JARM Internet scanning, coupled with other metadata and historical analysis, allows for the possibility of proactive IOC

Now, let’s get started with creating Tensors. We would create some for int, float and string here. Notice the specialised way of creating tensors using tf.Variable attribute. This means that we are creating a Tensor which would be a variable nature, so we can change/modify its values by performing specialised operations, just like we would do in conventional programming with variables. Note that while we would have used int, float and string to declare the corresponding variables for such data types in conventional programming, we will use tf.int16 (which means we are defining a 16 bit integer), tf.float32 (to define a 32 bit floating point value) and tf.string to do so in Tensorflow. Note that we could also use tf.float16, tf.int32 and so on, depending on the requirements for the value we want to store. If you have ever used C , you would be aware of things like int, short int, long long int, float, double etc. used to declare variables with smaller (or larger) number of bits, so we just do something similar here in Tensorflow.

To simplify the process, one could utilize a security vendor, like SecurityTrails or Shodan, and query their API for destination JARM enrichment. Security researchers and vendors are likely to be better suited to maintain historical analysis of TLS servers and can therefore provide greater levels of metadata to utilize in measuring a host’s risk score.

JARM fingerprints appear to also be unique to default configurations and patch levels for certain servers and appliances. Because of this, it may be possible to associate a JARM fingerprint with a specific version of Apache, for example. There has yet to be exhaustive research put into this, but here are some preliminary findings:

We are all familiar with data types in programming, right? 1, 2, 3 etc. are integers, numbers with decimal points (1.5,3.141,5.2555 etc.) are floating point values and another common one is strings (such as “Hello. How are you today?”). When we have a collection of multiple elements together, we generally term that as a list (e.g. [1,4,7]).

Tensorflow is a buzz word nowadays in this exciting world of Artificial Intelligence (AI), especially as Deep Learning continues to rapidly accelerate progress in AI. But for someone just starting with Tensorflow, the experience can be scary and daunting, as the terminologies and usage of the beautiful library can be confusing for complete beginners. When I first started learning Tensorflow, I faced similar challenges, and hope to simplify some of the intricacies through this article. This article requires a basic understanding of Python to get a clearer picture of Tensorflow.

A fleet of application servers that are all running the same TLS configuration should have the same JARM fingerprint. One could regularly scan the fleet with JARM to confirm that they are the same. If a server in the fleet produces a different JARM fingerprint than the rest, then it is not running the same configuration. One major financial institution is already planning to use this capability to identify servers that are not running their latest TLS standard.

This shows that of the 100 Tor nodes the user maintains, 100 of them have the same JARM fingerprint. We essentially just ran a configuration drift check on this user’s Tor node deployment and found that they indeed have a well-maintained fleet. However, if one host had a different JARM than the others, then it would mean it’s not running the same configuration and may warrant investigation. To simulate this, I’ll throw a random IP into the list and run it again:

JARM fingerprints appear to also be unique to default configurations and patch levels for certain servers and appliances. Because of this, it may be possible to associate a JARM fingerprint with a specific version of Apache, for example. There has yet to be exhaustive research put into this, but here are some preliminary findings:

The basic (and simplest) way to describe Tensorflow is that it is a cool library in Python (and probably other programming languages as well) which allows us to create computational graphs to develop neural network models. The basic element which comprises Tensorflow objects is a Tensor, and all computations which are performed occur in these Tensors. So literally (in my words), these Tensors flow in an orderly manner when you develop any neural network model, and give rise to the final outputs when evaluated. So yes, learning what tensors really are and how we can use them is the first step to getting started with Tensorflow.

Now, let’s come to Tensors- they are basically higher dimensional representations/generalisations of conventional matrices and vectors. It could even be a 1-D matrix, a 2-D matrix, a 4-D matrix, or an n-dimensional matrix! So we can have multi-dimensional arrays beyond a single list in a Tensor. That’s it- that is what a tensor really is in the most basic sense. Just like we can assign variables values in conventional programming, we could do the same in Tensorflow. Just that there is a specialised way to do it. Similar, just like you could perform multiple operations on conventional variables (or constants) in programming, we can do that with Tensors. We can slice tensors and select a portion of its elements, have various data types for tensors (integers, floating point, strings etc.) and much more.

The very first step is to install the beautiful library! pip is all you need here. Note that if you’re using Google Colab (which is very similar to Jupyter notebooks in Python), you don’t even need to install this library as it is already installed and available ready for use. Thank you, Google!

One could utilize JARM for detection and response by automatically scanning all destination hosts observed on their network for event enrichment, utilizing a summary table so as to not scan the same hosts multiple times in a given timeframe. They could then run queries of known-bad against the JARM list or utilize the list for correlation in response scenarios.

There is also a lot of potential for security researchers and vendors to utilize JARM with correlation and historical analysis to identify and track malicious servers. This data could then be used to build high fidelity proactive blocklists for easy consumption. But please note that extra care should be put into these blocklists to ensure minimal false positives.

Let’s import the tensorflow module using the golden keyword import! And yes, you could check what version of the library you’re using by simply printing its version attribute.

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