Information security is of utmost importance to any organization. With the increasing number of attacks on private data, understanding the risk involved with handling and maintaining it is relevant. Although there are various methods to determine the risk associated with a certain organization's data, there is also a need to speed up the process of computation of this risk. This paper discusses the usage of Artificial Neural Networks that bodes well for the non linear nature of the threat vectors that affect risk involved in setting up a distributed MOOC based software system. An optimization to the existing methods is proposed that makes use of the bio inspired, Cuckoo Search Algorithm. With the concept of Levy Flights and Random Walks, this algorithm produces a much faster rate of convergence in calculation of the importance to be given to each threat vector in assessing the security of the software system.
['Nimisha Sharath', 'Shivani Parikh', 'K. Chandrasekaran']
Information Risk Analysis in a Distributed MOOC Based Software System Using an Optimized Artificial Neural Network