So, I thought of making the concept a little easier for fellow programmers, Data Scientists and Pythonistas. The way I i

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.

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.

We are creating an object a of class int holding the value 2 And and object b of class str holding the value “Hello!”. In a way, these two particular classes are provided to us by default when we use numbers or strings.

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.

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ll break from writing more technical and factual based articles to discuss three main points of the Data Science job that I enjoy. There are a lot of articles out there that include a lot of negativity around being a Data Scientist, as well as some alongside some pro points, but in this article, I want to focus purely on the pros that I have personally experienced. Maybe some of these pros or enjoyments will be something you experience as well. The main topics I will discuss are freedom of the role as a Data Scientist in your everyday work, impact on where you work (the business), and the growth you will be able to obtain as a Data Scientist. Keep reading to find out more about the enjoyments of being a Data Scientist.

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.

Apart from these a lot of us end up working with classes and objects without even realizing it. For example, you are actually using a class when you use any scikit Learn Model.

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.

In this series of posts named Python Shorts, I will explain some simple but very useful constructs provided by Python, some essential tips, and some use cases I come up with regularly in my Data Science work.

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 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!

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.

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]).