | 288301 |
| 62561 | 256368 |
| 45031 | 227789 |
| 51359 | 180029 |
| 45033 | 157071 |
| 45034 | 117760 |
Looks pretty normal. Well. Sort of, but let’s not digress. But lets try to see if there are any ports below 1024 showing up. Indeed, there is port 20 that shows, totally legitimate destination port. Let’s check out the. Pulling out the destination ports for those show nice actual source ports:
That would hint at this guy being actually a destination port. You can also query for all the records that have the destination port set to 1984, which will show that a lot of the source ports in those connections are definitely source ports, another hint that we should add 1984 to our list of actual ports. Continuing our journey, I found something interesting. I was looking for all connections that don’t have a source or destination port in our candidate list and sorted by the number of occurrences:
This is strange in so far as this source port seems to connect to totally random ports, but not making any sense. Is this another legitimate destination port? I am not sure. It’s way too high and I don’t want to put it on our list. Open question. No idea at this point. Anyone?
Moving on without this 62559, we see the same behavior for 62560 and then 51357 and 51358, as well as 45031, 45032, 45033. And it keeps going like that. Let’s see what the machines are involved in this traffic. Sorry, not the nicest SQL, but it works:
We see one dominant IP here. Probably another ‘attacker’. So we exclude that and see what we are left with. Now, this is getting tedious. Let’s just visualize some of the output to see what’s going on. Much quicker! And we only have 36970 records unaccounted for.
What you can see is the remainder of traffic. Very quickly we see that there is one dominant IP address. We are going to filter that one out. Then we are left with this:
I selected some interesting traffic here. Turns out, we just found another destination port: 5535 for our list. I continued this analysis and ended up with something like 38 records, which are shown in the last image:
I’ll leave it at this for now. I think that’s a pretty good set of ports:
Knowing me, you might be able to guess the topic I chose to present: Visual Analytics. I am focussing on not the visualization layer or the data layer, but on the analytics layer. In the presentation I am showing what we have been doing with data analytics and data mining in cyber security. I am showing some examples for three topics:
Exploration and Discovery
At the end, I am presenting a number of challenges to the community; hard problems that we need help with to advance insights into cyber security of infrastructures and applications. The following slide summarizes the challenges I see in data mining for security:
If you have any suggestions on each of the challenges, please contact me or comment on this post!
This is a slide I built for my Visual Analytics Workshop at BlackHat this year. I tried to summarize all the SIEM and log management vendors out there. I am pretty sure I missed some players. What did I miss? I’ll try to add them before the training.
Here is the list of vendors that are on the slide (in no particular order):
Logging as a Service
Update: With input from a couple of folks, I updated the slide a couple of times.
There are cases where you need fairly sophisticated logic to visualize data. Network graphs are a great way to help a viewer understand relationships in data. In my last blog post, I explained how to visualize network traffic. Today I am showing you how to extend your visualization with some more complicated configurations.
This blog post was inspired by an AfterGlow user who emailed me last week asking how he could keep a list of port numbers to drive the color in his graph. Here is the code snippet that I suggested he use:
variable=@ports=qw(22 80 53 110);
color="green" if (grep(/^\Q$fields\E$/,@ports))
Put this in a configuration file and invoke AfterGlow with it:
perl afterglow.pl -c file.config | ...
What this does is color all nodes green if they are part of the list of ports (22, 80, 53, 110). I am using $fields to reference the first column of data. You could also use the function fields() to reference any column in the data.
Another way to define the variable is by looking it up in a file. Here is an example:
variable=open(TOR,"tor.csv"); @tor=; close(TOR);
color="red" if (grep(/^\Q$fields\E$/,@tor))
This time you put the list of items in a file and read it into an array. Remember, it’s just Perl code that you execute after the variable= statement. Anything goes!
I am curious what you will come up with. Post your experiments and questions on secviz.org!
Have you ever collected a packet capture and you needed to know what the collected traffic is about? Here is a quick tutorial on how to use AfterGlow to generate link graphs from your packet captures (PCAP).
I am sitting at the 2012 Honeynet Project Security Workshop. One of the trainers of a workshop tomorrow just approached me and asked me to help him visualize some PCAP files. I thought it might be useful for other people as well. So here is a quick tutorial.
To start with, make sure you have AfterGlow installed. This means you also need to install GraphViz on your machine!
First Visualization Attempt
The first attempt of visualizing tcpdump traffic is the following:
I am using the tcpdump2csv parser to deal with the source/destination confusion. The problem with this approach is that if your output format is slightly different to the regular expression used in the tcpdump2csv.pl script, the parsing will fail [In fact, this happened to us when we tried it here on someone else's computer].
It is more elegant to use something like Argus to do this. They do a much better job at protocol parsing:
Big data doesn’t help us to create security intelligence! Big data is like your relational database. It’s a technology that helps us manage data. We still need the analytical intelligence on top of the storage and processing tier to make sense of everything. Visual analytics anyone?
A couple of weeks ago I hung out around the RSA conference and walked the show floor. Hundreds of companies exhibited their products. The big topics this year? Big data and security intelligence. Seems like this was MY conference. Well, not so fast. Marketing does unfortunately not equal actual solutions. Here is an example out of the press. Unfortunately, these kinds of things shine the light on very specific things; in this case, the use of hadoop for security intelligence. What does that even mean? How does it work? People seem to not really care, but only hear the big words.
Here is a quick side-note or anecdote. After the big data panel, a friend of mine comes up to me and tells me that the audience asked the panel a question about how analytics played into the big data environment. The panel huddled, discussed, and said: “Ask Raffy about that“.
Back to the problem. I have been reading a bunch lately about SIEM being replaced or superseded by big data infrastructure. That’s completely and utterly stupid. These are not competing technologies. They are complementary. If anything, SIEM will be replaced by some other analytical capabilities that are leveraging big data infrastructures. Big data is like RDBMS. New analytical capabilities are like the SIEMs (correlation rules, parsed data, etc.) For example, using big data, who is going to write your parsers for you. SIEMs have spent a lot of time and resources on things like parsers, big data solutions will need to do the same! Yes, there are a couple of things that you can do with big data approaches and unparsed data. However, most discussions out there do not discuss those uses.
In the context of big data, people also talk about leveraging multiple data sources and new data sources. What’s the big deal? We have been talking about that for 6 years (or longer). Yes, we want video feeds, but how do you correlate a video with a firewall log? Well, you process the video and generate events from it. We have been doing that all along. Nothing new there.
What HAS changed is that we now have the means to store and process the data; any data. However, nobody really knows how to process it.
The visualization maturity scale can be used to explain a number of issues in the visual analytics space. For example, why aren’t companies leveraging visualization to analyze their data? What are the requirements to implement visual analytics services? Or why don’t we have more visual analytics products?
About three years ago I posted the log management maturity scale. The maturity scale helped explain why companies and products are not as advanced as they should be in the log management, log analysis, and security information management space.
While preparing my presentation for the cyber security grand challenge meeting in early December, I developed the maturity scale for information visualization that you can see above.
Companies that are implementing visualization processes move from through each of the steps from left to right. So do product companies that build visualization applications. In order to build products on the right-hand side, they need to support the pieces to the left. Let’s have a look at the different stages in more detail:
Data Collection: No data, no visuals (see also Where Data Analytics and Security Collide). This is the foundation. Data needs to be available and accessible. Generally it is centralized in a big data store (it used to be relational databases and that’s a viable solution as well). This step generally involves parsing data. Turning unstructured data or semi-structured data into structured data. Although a fairly old problem, this is still a huge issue. I wonder if anyone is going to come up with a novel solution in this space anytime soon! The traditional regular expression based approach just doesn’t scale.
Data Analysis: Once data is centralized or accessible via a federated data store, you have to do something with it. A lot of companies are using Excel to do the first iteration of data analysis. Some are using R, SAS, or other statistics and data analytics software. One of the core problems here is data cleansing. Another huge problem is understanding the data itself. Not every data set is as self explanatory as sales data.
Context Integration: Often we collect data, analyze it, and then realize that the data doesn’t really contain enough information to understand it. For example in network security. What does the machine behind a specific IP address do? Is it a Web server? This is where we start adding more context: roles of machines, roles of users, etc. This can significantly increase the value of data analytics.
Visualization: Lets be clear about what I refer to as visualization. I am using visualization to mean reporting and dashboards. Reports are static summaries of historical data. They help communicate information. Dashboards are used to communicate information in real-time (or near real-time) to create situational awareness.
Visual Analytics: This is where things are getting interesting. Interactive interfaces are used as a means to understand and reason about the data. Often linked views, brushing, and dynamic queries are key technologies used to give the user the most freedom to look at and analyze the data.
Collaboration: It is one thing to have one analyst look at data and apply his/her own knowledge to understand the data. It’s another thing to have people collaborate on data and use their joint ‘wisdom’.
Dissemination: Once an analysis is done, the job of the analyst is not. The newly found insights have to be shared and communicated to other groups or people in order for them to take action based on the findings.
Put in Action: This could be regarded as part of the dissemination step. This step is about operationalizing the information. In the case of security information management, this is where the knowledge is encoded in correlation rules to catch future instances of the same or similar incidents.
For an end user, the visualization maturity scale outlines the individual steps he/she has to go through in order to achieve analytical maturity. In order to implement the ‘put in action’ step, users need to implement all of the steps on the left of the scale.
For visualization product companies, the scale means that in order to have a product that lets a user put findings into action, they have to support all the left-hand stages: there needs to be a data collection piece; a data storage. The data needs to be pre-analyzed. Operations like data cleansing, aggregation, filtering, or even the calculation of certain statistical properties fall into this step. Context is not always necessary, but often adds to the usefulness of the data. Etc. etc.
There are a number of products, both open source, as well as commercial solutions that are solving a lot of the left hand side problems. Technologies like column-based data bases (e.g., MongoDB) or map reduce (e.g., Hadoop), or search engines like ElasticSearch are great open source examples of such technologies. In the commercial space you will find companies like Karmaspehre or DataMeer tackling these problems.