• Intro to Big Data

    A introduction/primer on Big Data:

    will be continuously updating~

     

    Read

  • Reverse Engineering

    Essay on Reverse Engineering:

    An Analysis of Modern Security Software Protection (circa 2010)

     

    Read

     

Introduction to Big Data

Nov 2

 

bigdata

With the evolution and growth of digital data, there’s been a trend of ‘Big Data’ – there’s no official definitions but there’s some industry rule-of-thumbs:

If you have to ask, you probably aren’t using it

But the non-whimsical and grounded definition is probably

data that is too complex/large to process with standard database management systems or traditional analytics.

There aren’t technical delineations amongst all big data – there are common characteristics:

  • Volume - the most obvious is size. Users are generating immense amounts of data with their software and hardware – from obvious things like Facebook content and Tweets, to subtle things like application settings, playlists in the cloud, exercise tracking.

    If you think about software written for a traditional company, it’s designed for access by hundred of thousands of employees at once – even then, the average case is significantly less than that (in the middle of the night, non-peak hours). While Google is an extreme example, the search engine processes billions of search queries daily.

Reverse Engineering – An Analysis of Modern Security Software Protection

May 30

 

I wrote this for my Extended Essay for my International Baccalaureate diploma back in 2010 – it’s similar to a research paper but not at the same level as Masters/PhDs.

It covers most modern (circa 2010) anti-reverse engineering schemas:

  • Code obsfucation
  • Anti-Dumps (stack, libraries)
  • Debugger detection
  • Kernel-level rewrites
  • Anti-Tracing (SoftICE and modern equivalents)
  • Virtual Machines (Themida CISC/RISC VM)