Emulating decryption function with radare2

This is the first part of the three-part series about code Emulation for Reversing Malware :
Part 1 describes how to use radare2 function emulation along with an exercise of cracking password of function implemented using radare2 python scripting plugin r2pipe.
Part 2 describes how to use the feature to decode a configuration of a Mirai IOT botnet, by implementing the solution in radare python scripting capabilities.
Part 3 improves the script created in the previous by adding more features of searching for addresses of encrypted string and creating function signature to search for decryption function instead of using the hard-coded address of the function.

radare2 is reverse engineering tool that can be very useful to reverse engineer malware or any type of binary as it supports many CPU architectures. One of the most striking features I found about radare is the partial code emulation. I was initially sceptical about this feature what could it be actually used for but think it about for a while and playing with that feature I realized its potential, it’s simply amazing.

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Reshaping Dataframe using Pivot and Melt in Apache Spark and pandas

Data cleaning is one of the most important and tedious part of data science workflow often mentioned but least discussed topic. Reflecting on my daily workflow, task of reshaping DataFrame is the very common operation I often do to get the data in desired format. Reshaping dataframe means transformation of the table structure, may be remove/adding of columns/rows or doing some aggregations on certains rows and produce a new column to summerize the aggregation result. In this post I won’t cover everything about reshaping, but I will discuss two most frequently used operations i.e. pivot and melt. The solutions I discuss are in spark to be more specific pyspark and I will give you brief solution for pandas but if you want detail explanation of pandas solution I would recommend you to read this post.

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Data cleaning in python using pandas

Data cleaning is a very important part of any data science project as data scientist spend 80% of their time is this step of the project. But not very much attentions is given to the cleaning process and not much research efforts are put to create any sort of framework recently I came across an amazing paper titled as Tidy data by Hadley Wickham in Journal of Statistical Software in which he talks about common problems one might encounter in data cleaning and what a Tidy data looks like I couldn’t agree more to him, he has also created a R package reshape and reshape2 for data cleaning, but the problem was the paper had very little to no code I also found the code version of the paper but it was in _R_, while most of my data cleaning work is done in pandas, I had to translate all those R solutions to pandas equivalent, so in this post the I will summarize all the main idea of the paper that the author suggests in the paper and also how we can solve it in pandas.

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Handling categorical features with python

As a data scientist, you may very frequently encounter categorical variable in your dataset like location, car model, gender, etc. You cannot directly use them in our machine learning algorithm as these algorithms only understand numbers. There are various techniques to convert these categorical features to numerical features but that is not the focus of this post, this post is about how to implement these techniques in python. I will talk a little bit about these techniques but won’t go into too much depth, I will emphasise more on various ways how you can implement this technique in python.

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Visual text Analytics with python

Due to the flourish of internet and accessibility of technology incredible platforms like social media, forums, etc have been created for knowledge sharing. Exchanging ideas is not confined to a geographical area. Due to this volume and variety of content is generated in the form of images, video, text, etc. The amount of information is so much that it’s unmanageable to perceive it in bounded time, in such times area of text analytics has got the attention of people in the field of linguistics, business, etc. The goal of the post is to summarize few of the visual text analytical techniques that could help you in your initial phase of text mining or help you create a new feature for creating machine learning model. I will describe few online and offline tools that you could use to help you get started. By offline tools, I mean using python based software packages to created visualization and text pre-processing. Online tools will be web-browser based applications to which just have to paste the text or upload the text file to visualise the results.

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