There has been a lot of discussion in this list regarding the need to assess 
and include the attacker’s ROI as a way to properly measure cyber attack risk. 
I had always strongly believed that this could be modeled mathematically by 
combining game theory and complex network theory, and that this would allow for 
a far more comprehensive approach than the industry’s subjective probability x 
impact assessments. 

We have written a book "Intentional Risk Management through Complex Networks 
Analysis (SpringerBriefs in Optimization) 
<http://www.amazon.com/gp/product/3319264214/ref=as_li_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=3319264214&linkCode=as2&tag=sm4rt-20&linkId=E7YMQRIJUA64GQKX>”
 with the results of several years of work trying to create a mathematical 
model for this. I was lucky to partner up with my good friend Dr. Santiago 
Moral and a leading information security authority, as well as with two 
distinguished mathematicians, Dr. Regino Criado and Dr. Miguel Romance with 
whom we worked in developing a mathematical model around these concepts. This 
is still work in progress and I believe there is room for improvement and 
enhancement. This is precisely why we chose to share it with the world by 
publishing our findings. 

Our main intention was to produce something similar to a page-rank algorithm 
for calculating relative and absolute risk for every node in a network. This 
risk could be from an employee with authorized access (we called this static 
risk) or from a hacker that would be able to move through the network more 
freely (we called this dynamic risk). This methodology allows us to consider 
the attackers perceived risk/reward at each node and through each path. We were 
trying to model how an attacker would rationally assess each potential target. 
Even though for individual hackers there is still a lot of serendipity it 
averages out when you consider all potential attacks and this should allow us 
to determine risk for each node or path. 

I hope it proves useful,
Victor
-- 

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