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====== TLS Fingerprinter ======


TLS Fingerprinting is a technique by which you can identify SSL/TLS clients.  We have launched a new [[https://trisul.org|Trisul App]] 
 that adds this capability to TrisulNSM. This page discusses this new App.

{{ :app:fp.png?nolink |}}


===== What is TLS Fingerprinting =====

This technique builds upon the patterns found in the client preferences that are advertised in the "Client Hello" message that is sent as the very first message in the TLS Handshake process. This message is un-encrypted, therefore it allows for NSM tools like Trisul to look at it.  Each SSL/TLS Client such as browsers use a particular version of a particular implementation of SSL/TLS. Some major implementations are OpenSSL, GnuTLS, Windows, Java SSE, NSS, embedded libraries like WolfSSL etc. 

The 3 major fields in the Client Hello that can identify a client are 

  - **Ciphersuite** : A ciphersuite is a combination of the Key Exchange+Encryption. The list of ciphersuites and the preferences usually change per implementation and by version. A few clients specifically prefer certain ciphersuites. For example Chrome is increasingly in favour of ChaCha20-Poly1305. Most other apps such as malware may just use the defaults that the library provides.
  - **Extensions** : Which TLS extensions are supported and in what order are they seen in the message.  This depends on the SSL library type and version.  Some advanced clients like Chrome dynamically use extensions to check for SPDY support for example. 
  - **Elliptical Curves**: There are about 25 Elliptical Curve types registered by IANA. This number and preference will also vary by client.

So if you take all the three together there is a high likelyhood that you can minimize collisions and identify a particular client on a particular operating system.If you printed out the values of all these three fields and then computed a MD5 hash over the string, we can get a 'fingerprint'.  We can call this a //JA3 hash// as proposed by the researchers John Althouse and team.  

At first, this approach may seem a bit flaky but it is not easy for an application to change its //print// dynamically without some major code rewrite. We currently have about 300 hashes largely due to Lee Brotherston's original work. We at Trisul also added in some 50 hashes. 

Links 
  * [[https://github.com/synackpse|Lee Brotherstons work]] on TLS Fingerprinting
  * [[https://github.com/salesforce/ja3|JA3 Hash]]  

==== Fingerprints database ====

The fingerprints database we have at our [[https://raw.githubusercontent.com/trisulnsm/trisul-scripts/master/lua/frontend_scripts/reassembly/ja3/prints/ja3fingerprint.json|Github repo]] contains some 450 signatures in a JSON file.  Each print is on a single line.  If you captured on a live network the JA3 hash ''d82cbe0b93f2b02d490a14f6bc1d421a'' you can flag that as ''PaleMoon Browser 27.4.2''

<code>
{"desc":"iPad; CPU OS 9_3_5  Safari 601.1", "ja3_hash":"a9aecaa66ad9c6cfe1c361da31768506", "ja3_str":"771,255-49196-49195-49188-49187-49162-49161-49160-49200-49199-49192-49191-49172-49171-49170-157-156-61-60-53-47-10,0-10-11-13-5-18,23-24-25,0" },

{"desc":"PaleMoon Browser;  PaleMoon/27.4.2", "ja3_hash":"d82cbe0b93f2b02d490a14f6bc1d421a", "ja3_str":"771,4865-4867-4866-49195-49199-52393-52392-49196-49200-49162-49161-49171-49172-69-136-157-47-65-53-61-132,0-65281-10-11-35-16-5-40-43-13-45,29-23-24-25-256-257-258-259-260,0" },

</code>



===== Analysis of TLS Fingerprints =====
 
What are you going to do with these prints. There are a few options 

  * **Malware prints**  - These are hard to come by  but if you can get a few prints from malware clients. You can flag them quickly. These will likely evade IDS because they use TLS to connect to presumably well known C&C servers with valid certificates etc.
  * **Anomaly detection** : If you can track known prints,then you can build a large Database over a period of time. After that you can send unseen prints into a "Triage state" where a human can look into it. 

In both analysis paths,we think TLS Prints is a valuable piece of intel, especially given we are moving to pervasive TLS. 

Lets look at what you can do with TrisulNSM and the new TLS Prints App.


===== How the TrisulNSM App works  =====

The TLS Print app is written in LuaJIT and plugs into the TrisulNSM Scripting Engine. The source code for the App is available on the [[https://github.com/trisulnsm/apps/tree/master/analyzers/tls-print|Github trisulnsm/apps repo]]. Essentially it uses the [[https://www.trisul.org/docs/lua/reassembly.html|"TCP Reassembly Handler"]] Lua script type and parses the Client Hello messages and constructs the JA3 fingerprints and pushes them back into the Trisul streaming pipeline. 

The app generates the following pieces of info. 

  - **Metrics** :  The app generates metrics for each TLS-Print it finds. If the print is a known one, it also updates the label. You can do long term trend analysis to see when each print was seen over the past few months.
  - **Graph Analytics** : When a Print is seen , the edge vertices namely the IP Flow Tuples, and the SNI (Server Name Indication) Extension are added.
  - **Alerts** : Right now we dont have many Malware prints, but when do we have them, the App can generate an alert. 


 
==== Metrics ====
 
One of the first things you want to check if what kind of coverage does the TLSPrint database have over your network traffic. Go to //Retro > Counters > Select the JA3 Print counter group// for a view of the top prints shown below.

{{ :app:tlsprint1.png?direct |}}


=== Trends ===

The above table shows the total number of hits of each TLSPrint over the selected timeframe, you can click on the //Trends// tab to get the timeseries distribution 

{{ :app:fp2.png?direct&600 |}}




==== Graph Analytics ====

With Trisul EDGE, you can now use streaming graph analytics to explore relationships. For example if you want to see if you can resolve an unknown TLSPrint, click on the small tag to the right of the print and // select "View Edges" //

This brings up a graph with the selected TLS Print as the root node. See below

{{ :app:fp3.png?direct |}}


You can find all the IPs that are connecting using this TLS-Print. You can then click on any of the IPs and try to reveal // its // adjacent vertices. You then get the following graph.

{{ :app:fp4.png?direct |}}
 

When you reveal adjacent vertices in [[https://www.trisul.org/docs/ug/edges/index.html|Trisul EDGE]] you will see vertices of all types. One of vertex types is **User-Agent** others are TLS Cert, Country, ASN etc.  We are lucky that we found a single User Agent around the same time interval as Semrush - which happens to be a SEO Bot. So there .. this **TLS Print can be pegged to SEMRUSH Bot**.  You can then go to the Key Dashboard and set the label to //resolve the previously unknown TLS Print//

===== Programatically resolving TLS Prints =====

This App dumps all fingerprints along with the parameters used to compute them and the TCP Flow details in a log file.  In another article we will outline how we can programatically deduce the Client Fingerprint.


We invite you to try this app in your network and let us know how it works. It is free to run .
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