This tweet came into the collection thanks to a study of #Qanon we did earlier this year. The actual inception of our current cluster hardware appears to have been on January 29th of 2019. The very earliest it could have been created was December 19th of 2018 – the release date for Elasticsearch 6.5.4.
The system is resilient to the loss of any one system, which was given an unintended test last night, with an inadvertent shutdown of one of the servers in the cluster. Recovery takes a couple of minutes given the services and virtual machines, but there was not even an interruption in processing.
Today, for a variety of reasons, we began the process of upgrading to the June 20th, 2019 release of Elasticsearch 6.8.1. There are a number of reasons for doing this:
Index Life Cycle Management (6.6)
Cross Cluster Replication (6.6)
Elasticsearch 7 Upgrade Assistant (6.6)
Rolling Upgrade To Elasticsearch 7 (6.7)
Better Index Type Labeling (6.7)
Security Features Bundled for Community Edition (6.8)
Conversion From Ubuntu to Debian Linux
We are not jumping directly to Elasticsearch 7.x due to some fairly esoteric issues involving field formats and concerns regarding some of the Python libraries that we use. Ubunt1.u has been fine for both desktop and server use, but we recently began using the very fussy Open Semantic Search, and it behaves well with Debian. Best of all, the OVA of a working virtual machine with the Netwar System code installed and running is just 1.9 gig.
Alongside the production ready Elasticsearch based system we are including Neo4j with some example data and working code. The example data is a small network taken from Canadian Parliament members and the code produces flat files suitable for import as well as native GML file output for Gephi. We ought to be storing relationships to Neo4j as we see them in streams, but this is still new enough that we are not confident shipping it.
Some questions that have cropped up and our best answers as of today:
Is Open Semantic Search going to be part of Netwar System?
We are certainly going to be doing a lot of work with OSS and this seems like a likely outcome, given that it has both Elasticsearch and Neo4j connectors. The driver here is the desire to maintain visibility into Mastodon instances as communities shift off Twitter – we can use OSS to capture RSS feeds.
Will Netwar System still support Search Guard?
Yes, because their academic licensing permits things that the community edition of Elasticsearch does not. We are not going to do Search Guard integration into the OVA, however. There are a couple reasons for that:
Doesn’t make sense on a single virtual machine.
Duplicate configs means a bad actor would have certificate based access to the system.
Eager, unprepared system operators could expose much more than just their collection system if they try to use it online.
Netdata monitoring provides new users insight into Elasticsearch behavior, and we have not managed to make that work with SSL secured systems.
We are seeking a sensible free/paid break point for this system. It’s not clear where a community system would end and an enterprise system would begin.
Is there a proper FOSS license?
Not yet, but we are going to follow customs in this area. A university professor should expect to be able to run a secure system for a team oriented class project without incurring any expense. Commercial users who want phone support will incur an annual cost. There will be value add components that will only be available to paying customers. Right now 100% of revenue is based on software as a service and we expect that to continue to be the norm.
So the BSD license seems likely.
When will the OVA be available?
It’s online this morning for internal users. If it doesn’t explode during testing today, a version with our credentials removed should be available Tuesday or Wednesday. Most of the work required to support http/https transparently was finished during first quarter. One it’s up we’ll post a link to it here and there will be announcements on Twitter and LinkedIn.
An associate earlier this week mentioned having trouble getting Open Semantic Desktop Search to behave. This system offers an intriguing collection of capabilities, including an interface for Elasticsearch. Many hours later, we are picking our way through a minefield. This project is about to shift from Debian 9 to 10, and things are in terrible disarray.
First, some words about frustration free experimentation. If you store your virtual machines on a ZFS file system you can snapshot each time you complete and install step. If something goes wrong later, the snapshot/rollback procedure is essentially instantaneous. This is dramatically more useful than exporting VMs to OVA as a checkpoint. Keep in mind the file system will be dismounted during rollback; it’s best to have some VM specific space set aside.
The project wants Debian proper, so take the time to get Debian 9.9 installed. The desktop OVA wanted a single processor and five gig of ram. Four cores and eight gig seemed to be a sensible amount for a server. Do remember to add a host-only interface under VirtualBox so you can have direct ssh and web access.2
There are some precursors that you will need to put in place before trying to install the monolithic package.
apt install celeryd
apt install python3-pip
apt install python3-celery
apt install python-flower
Celery is a task queue manager and Flower provides a graphical interface to it at port 5555. These are missing from the monolithic package. You will also need to add the following to your /etc/security/limits.conf
Now Reboot The System So The Limits Are In Effect
Now you’re ready to install the monolithic package. This is going to produce an error indicating there are packages missing. You correct this problem with this command:
apt install -f
This is going to take a long time to run, maybe ten or fifteen minutes. It will reach 99% pretty quickly – and that’s about the 50% mark in terms of time and tasks. Once this is done, shut the system down, and take a snapshot. Be patient when you reboot it, the services are complex, hefty, and took a couple minutes to all become available on our i7 test system. This is what the system looks like when fully operational.
The Celery daemon config also needs attention. The config in /etc/default/celeryd must be edited so that it is ENABLED, and the chroot to /opt/Myproject will cause a failure to start due to missing directory. It seems safe to just turn this off.
Neo4j will be bound to just localhost and will not have a password. Since we’re building a server, rather than a specialty desktop, let’s fix this, too. The file is /etc/neo4j/neo4j.conf, these steps will permit remote access.
systemctl restart neo4j
visit http://yoursolrIP:7474 and set password
visit Config area in OSS web interface, add Neo4j credentials
Having completed these tasks, reboot the system to ensure it starts cleanly. You should find the Open Semantic Search interface here:
http://<IP of VM>/search
This seems like a good stopping point, but we are by no means finished. You can manually add content from the command line with the opensemanticsearch commands:
There are still many problems to be resolved. Periodic collection from data sources is not working, and web interface submissions are problematic as well. Attempts to parse RSS feeds generate numerous parse errors. Web pages do not import smoothly from our WordPress based site as well as one hosted on the WordPress commercial site.
We will keep coming back to this area, hopefully quickly moving past the administration details, and getting into some actual OSINT collection tradecraft.
Six months ago we published An Analyst’s Environment, which describes some tools we use that are a bit beyond the typical lone gun grassroots analyst. Since then our VPS based Elasticsearch cluster has given way to some Xeon equipment in racks, which lead to Xeon equipment under desks.
Looking back over the past two months, we see a quickly maturing “build sheet” for analyst workstations. This is in no small part due to our discovery of Budgie, an Ubuntu Linux offshoot. Some of our best qualitative analysts are on Macs and they are extremely defensive of their work environment. Budgie permits at least some of that activity to move to Linux, and its thought that this will become increasingly common.
Do not assume that “I already use Ubuntu” is sufficient to evaluate Budgie. They are spending a lot of time taking off the rough edges. At the very least, put it in a VM and give it a look.
Once installed, we’re including the following packages by default:
The Hunch.ly web capture package requires Google Chrome.
Chromium provides a separate unrecorded browser.
Maltego CE link analysis package is useful even if constrained.
Evernote is popular with some of our people, Tusk works on Linux.
XMind Zen provides mind mapping that works on all platforms.
Timeline has been a long term player and keeps adding features.
Gephi data visualization works, no matter what sized screen is used.
Both Talkwalker Alerts and Inoreader feeds are RSS based. People seem to be happy with the web interface, but what happens when you’re in a place without network access. There are a number of RSS related applications in Budgie’s slick software store. Someone is going to have to go through them and see which best fits that particular use case.
There have been so many iterations of this set of recommendations, most conditioned by the desire to support Windows, as well as Mac and Linux. The proliferation of older Xeon equipment, in particular the second generation HP Z420/Z620/Z820, which start in useable condition at around $150, mean we no longer have that constraint.
Sampling of inexpensive HP Z420s on Ebay in May of 2019.
Starting with that base, 64 gig of additional memory is about $150, and another $200 will will cover a 500 gig Crucial solid state disk and the fanless entry level Nvidia GT1030.
The specific combination of the Z420 and the Xeon E5-2650L v2 has a benchmark that matches the current MacBook Pro, it will be literally an order of magnitude faster on Gephi, the most demanding of those applications, and it will happily work for hours on end without making a sound. The Mac, on the other hand, will be making about as much noise as a Shopvac after just five minutes.
That chip and some Thermal Grizzly Kryonaut should not cost you more than $60 and will take a base Z420 from four cores to ten. So there you have it – mostly free software, a workstation you can build incrementally, and then you have the foundation required to sort out complex problems.
Online conflicts are evolving rapidly and escalating. Three years ago we judged that it was best to not release a conflict oriented tool, even one that is used purely for observation. Given the events since then, this notion of not proliferating seems … quaint.
So we released the Netwar System code, the companion ELK utilities, and this week we are going to revisit the Twitter Utils, a set of small scripts that are part of our first generation software, and which are still used for some day to day tasks.
When you live with a programming language and a couple of fairly complex distributed systems, there are troubles that arise which can be dispatched almost without thought. A new person attempting to use such a system might founder on one of these, so this post is going to memorialize what is required for a from scratch install on a fresh Ubuntu 18.04 installation.
We converted to Python 3 a while ago. The default install includes Python 3.6.7, but you need pip, and git, too.
You can manually install those and they’ll all work, except for squish2, the name for our internal package that contains the code to “squish” bulky, low value fields out of tweets and user profiles. This requires special handling like so.
cd NetwarSystem/squish2 pip install -e .
If you have any errors related to urllib3, SSL, or XML, those might be subtle dependency problems. Post them as issues on Github.
There are a bunch of Elasticsearch related scripts in the ELKSG repository. You should clone them and then copy them into your path.
The ELK software can handle a simple install, or one with Search Guard. This is the simple setup, so add this final line to your ~/.profile so the scripts know where to find Elasticsearch.
You need the following four pieces of software to get the system running in standalone mode.
Redis and Netdata are simple.
apt update apt install redis
There is an install procedure for Netdata that is really slick. Copy one command, paste it in a shell, it does the install, and makes the service active on port 19999.
Elasticsearch and Neo4j require a bit more work to get the correct version. The stricken lines used to install the Oracle JDK 8, but they changed licensing in late April of 2019. The OpenJDK install seems to make its dependents happy, but we are trialing this with a new system. Our production stuff still has the last working official Oracle setup.
echo 'deb https://debian.neo4j.org/repo stable/' | tee -a /etc/apt/sources.list.d/neo4j.list
apt install neo4j=1:3.5.4
Note that the version mentioned there is just what happens to be in the Neo4j install instructions on the day this article was written. This is not sensitive the way Elasticsearch is.
At this point you should have all four applications running. The one potential problem is Kibana, which may fail to start because it depends on Elasticsearch, which takes a couple minutes to come alive the first time it is run. Try these commands to verify:
systemctl status redis systemctl status elasticsearch systemctl status kibana systemctl status neo4j
In terms of open TCP ports, try the following, which checks the access ports for Kibana, Redis, Neo4j, and Elasticsearch.
We also need to adjust the file handles and process limits upward for Elasticsearch’s Lucene component and Neo4j’s worker threads. Add these lines to /etc/security/limits.conf, and note that there are tab stops in the actual file, this looks terrible on the blog. Here it’s just best to reboot to make these settings active.
If you’re running this software on your desktop, pointing a web browser at port 5601 will show Kibana and 7474 will show Neo4j. If you’re using a standalone or virtual machine, you’ll need to open some access. Here are three one liners with sed that will do that.
sed -i 's/#network.host: 192.168.0.1/network.host: 0.0.0.0/' /etc/elasticsearch/elasticsearch.yml
sed -i 's/#server.host: \"localhost\"/server.host: 0.0.0.0/' /etc/kibana/kibana.yml
sed -i 's/#dbms.connectors.default_listen/dbms.connectors.default_listen/' /etc/neo4j/neo4j.conf
systemctl restart elasticsearch
systemctl restart kibana
systemctl restart neo4j
Elasticsearch doesn’t require a password in this configuration, but Neo4j does, and it’ll make you change it from the default of ‘neo4j’ the first time you log in to the system.
OK, point your browser at port 19999, and you should see this:
Notice the elasticsearch local and Redis local tabs at the lower right. You can get really detailed information on what Elasticsearch is doing, which is helpful when you are just starting to explore its capabilities.
Configuring Your First Twitter Account
You must have a set of Twitter application keys to take the next step. You’ll need to add the Consumer Key and Consumer Secret to the tw-auth command. Run it, paste the URL it offers into a browser, log in with your Twitter account, enter the seven digit PIN from the browser into the script, and it will create a ~/.twitter file that looks something like this.
You’ll need to enter the Neo4j password you set earlier. The elksg variable has to point to the correct host and port. The elksguser/elksgpass entries are just placeholders. If you got this right, this command will cough up your login shell name and Twitter screen name.
Next, you can check that your Elasticsearch commands are working:
Now is the time to get Elasticsearch ready to accept Twitter data. Mostly this involves making sure it recognizes timestamps. Issue these commands:
The first three ensure that timestamps work for the master user index, any tu* index related to a specific collection, and any tw* index containing tweets. The mylog command ensures the perflog indec is searchable. The last command bumps the field limit on indices. Experienced Elasticsearch users will be scratching their heads on this one – we still have much to learn here, feel free to educate us on how to permanently handle that problem.
If you want to see what these did, this command will show you a lot of JSON.
And now we’re dangerously close to actually getting some content in Elasticsearch. Try the following commands:
tw-friendquick NetwarSystem > test.txt
This should produce a file with around 180 numeric Twitter IDs that are followed by @NetwarSystem, load them into Redis for processing, and the last command will give you a count of how many are loaded. This is the big moment, try this command next:
That command should spew a bunch of JSON as it runs. The preceding time command will tell you how long it took, a useful thing when performance tuning long running processes.
Now try this one:
You should get back two very long lines of text – one for the usertest index, show about 180 documents, and the other for perflog, which will just have a few.
There, you’ve done it! Now let’s examine the results.
Your next steps require the Kibana graphical interface. Point your browser at port 5601 on your system. You’ll be presented with the Kibana welcome page. You can follow their tutorial if you’d like. Once you’ve done that, or skipped it, you will do the following:
Go the Management tab
Select Index Patterns
Create an Index Pattern for the usertest index
There should be a couple of choices for time fields – one for when the user account was created, the other is the date for their last tweet. Once you’ve done this, go to the Discover tab, which should default to your newly created Index Pattern. Play with the time picker at the upper right, find the Relative option, and set it to 13 years. You should see a creation date histogram something like this:
Writing this post involved grinding off every burr we found in the Github repositories, which was an all day job, but we’ve come to the point where you have cut & pasted all you can. The next steps will involve watching videos about how to use Kibana, laying hands on a copy of Elasticsearch: The Definitive Guide, and installing Graphileon so you can explore the Neo4j data.
Our prior work on Twitter content has involved bulk collection of the following types of data:
Tweets, including raw text suitable for stylometry.
Activity time for the sake of temporal signatures.
Mentions including temporal data for conversation maps.
User ID data for profile searches.
Follower/following relationships, often using Maltego.
Early on this involved simply running multiple accounts in parallel, each working on their own set of tasks. Seemingly quick results were a matter of knowing what to collect and letting things happen. Hardware upgrades around the start of 2019 permitted us to run sixteen accounts in parallel … then thirty two … and finally sixty four, which exceeded the bounds of 100mbit internet service.
We had never done much with the Twitter streaming API until just two weeks ago, but our expanded ability to handle large volumes of raw data has made this a very interesting proposition. There are now ten accounts engaged in collecting either a mix of terms or following lists of hundreds of high value accounts.
What we get from streams at this time includes:
RT’d tweet content.
Quoted tweet content.
Twitter user data for the source.
Twitter user data for accounts mentioned.
Twitter user data for accounts that are RT’d.
User to mentioned account event including timestamp.
User to RT’d account event including timestamp.
This data is currently accumulating in a mix of Elasticsearch indices. We recognize that we have at least three document types:
Our current setup is definitely beta at this point. We probably need more attention on the natural language processing aspect of the tweets themselves, particularly as we expand into handling multiple European languages. User data could standing having hashtags extracted from profiles, which we missed the first time around, otherwise this seems pretty simple.
The interaction data is where things become uncertain. It is good to have this content in Elasticsearch for the sake of filtering. It is unclear precisely how much we should permit to accumulate in these derivative documents; at this point they’re just the minimal data from each tweet that permits establishing the link between accounts involved. Do we also do this for hashtags?
Once we have this, the next question is what do we do with it? The search, sorting, and time slicing of Elasticsearch is nice, but this is really network data, and we want to visualize it.
Maltego is out of the running before we even start; 10k nodes maximum has been a barrier for a long time. Gephi is unusable on a 4k Linux display due to font sizing for readability, and it will do just enough on a half million node network to leave one hanging with an analysis half finished on a smaller display.
The right answer(s) seem to be to get moving on Graphistry and Neo4j. An EVGA GTX 1060 turned up here a few weeks ago, displaying a GT 1030 to an associate. Given the uptime requirements for Elasticsearch, not much has happened towards Graphistry use other than the physical install. It looks like Docker is a requirement, and that’s a synonym for “invasive nuisance”.
Neo4j has some visualization abilities but its real attraction is the native handling of storage and queries for graphs. Our associates who engage in analysis ask questions that are easily answered with Elasticsearch … and other questions that are utterly impossible to resolve with any tool we currently wield.
Expanding capacity has permitted us to answer some questions … but on the balance its uncovered more mysteries than it has resolved. This next month is going to involve getting some standard in place for assessing incoming streams, and pressing on both means of handling graph data, to see which one we can bring to bear first.
Last month we announced the Netwar System Community Edition, the OVA for which is still not posted publicly. In our defense, what should have been a couple days with our core system has turned into a multifaceted month long bug hunt. A good portion could be credited to “unfamiliar with Search Guard”, but there is a hard kernel of “WTF, Twitter, WTF?!?” that we want to describe for other analysts.
Core System Configuration
First, some words about what we’ve done with the core of the system use day to day. After much experimentation we settled on the following configuration for our Elasticsearch dependent environment.
HP Z620 workstations with dual eight core Xeons.
128 gig of ram.
Dual Seagate IronWolf two terabyte drives in a mirror.
Single Samsung SSD for system and ZFS cache.
Trio of VirtualBox VMs with 500 gig of storage each.
32 gig for host, ZFS ARC (cache) limited to 24 gig.
24 gig per VM, JVM limited to 12 to 16 gig.
There are many balancing acts in this, too subtle and too niche to dig into here. It should be noted that FreeBSD Mastery:ZFS is a fine little book, even if you’re using Linux. The IronWolf drives are helium filled gear meant for NAS duty. In retrospect, paying the 50% premium for IronWolf Pro gear would have been a good move and we’ll go that way as we outgrow these.
We’ve started with a pair of machines, we’re defaulting to three shards per index, and a single replica for each. The Elasticsearch datacenter zones feature proved useful; pulling the network cable on one machine triggers some internal recovery processes, but there is no downtime from the user’s perspective. We’re due for a third system with similar specifications, it will receive the same configuration including a zone of its own, and we’ll move from one replica per index to two. This will be a painless shift to N+1 redundancy.
API Mysteries At Scale
Our first large scale project has been profiling the followers of 577 MPs in the U.K. Parliament. There are 20.6M follow relationships with 6.6M unique accounts. Extracting their profiles would require forty hours with our current configuration … but there are issues.
Users haven’t seen a Twitter #FailWhale in years, but as continuous users of the API we expect to see periods of misbehavior on about a monthly basis. February featured some grim adjustments, signs that Twitter is further clamping down on bots, which nipped our read only analytical activities. There are some features that seem to be permanently throttled now based on IP address.
When we arrived at what we thought was the end of the road, we had 6.26M profiles in Elasticsearch rather than the 6.6M we knew to exist, a discrepancy of about 350,000. We tested all 6.6M numeric IDs against the index and found just 325,000 negative responses. We treated that set as a new batch and the system captured 255,000, leaving only 70,000 missing. Repeating the process again with the 70,000 we arrived at a place where the problem was amenable to running a small batch in serial fashion.
Watching a batch of a thousand of these stragglers, roughly a quarter got an actual response, a quarter came back as suspended, and the remainder came back as page not found. The last response is expected when an account has renamed or self suspended, but we were using numeric ID rather than screen name.
And the API response to this set was NOT deterministic. Run the process again with the same data, the percentages were similar, but different accounts were affected.
A manual inspection of the accounts returned permits the formulation of a theory as to why this happens. We know the distribution of the creation dates of these accounts:
The bulk of the problematic accounts are dated between May and August of 2018. Recall that Twitter completed its acquisition of Smyte and shut down 70 million bots during that time frame. May in the histogram is the first month where account creation dates are level. A smaller set clustered around the same day in mid-December of 2012, another fertile time period for bot creation.
The affected accounts have many of the characteristics we associate with bots:
Steeply inverted following to follow ratio.
Complete lack of relationships to others.
Relatively few tweets.
Default username with eight trailing digits.
An account that was created and quickly abandoned will share these attributes. So our theory regarding the seeming problem with the API is as follows:
These accounts that can not be accessed in a deterministic fashion using the API are in some sort of Smyte induced purgatory. They are not accessible, protected, empty of content, suspended, or renamed, which are five conditions our code already recognizes. There is a new condition, likely “needs to validate phone number”, and accounts that have not done this are only likely of interest to their botnet operators, or researchers delving very deeply into the system’s behavior.
But What Does This MEAN?
Twitter has taken aggressive steps to limit the creation of bots. Accounts following MPs seem to have fairly evenly distributed creation dates, less the massive hump from early 2016 to mid 2018. We know botnet operators are liquidating collections of accounts that have been wiped of prior activity for as little as $0.25 each. There are reportedly offerings of batches of accounts considered to be ‘premium’, but what we know of this practice is anecdotal.
Our own experience is limited to maintaining a couple platoons of collection oriented accounts, and Twitter has erected new barriers, requiring longer lasting phone numbers, and sometimes voice calls rather than SMS.
This coming month we are going to delve into the social bot market, purchasing a small batch, which we will host on a remote VPS and attempt to use for collection work.
The bigger implication is this … Twitter’s implementation of Smyte is good, but it’s created a “hole in the ocean problem”, a reference to modern submarines with acoustic signatures that are less than the noise floor in their environment. If the affected accounts are all bots, and they’re just standing deadwood of no use to anyone, that’s good. But if they can be rehabilitated or repurposed, they are still an issue.
Seems like we have more digging to do here …
Mystery Partially Resolved …
So there was an issue with the API, but an issue on our side.
When a Twitter account gets suspended, it’s API tokens will still permit you to check its credentials. So a script like this reports all is well:
But if three of the sixty four accounts used in doing numeric ID to profile lookups have been suspended … 3/64 = 4.69% failure rate. That agrees pretty well with some of the trouble we observed. We have not had cause to process another large batch of numeric IDs yet, but when we do, we’ll check this theory against the results.
This site has been quiet the last five weeks, but many good things happened in the background. One of those good things has been progress on a small Netwar System demonstrator virtual machine, tentatively named the Community Edition.
What can you do with Netwar System CE? It supports using one or two Twitter accounts to record content on an ongoing basis, making the captured information available via the Kibana graphical front end to Elasticsearch. Once the accounts are authorized the system checks them every two minutes for any list that begins with “nsce-“, and accounts on those lists are recorded.
Each account used for recording produces a tw<name> index containing tweets and a tu<name> index containing the profiles of the accounts.
The tw* and tu* are index patterns that cover the respective content from all three accounts. The root account is the system manager and we assume users might place a set of API tokens on that account for command line testing.
This is a view from Kibana’s Discovery tab. The timeframe can be controller via the time picker at the upper right, the Search box permits filtering, the activity per date histogram appears at the top, and in the case we can see a handful of Brexit related tweets.
There are a variety of visualization tools within Kibana. He we see a cloud of hashtags used by the collected accounts. The time picker can be adjusted to a certain time frame, search terms may be added so that the cloud reflects only hashtags used in conjunction with the search term, and there are many further refinements that can be made.
What does it take to run Netwar System CE? The following is a minimal configuration of a desktop or laptop that could host it:
8 gig of ram
solid state disk
four core processor
There are entry level Dell laptops on Amazon with these specifications in the $500 range.
The VM itself is very light weight – two cores, four gig of ram, and the OVA file for the VM is just over four gig to download.
As shipped, the system has the following limits:
Tracking via two accounts
Disk space for about a million tweets
Collects thirty Twitter accounts per hour per account
If you are comfortable with the Linux command line it is fairly straightforward to add additional accounts. If you have some minimal Linux administration capabilities you could add a virtual disk, relocate the Elasticsearch data, and have room for more tweets.
If you are seeking to do a larger project, you should not just multiply these numbers to determine overall capacity. An eight gig VM running our adaptive code can cover about three hundred accounts per hour and a sixty four gig server can exceed four thousand.
This week we had a chance to work with an analyst who is new to our environment. The conversation revealed some things we find pedestrian that are exciting to a new person, so we’re going to detail them.
Many people use Google’s Alerts, but far fewer are familiar with the service Talkwalker offers. This company offers social media observation tools and their free alerts service seems to be a way to gather cognitive excess, to learn what things might matter to actual humans. These alerts arrive as email, or as an RSS feed, which is a very valuable format.
Google Reader used to be a good feed reader, but it was canceled some years ago. Alternatives today include Feedly and Inoreader. The first is considered the best for day to day reading activity, while Inoreader gets high marks for archival and automation. The paid version, just $49 per year, will comfortably handle hundreds of feeds, including the RSS output from the above mentioned Talkwalker.
Talkwalker Alerts never sleep, Inoreader provides all sorts of automation, but how does one preserve some specific aspect of the overall take? We like Hunch.ly for faithful capture. This $129 tool is a Chrome extension that faithfully saves every page visited, it offers ‘selectors’, text strings that are standing queries in an ‘investigation’, which can be exported as a single zip file, which another user can then import. That is an amazingly powerful capability for small groups, who are otherwise typically trying to synchronize with an incomplete, error filled manual process.
Alerting, feed tracking, and content preservation are important, but the Hunch.ly investigation is the right quantum of information for an individual or a small group. Larger bodies of information where linkages matter are best handled with Maltego Community Edition, which is free. There are transforms (queries) that will pull information from a Hunch.ly case, but the volume of information returned exceeds the CE version’s twelve item limit.
Maltego Classic is $1,000 with a $499 annual maintenance fee. This is well worth the cost for serious investigation work, particularly when there is a need to live share data among multiple analysts.
Costs Of Doing Business
We are extremely fond of FOSS tools, but there are some specialized tasks where it simply makes no sense to try to “roll your own”. This $1,200 kit of tools is a force multiplier for any investigator, dramatically enhancing accuracy and productivity.
After Implementing Search Guard ten days ago I was finally pushed into using Elasticsearch 6. Having noticed that 6.5.0 was out I decided to wait until Search Guard, which seems to lag about a week behind, managed to get their update done.
The 6.5.0 release proved terribly buggy, but now here we are with 6.5.1, running tests in A Small Development Environment, and the results are impressive. The combination of this code and an upgrade from Ubuntu 16.04 to 18.04 has made the little test machine, which we refer to as ‘hotpot‘, as speedy as our three node VPS based cluster.
This is a solid long term average of fully collecting over eleven accounts per minute, but the curious thing is that it’s not obvious what resource is limiting throughput. Ram utilization eventually ratcheted up to 80% but the CPU load average has been not more than 20% the whole time.
There is still a long learning curve ahead, but what I think I see here is that an elderly four core i7, if it has a properly tuned zpool disk subsystem, will be able to support a group of eight users in constant collection mode.
And that makes this page of Kimsufi Servers intriguing. The KS-9 looks to be the sweet spot, due to the presence of SSDs instead of spindles. If our monthly hardware is $21 that puts us in a place where maybe a $99/month small team setup makes sense to offer.
There is much to be done with Search Guard before this can happen, but hopefully we’ll be ready at the start of 2019.
Our conversion to Elasticsearch began almost a year ago. Aided by their marvelous O’Reilly book, Elasticsearch: The Definitive Guide, we grew comfortable with the system, exploring Timesketch and implementing Wazuh for our internal monitoring. Our concerns here were the same issue we faced during prior Splunk adventures – how do we fund the annual cost of an enterprise license?
Search Guard solves the explicit cost question and it does a good job on the implicit barrier to entry problem. What you see below is the contents of the Search Guard tab on our prototype system, which more or less installed itself with single command.
The initial experience was so smooth we decided to implement Search Guard on our cluster, which has been a learning experience. The system requires Elasticsearch 6.x, but we have clung to the familiar environment of the 5.6 version of the system. The switch required a solid day of fiddling with Bash scripts and Python code in order to make everything work with the newest Elasticsearch, and then the cluster upgrade was not nearly so straightforward.
The self-installing demo reuses a PKI setup. That’s great for lowering the barrier to entry for initial experiments, but there is no way that can be used on a publicly accessible system. Having done a bit of PKI here and there, the instructions and scripts they offer are fairly smooth.
The troubles began when we moved from Elasticsearch 5.6.13 to 6.4.3. A stumble on our part during Search Guard install left us with a system that was stuck tight. Their install procedure could not continue from the state we had put the system into, while our command line tools and system knowledge were insufficient to back out of the partially completed process.
Resolving that took the better part of a day, but it proved beneficial in the end, as their voluminous documentation did not address the specific problem, but it did offer many pointers. Think: six months troubleshooting experience in an afternoon, and questions posted to their Google Group yielded authoritative answers within hours.
There are six weeks left in 2018 and during this time we are going to accomplish the following:
Index Twitter, RSS data, and one chat service for the team
Explore roles and permissions against real world considerations
Create a public facing dashboard for botnetsu.press
Implement Search Guard for a Wazuh system
Best of all, Search Guard offers a gratis Enterprise license to non-profits. We have applied for this for both botnetsu.press here in the U.S., as well as a similar effort in the U.K. Given just a bit of luck, we’ll have two teams active in the field by the end of first quarter, and maybe some of the commercial opportunities we are pursuing will come to fruition as well.